

import argparse
import os
import re

import packaging.version


a		="""examples/"""
a		={
    """examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
    """doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
a		={
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
a		="""README.md"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	with open(lowerCamelCase__     ,			'r'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		__lowerCamelCase    :    int 	=			f.read()
	__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			REPLACE_PATTERNS[pattern]
	__lowerCamelCase    :    List[Any] 	=			replace.replace('VERSION'     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			re_pattern.sub(lowerCamelCase__     ,			lowerCamelCase__      )
	with open(lowerCamelCase__     ,			'w'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		f.write(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	for folder, directories, fnames in os.walk(lowerCamelCase__      ):
		# Removing some of the folders with non-actively maintained examples from the walk
		if "research_projects" in directories:
			directories.remove('research_projects'      )
		if "legacy" in directories:
			directories.remove('legacy'      )
		for fname in fnames:
			if fname.endswith('.py'      ):
				update_version_in_file(os.path.join(lowerCamelCase__     ,			lowerCamelCase__      )     ,			lowerCamelCase__     ,			pattern='examples'      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					str:
	for pattern, fname in REPLACE_FILES.items():
		update_version_in_file(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	if not patch:
		update_version_in_examples(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  )  ->					int:
	__lowerCamelCase    :    Dict 	=			'🤗 Transformers currently provides the following architectures'
	__lowerCamelCase    :    Union[str, Any] 	=			'1. Want to contribute a new model?'
	with open(lowerCamelCase__     ,			'r'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		__lowerCamelCase    :    str 	=			f.readlines()

	# Find the start of the list.
	__lowerCamelCase    :    Optional[Any] 	=			0
	while not lines[start_index].startswith(_start_prompt      ):
		start_index += 1
	start_index += 1

	__lowerCamelCase    :    Tuple 	=			start_index
	# Update the lines in the model list.
	while not lines[index].startswith(_end_prompt      ):
		if lines[index].startswith('1.'      ):
			__lowerCamelCase    :    List[str] 	=			lines[index].replace(
			    'https://huggingface.co/docs/transformers/main/model_doc'     ,			'https://huggingface.co/docs/transformers/model_doc'     ,			)
		index += 1

	with open(lowerCamelCase__     ,			'w'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		f.writelines(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	with open(REPLACE_FILES['init']     ,			'r'      ) as f:
		__lowerCamelCase    :    str 	=			f.read()
	__lowerCamelCase    :    str 	=			REPLACE_PATTERNS['init'][0].search(lowerCamelCase__      ).groups()[0]
	return packaging.version.parse(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=False      )  ->					Optional[int]:
	__lowerCamelCase    :    List[str] 	=			get_version()
	if patch and default_version.is_devrelease:
		raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'      )
	if default_version.is_devrelease:
		__lowerCamelCase    :    int 	=			default_version.base_version
	elif patch:
		__lowerCamelCase    :    str 	=			F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
	else:
		__lowerCamelCase    :    Optional[int] 	=			F"{default_version.major}.{default_version.minor + 1}.0"

	# Now let's ask nicely if that's the right one.
	__lowerCamelCase    :    Union[str, Any] 	=			input(F"Which version are you releasing? [{default_version}]"      )
	if len(lowerCamelCase__      ) == 0:
		__lowerCamelCase    :    str 	=			default_version

	print(F"Updating version to {version}."      )
	global_version_update(lowerCamelCase__     ,			patch=lowerCamelCase__      )
	if not patch:
		print('Cleaning main README, don\'t forget to run `make fix-copies`.'      )
		clean_main_ref_in_model_list()






def        SCREAMING_SNAKE_CASE__	(  )  ->					Optional[Any]:
	__lowerCamelCase    :    List[Any] 	=			get_version()
	__lowerCamelCase    :    int 	=			F"{current_version.major}.{current_version.minor + 1}.0.dev0"
	__lowerCamelCase    :    Optional[Any] 	=			current_version.base_version

	# Check with the user we got that right.
	__lowerCamelCase    :    Tuple 	=			input(F"Which version are we developing now? [{dev_version}]"      )
	if len(lowerCamelCase__      ) == 0:
		__lowerCamelCase    :    List[str] 	=			dev_version

	print(F"Updating version to {version}."      )
	global_version_update(lowerCamelCase__      )
	print('Cleaning main README, don\'t forget to run `make fix-copies`.'      )
	clean_main_ref_in_model_list()


if __name__ == "__main__":
					a		=argparse.ArgumentParser()
					parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
					parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
					a		=parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("""Nothing to do after a patch :-)""")
					else:
										post_release_work()







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


a		=["""gpt2"""]
a		="""gpt2"""

if is_tf_available():






					class 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

													__lowerCamelCase    :    List[Any] 	=			tf.cast(input_ids_dense > 0       ,tf.intaa)
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__lowerCamelCase    :    Optional[int] 	=			[
								    'This is a straightforward English test sentence.',
								    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
								    'Now we\'re going to add some Chinese: 一 二 三 一二三',
								    'And some much more rare Chinese: 齉 堃 齉堃',
								    'Je vais aussi écrire en français pour tester les accents',
								    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
								]
								__lowerCamelCase    :    List[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											__lowerCamelCase    :    List[str] 	=			python_outputs[key].numpy()
											__lowerCamelCase    :    Optional[Any] 	=			tf_outputs[key].numpy()

											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
											self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE__       ,tf.intaa) == tf_outputs_values))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Any 	=			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    str 	=			model.serving(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__lowerCamelCase    :    Optional[Any] 	=			Path(SCREAMING_SNAKE_CASE__) / 'saved.model'
										tf.saved_model.save(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,signatures={'serving_default': model.serving})
										__lowerCamelCase    :    Tuple 	=			tf.saved_model.load(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			loaded_model.signatures['serving_default'](SCREAMING_SNAKE_CASE__)['output_0']
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output))
							@slow
							def    lowerCAmelCase					(		self		:      int):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Optional[int] 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    Optional[Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			model_from_config(SCREAMING_SNAKE_CASE__)

									for key in from_config_output.keys():
										self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    List[Any] 	=			out['input_ids'].numpy().shape[1]

										assert out_length == max_length




