infinityofspace/python_codestyle_validator-dt-random_500
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'''simple docstring'''
def a_ ( _UpperCAmelCase : Optional[Any] ) -> List[str]:
__snake_case : Dict = []
__snake_case : Any = set({'(', '[', '{'} )
__snake_case : List[Any] = set({')', ']', '}'} )
__snake_case : List[Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(_UpperCAmelCase ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_UpperCAmelCase ) == 0 or (len(_UpperCAmelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_UpperCAmelCase ) == 0
def a_ ( ) -> Optional[int]:
__snake_case : Any = input('Enter sequence of brackets: ' )
if is_balanced(_UpperCAmelCase ):
print(_UpperCAmelCase ,'is balanced' )
else:
print(_UpperCAmelCase ,'is not balanced' )
if __name__ == "__main__":
main()
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 1 |
'''simple docstring'''
A__ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__ : Tuple = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> str:
assert len(str(_UpperCAmelCase ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
__snake_case : str = year // 1_00
__snake_case : Tuple = (5 * (century % 4) + 2) % 7
__snake_case : Any = year % 1_00
__snake_case : Optional[int] = centurian % 12
__snake_case : List[Any] = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
__snake_case : Optional[int] = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
__snake_case : Dict = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 1 |
'''simple docstring'''
from torch import nn
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function: {act_fn}''' )
| 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ : Tuple = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[Any] = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
A__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_0_0, 0.25) = }""")
print(F"""{price_plus_tax(1_25.50, 0.05) = }""")
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
__snake_case : Tuple = [True] * (num + 1)
__snake_case : Tuple = 2
while p * p <= num:
if primes[p]:
for i in range(p * p ,num + 1 ,_UpperCAmelCase ):
__snake_case : str = False
p += 1
return [prime for prime in range(2 ,num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ : Any = int(input('''Enter a positive integer: ''').strip())
print(prime_sieve_eratosthenes(user_num))
| 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def a_ ( _UpperCAmelCase : str = "laptop" ) -> DataFrame:
__snake_case : List[Any] = f'''https://www.amazon.in/laptop/s?k={product}'''
__snake_case : Optional[Any] = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
__snake_case : List[str] = BeautifulSoup(requests.get(_UpperCAmelCase ,headers=_UpperCAmelCase ).text )
# Initialize a Pandas dataframe with the column titles
__snake_case : Optional[int] = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' ,attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} ,) ,soup.find_all('div' ,attrs={'class': 'a-row a-size-base a-color-base'} ) ,):
try:
__snake_case : List[Any] = item.ha.text
__snake_case : Optional[int] = 'https://www.amazon.in/' + item.ha.a['href']
__snake_case : Optional[Any] = item.find('span' ,attrs={'class': 'a-offscreen'} ).text
try:
__snake_case : Any = item.find('span' ,attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
__snake_case : Optional[Any] = 'Not available'
try:
__snake_case : Any = (
'₹'
+ item.find(
'span' ,attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
__snake_case : Optional[Any] = ''
try:
__snake_case : List[str] = float(
(
(
float(product_mrp.strip('₹' ).replace(',' ,'' ) )
- float(product_price.strip('₹' ).replace(',' ,'' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' ,'' ) )
)
* 1_00 )
except ValueError:
__snake_case : Optional[Any] = float('nan' )
except AttributeError:
pass
__snake_case : Tuple = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__snake_case : Any = ' '
__snake_case : Tuple = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
A__ : List[Any] = '''headphones'''
