

import argparse
import os
import re

import packaging.version


A_	:			Any  	=      'examples/'
A_	:			Tuple  	=      {
    '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_	:			Optional[Any]  	=      {
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
A_	:			Dict  	=      'README.md'

def 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			Dict   ,				lowercase_:			Tuple )					->				Optional[int]:
	with open(lowerCAmelCase__   ,				"""r"""   ,				encoding="""utf-8"""   ,				newline="""\n""" ) as f:
		A__							:   Tuple					       =		f.read()
	A__							:   Optional[int]					       =		REPLACE_PATTERNS[pattern]
	A__							:   List[Any]					       =		replace.replace("""VERSION"""   ,				lowerCAmelCase__ )
	A__							:   Any					       =		re_pattern.sub(lowerCAmelCase__   ,				lowerCAmelCase__ )
	with open(lowerCAmelCase__   ,				"""w"""   ,				encoding="""utf-8"""   ,				newline="""\n""" ) as f:
		f.write(lowerCAmelCase__ )

def 						UpperCamelCase (lowercase_:			Tuple )					->				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 						UpperCamelCase (lowercase_:			Optional[int]   ,				lowercase_:			Dict=False )					->				Optional[int]:
	for pattern, fname in REPLACE_FILES.items():
		update_version_in_file(lowerCAmelCase__   ,				lowerCAmelCase__   ,				lowerCAmelCase__ )
	if not patch:
		update_version_in_examples(lowerCAmelCase__ )

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

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

	A__							:   Any					       =		start_index
	# Update the lines in the model list.
	while not lines[index].startswith(_end_prompt ):
		if lines[index].startswith("""1.""" ):
			A__							:   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 						UpperCamelCase ()					->				Optional[Any]:
	with open(REPLACE_FILES["""init"""]   ,				"""r""" ) as f:
		A__							:   Optional[int]					       =		f.read()
	A__							:   str					       =		REPLACE_PATTERNS['''init'''][0].search(lowerCAmelCase__ ).groups()[0]
	return packaging.version.parse(lowerCAmelCase__ )

def 						UpperCamelCase (lowercase_:			Union[str, Any]=False )					->				Optional[int]:
	A__							:   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:
		A__							:   Union[str, Any]					       =		default_version.base_version
	elif patch:
		A__							:   Tuple					       =		f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
	else:
		A__							:   List[str]					       =		f"""{default_version.major}.{default_version.minor + 1}.0"""

	# Now let's ask nicely if that's the right one.
	A__							:   List[str]					       =		input(f"""Which version are you releasing? [{default_version}]""" )
	if len(lowerCAmelCase__ ) == 0:
		A__							:   Tuple					       =		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 						UpperCamelCase ()					->				List[str]:
	A__							:   Dict					       =		get_version()
	A__							:   str					       =		f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
	A__							:   Any					       =		current_version.base_version

	# Check with the user we got that right.
	A__							:   Any					       =		input(f"""Which version are we developing now? [{dev_version}]""" )
	if len(lowerCAmelCase__ ) == 0:
		A__							:   Any					       =		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_	:			Tuple  	=      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_	:			Optional[int]  	=      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()



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	):
						A__							:   Any					       =		parent
						A__							:   Any					       =		13
						A__							:   Optional[Any]					       =		7
						A__							:   Union[str, Any]					       =		30
						A__							:   str					       =		self.seq_length + self.mem_len
						A__							:   Dict					       =		15
						A__							:   int					       =		True
						A__							:   Tuple					       =		True
						A__							:   Union[str, Any]					       =		99
						A__							:   Optional[Any]					       =		[10, 50, 80]
						A__							:   str					       =		32
						A__							:   Tuple					       =		32
						A__							:   Union[str, Any]					       =		4
						A__							:   Optional[Any]					       =		8
						A__							:   int					       =		128
						A__							:   List[Any]					       =		2
						A__							:   List[str]					       =		2
						A__							:   int					       =		None
						A__							:   List[str]					       =		1
						A__							:   Union[str, Any]					       =		0
						A__							:   List[str]					       =		3
						A__							:   int					       =		self.vocab_size - 1
						A__							:   Optional[Any]					       =		0.0_1






					def   __A      (			self      ):
						A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )
						A__							:   Tuple					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Optional[Any]					       =		None
						if self.use_labels:
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Any					       =		TransfoXLConfig(
						    vocab_size=self.vocab_size   ,	mem_len=self.mem_len   ,	clamp_len=self.clamp_len   ,	cutoffs=self.cutoffs   ,	d_model=self.hidden_size   ,	d_embed=self.d_embed   ,	n_head=self.num_attention_heads   ,	d_head=self.d_head   ,	d_inner=self.d_inner   ,	div_val=self.div_val   ,	n_layer=self.num_hidden_layers   ,	eos_token_id=self.eos_token_id   ,	pad_token_id=self.vocab_size - 1   ,	init_range=self.init_range   ,	num_labels=self.num_labels   ,	)

						return (config, input_ids_a, input_ids_a, lm_labels)






					def   __A      (			self      ):
						random.seed(self.seed      )
						tf.random.set_seed(self.seed      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Dict					       =		TFTransfoXLModel(A__      )