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(__a , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = BasicTokenizer(do_lower_case=__a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : Any = WordpieceTokenizer(vocab=__a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : str ,_UpperCAmelCase : int ,_UpperCAmelCase : Tuple ) -> int:
__snake_case : int = s.rsplit(_UpperCAmelCase ,_UpperCAmelCase )
return new.join(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def a_ ( _UpperCAmelCase : Tuple ) -> int:
__snake_case : Union[str, Any] = {}
__snake_case : str = ['group_1', 'group_2', 'group_3', 'group_4']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
__snake_case : Tuple = key.replace(f'''{group_key}.''' ,f'''{group_key}.group.''' )
if "res_path" in key:
__snake_case : Optional[int] = key.replace('res_path.' ,'res_path.path.' )
if key.endswith('.w' ):
__snake_case : Dict = rreplace(_UpperCAmelCase ,'.w' ,'.weight' ,1 )
if key.endswith('.b' ):
__snake_case : List[Any] = rreplace(_UpperCAmelCase ,'.b' ,'.bias' ,1 )
__snake_case : List[str] = value.float()
return upgrade
@torch.no_grad()
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[int]=None ,_UpperCAmelCase : List[str]=True ) -> List[Any]:
from dall_e import Encoder
__snake_case : List[str] = Encoder()
if os.path.exists(_UpperCAmelCase ):
__snake_case : str = torch.load(_UpperCAmelCase )
else:
__snake_case : Any = torch.hub.load_state_dict_from_url(_UpperCAmelCase )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = ckpt.state_dict()
encoder.load_state_dict(_UpperCAmelCase )
if config_path is not None:
__snake_case : Union[str, Any] = FlavaImageCodebookConfig.from_pretrained(_UpperCAmelCase )
else:
__snake_case : Tuple = FlavaImageCodebookConfig()
__snake_case : str = FlavaImageCodebook(_UpperCAmelCase ).eval()
__snake_case : int = encoder.state_dict()
__snake_case : Dict = upgrade_state_dict(_UpperCAmelCase )
hf_model.load_state_dict(_UpperCAmelCase )
__snake_case : Optional[Any] = hf_model.state_dict()
__snake_case : Union[str, Any] = count_parameters(_UpperCAmelCase )
__snake_case : List[str] = count_parameters(_UpperCAmelCase )
assert torch.allclose(_UpperCAmelCase ,_UpperCAmelCase ,atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(_UpperCAmelCase )
else:
return hf_state_dict
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
A__ : Union[str, Any] = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
A__ : Dict = numpy.array([0, 0])
A__ : Tuple = numpy.array([0.5, 0.8_66_02_54])
A__ : Any = numpy.array([1, 0])
A__ : Dict = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def a_ ( _UpperCAmelCase : list[numpy.ndarray] ,_UpperCAmelCase : int ) -> list[numpy.ndarray]:
__snake_case : Tuple = initial_vectors
for _ in range(_UpperCAmelCase ):
__snake_case : Tuple = iteration_step(_UpperCAmelCase )
return vectors
def a_ ( _UpperCAmelCase : list[numpy.ndarray] ) -> list[numpy.ndarray]:
__snake_case : List[Any] = []
for i, start_vector in enumerate(vectors[:-1] ):
__snake_case : Union[str, Any] = vectors[i + 1]
new_vectors.append(_UpperCAmelCase )
__snake_case : Union[str, Any] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def a_ ( _UpperCAmelCase : numpy.ndarray ,_UpperCAmelCase : float ) -> numpy.ndarray:
__snake_case : int = numpy.radians(_UpperCAmelCase )
__snake_case , __snake_case : Tuple = numpy.cos(_UpperCAmelCase ), numpy.sin(_UpperCAmelCase )
__snake_case : Union[str, Any] = numpy.array(((c, -s), (s, c)) )
return numpy.dot(_UpperCAmelCase ,_UpperCAmelCase )
def a_ ( _UpperCAmelCase : list[numpy.ndarray] ) -> None:
__snake_case : List[str] = plt.gca()
axes.set_aspect('equal' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
__snake_case , __snake_case : List[Any] = zip(*_UpperCAmelCase )
plt.plot(_UpperCAmelCase ,_UpperCAmelCase )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ : Optional[Any] = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A__ : Dict = logging.get_logger(__name__)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Tuple , *__a : List[Any] , **__a : Any ) -> None:
'''simple docstring'''
warnings.warn(
'The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use OwlViTImageProcessor instead.' , __a , )
super().__init__(*__a , **__a )
| 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class snake_case__ :
# setable values
A__ = None
A__ = None
A__ = None # sigma(t_i)
@classmethod
def A_ ( cls : Optional[int] ) -> List[str]:
'''simple docstring'''
return cls()
@dataclass
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = 42
A__ = 42
A__ = 42
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return True
@register_to_config
def __init__( self : Any , __a : float = 0.0_2 , __a : float = 100 , __a : float = 1.0_0_7 , __a : float = 80 , __a : float = 0.0_5 , __a : float = 50 , ) -> Dict:
'''simple docstring'''
pass
def A_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return KarrasVeSchedulerState.create()
def A_ ( self : Dict , __a : KarrasVeSchedulerState , __a : int , __a : Tuple = () ) -> KarrasVeSchedulerState:
'''simple docstring'''
__snake_case : Dict = jnp.arange(0 , __a )[::-1].copy()
__snake_case : List[Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__a , schedule=jnp.array(__a , dtype=jnp.floataa ) , timesteps=__a , )
def A_ ( self : List[Any] , __a : KarrasVeSchedulerState , __a : jnp.ndarray , __a : float , __a : random.KeyArray , ) -> Tuple[jnp.ndarray, float]:
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
__snake_case : Union[str, Any] = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
__snake_case : Optional[Any] = 0
# sample eps ~ N(0, S_noise^2 * I)
__snake_case : Optional[int] = random.split(__a , num=1 )
__snake_case : List[str] = self.config.s_noise * random.normal(key=__a , shape=sample.shape )
__snake_case : Optional[Any] = sigma + gamma * sigma
__snake_case : Dict = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def A_ ( self : List[str] , __a : KarrasVeSchedulerState , __a : jnp.ndarray , __a : float , __a : float , __a : jnp.ndarray , __a : bool = True , ) -> Union[FlaxKarrasVeOutput, Tuple]:
'''simple docstring'''
__snake_case : Union[str, Any] = sample_hat + sigma_hat * model_output
__snake_case : str = (sample_hat - pred_original_sample) / sigma_hat
__snake_case : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__a , derivative=__a , state=__a )
def A_ ( self : Tuple , __a : KarrasVeSchedulerState , __a : jnp.ndarray , __a : float , __a : float , __a : jnp.ndarray , __a : jnp.ndarray , __a : jnp.ndarray , __a : bool = True , ) -> Union[FlaxKarrasVeOutput, Tuple]:
'''simple docstring'''
__snake_case : Union[str, Any] = sample_prev + sigma_prev * model_output
__snake_case : Optional[int] = (sample_prev - pred_original_sample) / sigma_prev
__snake_case : Tuple = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__a , derivative=__a , state=__a )
def A_ ( self : Dict , __a : KarrasVeSchedulerState , __a : Any , __a : Dict , __a : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
raise NotImplementedError()
| 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
A__ : Optional[int] = '''bert-base-cased'''
A__ : Optional[Any] = '''google/pegasus-xsum'''
A__ : List[Any] = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
A__ : str = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
A__ : str = '''patrickvonplaten/t5-tiny-random'''
A__ : Union[str, Any] = '''sshleifer/bart-tiny-random'''
A__ : List[Any] = '''sshleifer/tiny-mbart'''
A__ : Dict = '''sshleifer/tiny-marian-en-de'''
def a_ ( _UpperCAmelCase : Path ,_UpperCAmelCase : list ) -> List[str]:
__snake_case : Tuple = '\n'.join(_UpperCAmelCase )
Path(_UpperCAmelCase ).open('w' ).writelines(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : str ) -> Any:
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(_UpperCAmelCase ,f'''{split}.source''' ) ,_UpperCAmelCase )
_dump_articles(os.path.join(_UpperCAmelCase ,f'''{split}.target''' ) ,_UpperCAmelCase )
return tmp_dir
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def A_ ( self : str , __a : Tuple ) -> str:
'''simple docstring'''
__snake_case : List[str] = AutoTokenizer.from_pretrained(__a )
__snake_case : int = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__snake_case : int = max(len(tokenizer.encode(__a ) ) for a in ARTICLES )
__snake_case : Dict = max(len(tokenizer.encode(__a ) ) for a in SUMMARIES )
__snake_case : Any = 4
__snake_case : Optional[Any] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__snake_case , __snake_case : Optional[int] = 'ro_RO', 'de_DE' # ignored for all but mbart, but never causes error.