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						A__							:   List[str]					       =		{"""input_ids""": input_ids_a, """mems""": mems_a}

						A__       ,       A__							:   str					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Optional[int]					       =		TFTransfoXLLMHeadModel(A__      )

						A__       ,       A__							:   int					       =		model(A__      ).to_tuple()

						A__							:   int					       =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
						A__       ,       A__							:   Optional[Any]					       =		model(A__      ).to_tuple()

						A__       ,       A__							:   Union[str, Any]					       =		model([input_ids_a, mems_a]      ).to_tuple()

						A__							:   Any					       =		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Any					       =		TFTransfoXLForSequenceClassification(A__      )
						A__							:   Optional[Any]					       =		model(A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)      )






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		self.prepare_config_and_inputs()
						((A__)       ,       (A__)       ,       (A__)       ,       (A__))							:   List[Any]					       =		config_and_inputs
						A__							:   int					       =		{"""input_ids""": input_ids_a}
						return config, inputs_dict



@require_tf
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	(
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					UpperCAmelCase__:       Optional[Any]  							=	() if is_tf_available() else ()
					UpperCAmelCase__:       int  							=	(
					    {
					        '''feature-extraction''': TFTransfoXLModel,
					        '''text-classification''': TFTransfoXLForSequenceClassification,
					        '''text-generation''': TFTransfoXLLMHeadModel,
					        '''zero-shot''': TFTransfoXLForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Tuple  							=	False
					UpperCAmelCase__:       List[str]  							=	False






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
							# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
							# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
							# tokenizer.
							return True

						return False






					def   __A      (			self      ):
						A__							:   Tuple					       =		TFTransfoXLModelTester(self      )
						A__							:   List[Any]					       =		ConfigTester(self   ,	config_class=A__   ,	d_embed=37      )






					def   __A      (			self      ):
						self.config_tester.run_common_tests()






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_model(*A__      )






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   Any					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_lm_head(*A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   int					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Union[str, Any]					       =		[TFTransfoXLForSequenceClassification]

						for model_class in self.all_model_classes:
							A__							:   Any					       =		model_class(A__      )
							assert isinstance(model.get_input_embeddings()   ,	tf.keras.layers.Layer      )
							if model_class in list_other_models_with_output_ebd:
								A__							:   Optional[Any]					       =		model.get_output_embeddings()
								assert isinstance(A__   ,	tf.keras.layers.Layer      )
								A__							:   Tuple					       =		model.get_bias()
								assert name is None
							else:
								A__							:   Dict					       =		model.get_output_embeddings()
								assert x is None
								A__							:   int					       =		model.get_bias()
								assert name is None






					def   __A      (			self      ):
						# TODO JP: Make TransfoXL XLA compliant
						pass






					@slow
					def   __A      (			self      ):
						for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   List[Any]					       =		TFTransfoXLModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )






					@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""      )
					def   __A      (			self      ):
						pass



@require_tf
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@unittest.skip("""Skip test until #12651 is resolved."""      )
					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""      )
						# fmt: off
						A__							:   Tuple					       =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,	dtype=tf.intaa      )  # noqa: E231
						# fmt: on
						#  In 1991 , the remains of Russian Tsar Nicholas II and his family
						#  ( except for Alexei and Maria ) are discovered .
						#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
						#  remainder of the story . 1883 Western Siberia ,
						#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
						#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
						#  father initially slaps him for making such an accusation , Rasputin watches as the
						#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
						#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
						#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

						# fmt: off
						A__							:   Dict					       =		[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
						# fmt: on
						#  In 1991, the remains of Russian Tsar Nicholas II and his family (
						#  except for Alexei and Maria ) are discovered. The voice of young son,
						#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
						#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
						#  and a group of men to perform magic. Rasputin has a vision and
						#  denounces one of the men as a horse thief. Although his father initially
						#  slaps him for making such an accusation, Rasputin watches as the man
						#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
						#  of the Virgin Mary, prompting him to become a priest.
						#  Rasputin quickly becomes famous, with people, even a bishop, begging for
						#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
						# Nicholas II and his family were discovered. The voice of <unk> young son,
						# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

						A__							:   Any					       =		model.generate(A__   ,	max_length=200   ,	do_sample=A__      )
						self.assertListEqual(output_ids[0].numpy().tolist()   ,	A__      )