__snake_case : Any = SeqaSeqDataset(
__a , data_dir=__a , type_path='train' , max_source_length=__a , max_target_length=__a , src_lang=__a , tgt_lang=__a , )
__snake_case : Union[str, Any] = DataLoader(__a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(__a , __a )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__snake_case : Tuple = shift_tokens_right(batch['labels'] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def A_ ( self : Optional[Any] , __a : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : int = AutoTokenizer.from_pretrained(__a )
__snake_case : Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__snake_case : str = max(len(tokenizer.encode(__a ) ) for a in ARTICLES )
__snake_case : Union[str, Any] = max(len(tokenizer.encode(__a ) ) for a in SUMMARIES )
__snake_case : Any = 4
__snake_case : Dict = LegacySeqaSeqDataset(
__a , data_dir=__a , type_path='train' , max_source_length=20 , max_target_length=__a , )
__snake_case : str = DataLoader(__a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def A_ ( self : List[Any] ) -> str:
'''simple docstring'''
__snake_case : str = AutoTokenizer.from_pretrained('facebook/mbart-large-cc25' )
__snake_case : Dict = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__snake_case : Any = tmp_dir.joinpath('train.source' ).open().readlines()
__snake_case : List[str] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(__a , __a , 128 , __a )
__snake_case : int = {x.name for x in tmp_dir.iterdir()}
__snake_case : int = {x.name for x in save_dir.iterdir()}
__snake_case : Dict = save_dir.joinpath('train.source' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(__a ) < len(__a )
assert len(__a ) == 1
assert len(packed_examples[0] ) == sum(len(__a ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='This test requires fairseq' )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
__snake_case , __snake_case , __snake_case : str = self._get_dataset(max_len=64 )
__snake_case : Union[str, Any] = 64
__snake_case : Union[str, Any] = ds.make_dynamic_sampler(__a , required_batch_size_multiple=__a )
__snake_case : Any = [len(__a ) for x in batch_sampler]
assert len(set(__a ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(__a ) == len(__a ) # no dropped or added examples
__snake_case : Union[str, Any] = DataLoader(__a , batch_sampler=__a , collate_fn=ds.collate_fn , num_workers=2 )
__snake_case : Optional[Any] = []
__snake_case : Dict = []
for batch in data_loader:
__snake_case : int = batch['input_ids'].shape
__snake_case : List[Any] = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__snake_case : Tuple = np.product(batch['input_ids'].shape )
num_src_per_batch.append(__a )
if num_src_tokens > (max_tokens * 1.1):
failures.append(__a )
assert num_src_per_batch[0] == max(__a )
if failures:
raise AssertionError(f'''too many tokens in {len(__a )} batches''' )
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
__snake_case , __snake_case , __snake_case : Union[str, Any] = self._get_dataset(max_len=512 )
__snake_case : Any = 2
__snake_case : Dict = ds.make_sortish_sampler(__a , shuffle=__a )
__snake_case : List[Any] = DataLoader(__a , batch_size=__a , collate_fn=ds.collate_fn , num_workers=2 )
__snake_case : int = DataLoader(__a , batch_size=__a , collate_fn=ds.collate_fn , num_workers=2 , sampler=__a )
__snake_case : Any = tokenizer.pad_token_id
def count_pad_tokens(__a : Tuple , __a : Optional[int]="input_ids" ):
return [batch[k].eq(__a ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(__a , k='labels' ) ) < sum(count_pad_tokens(__a , k='labels' ) )
assert sum(count_pad_tokens(__a ) ) < sum(count_pad_tokens(__a ) )
assert len(__a ) == len(__a )
def A_ ( self : Optional[Any] , __a : int=1000 , __a : int=128 ) -> str:
'''simple docstring'''
if os.getenv('USE_REAL_DATA' , __a ):
__snake_case : str = 'examples/seq2seq/wmt_en_ro'
__snake_case : Union[str, Any] = max_len * 2 * 64
if not Path(__a ).joinpath('train.len' ).exists():
save_len_file(__a , __a )
else:
__snake_case : List[Any] = 'examples/seq2seq/test_data/wmt_en_ro'
__snake_case : List[Any] = max_len * 4
save_len_file(__a , __a )
__snake_case : Optional[Any] = AutoTokenizer.from_pretrained(__a )
__snake_case : Optional[Any] = SeqaSeqDataset(
__a , data_dir=__a , type_path='train' , max_source_length=__a , max_target_length=__a , n_obs=__a , )
return ds, max_tokens, tokenizer
def A_ ( self : Any ) -> Dict:
'''simple docstring'''
__snake_case , __snake_case , __snake_case : List[str] = self._get_dataset()
__snake_case : Dict = set(DistributedSortishSampler(__a , 256 , num_replicas=2 , rank=0 , add_extra_examples=__a ) )
__snake_case : str = set(DistributedSortishSampler(__a , 256 , num_replicas=2 , rank=1 , add_extra_examples=__a ) )
assert idsa.intersection(__a ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def A_ ( self : int , __a : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = AutoTokenizer.from_pretrained(__a , use_fast=__a )
if tok_name == MBART_TINY:
__snake_case : Union[str, Any] = SeqaSeqDataset(
__a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='train' , max_source_length=4 , max_target_length=8 , src_lang='EN' , tgt_lang='FR' , )
__snake_case : List[Any] = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__snake_case : Optional[Any] = SeqaSeqDataset(
__a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='train' , max_source_length=4 , max_target_length=8 , )
__snake_case : Dict = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(__a ) == 1 if tok_name == BART_TINY else len(__a ) == 0
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Any = {
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegatronBertForCausalLM''',
'''MegatronBertForMaskedLM''',
'''MegatronBertForMultipleChoice''',
'''MegatronBertForNextSentencePrediction''',
'''MegatronBertForPreTraining''',
'''MegatronBertForQuestionAnswering''',
'''MegatronBertForSequenceClassification''',
'''MegatronBertForTokenClassification''',
'''MegatronBertModel''',
'''MegatronBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
A__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError('Parameter number must be int' )
if number < 0:
raise ValueError('Parameter number must be greater than or equal to 0' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError('Parameters chain_length and number_limit must be int' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'Parameters chain_length and number_limit must be greater than 0' )
# the counter for the chains with the exact desired length
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : Optional[Any] = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{solution()}""")
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A__ : List[str] = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
'''
A__ : Optional[int] = '''\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
'''
A__ : Dict = R'''
Calculates accuracy after canonicalizing inputs.
Args:
predictions: list of predictions to score. Each prediction
is a string that contains natural language and LaTex.
references: list of reference for each prediction. Each
reference is a string that contains natural language
and LaTex.
Returns:
accuracy: accuracy after canonicalizing inputs
(e.g., converting "1/2" to "\\frac{1}{2}")
Examples:
>>> metric = datasets.load_metric("competition_math")
>>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
>>> print(results)
{\'accuracy\': 1.0}
'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ ( datasets.Metric ):
def A_ ( self : str ) -> Tuple:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/hendrycks/math' , codebase_urls=['https://github.com/hendrycks/math'] , )
def A_ ( self : str , __a : List[str] , __a : Optional[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = 0.0
for i, j in zip(__a , __a ):
n_correct += 1.0 if math_equivalence.is_equiv(__a , __a ) else 0.0
__snake_case : Optional[Any] = n_correct / len(__a )
return {
"accuracy": accuracy,
}
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a_ ( _UpperCAmelCase : Namespace ) -> Union[str, Any]:
return ConvertCommand(
args.model_type ,args.tf_checkpoint ,args.pytorch_dump_output ,args.config ,args.finetuning_task_name )
A__ : Optional[int] = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@staticmethod
def A_ ( __a : ArgumentParser ) -> str:
'''simple docstring'''
__snake_case : List[str] = parser.add_parser(
'convert' , help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.' , )
train_parser.add_argument('--model_type' , type=__a , required=__a , help='Model\'s type.' )
train_parser.add_argument(
'--tf_checkpoint' , type=__a , required=__a , help='TensorFlow checkpoint path or folder.' )
train_parser.add_argument(
'--pytorch_dump_output' , type=__a , required=__a , help='Path to the PyTorch saved model output.' )
train_parser.add_argument('--config' , type=__a , default='' , help='Configuration file path or folder.' )
train_parser.add_argument(
'--finetuning_task_name' , type=__a , default=__a , help='Optional fine-tuning task name if the TF model was a finetuned model.' , )
train_parser.set_defaults(func=__a )
def __init__( self : Tuple , __a : str , __a : str , __a : str , __a : str , __a : str , *__a : int , ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = logging.get_logger('transformers-cli/converting' )
self._logger.info(f'''Loading model {model_type}''' )
__snake_case : Dict = model_type
__snake_case : Optional[Any] = tf_checkpoint
__snake_case : str = pytorch_dump_output
__snake_case : Dict = config
__snake_case : str = finetuning_task_name
def A_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(__a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__a )
if "ckpt" in self._tf_checkpoint.lower():
__snake_case : Optional[int] = self._tf_checkpoint
__snake_case : List[str] = ''
else:
__snake_case : int = self._tf_checkpoint
__snake_case : Tuple = ''
convert_transfo_xl_checkpoint_to_pytorch(
__a , self._config , self._pytorch_dump_output , __a )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__a )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__a )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )
| 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ : List[str] = logging.get_logger(__name__)
A__ : Dict = {
'''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = '''convnextv2'''
def __init__( self : Union[str, Any] , __a : Tuple=3 , __a : str=4 , __a : Any=4 , __a : Optional[Any]=None , __a : int=None , __a : int="gelu" , __a : Optional[int]=0.0_2 , __a : List[Any]=1e-12 , __a : Tuple=0.0 , __a : str=224 , __a : Any=None , __a : List[str]=None , **__a : List[str] , ) -> str:
'''simple docstring'''
super().__init__(**__a )
__snake_case : Dict = num_channels
__snake_case : List[Any] = patch_size
__snake_case : Dict = num_stages
__snake_case : Optional[Any] = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
__snake_case : Dict = [3, 3, 9, 3] if depths is None else depths
__snake_case : Union[str, Any] = hidden_act
__snake_case : List[Any] = initializer_range
__snake_case : Optional[Any] = layer_norm_eps
__snake_case : Optional[int] = drop_path_rate
__snake_case : str = image_size
__snake_case : str = ['stem'] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
__snake_case , __snake_case : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a , out_indices=__a , stage_names=self.stage_names )
| 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
End of preview. Expand
in Dataset Viewer.
Dataset Card for "python_codestyles-single-500"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains 500 completely
different code styles. The code styles differ in exactly one codestyle rule, which is called a single codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|---|---|
| TheAlgorithms/Python | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| huggingface/transformers | v4.31.0 |
| huggingface/datasets | 2.13.1 |
| huggingface/diffusers | v0.18.2 |
| huggingface/accelerate | v0.21.0 |
You can find the corresponding code styles of the examples in the file additional_data.json.
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns code_codestyle and style_context_codestyle in the dataset.
There are 182.184 samples in total and 91.084 positive and 91.100 negative samples.
- Downloads last month
- 48
Size of downloaded dataset files:
320 MB
Size of the auto-converted Parquet files:
320 MB
Number of rows:
182,184
Models trained or fine-tuned on infinityofspace/python_codestyles-single-500