



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase_							:      List[Any]      =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
UpperCAmelCase_							:      List[str]      =  "    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self		):
							A__							=       tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir			,	"models/bert/"		)		)
							A__							=       self.transformer_dir
							shutil.copy(
							    os.path.join(UpperCAmelCase__			,	"src/transformers/models/bert/modeling_bert.py"		)			,	os.path.join(self.transformer_dir			,	"models/bert/modeling_bert.py"		)			,	)



	def 			__A							(       self		):
							A__							=       "src/transformers"
							shutil.rmtree(self.transformer_dir		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=None		):
							A__							=       comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
													A__							=       comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							A__							=       black.Mode(target_versions={black.TargetVersion.PYaa}			,	line_length=119		)
							A__							=       black.format_str(UpperCAmelCase__			,	mode=UpperCAmelCase__		)
							A__							=       os.path.join(self.transformer_dir			,	"new_code.py"		)
							with open(UpperCAmelCase__			,	"w"			,	newline="\n"		) as f:
													f.write(UpperCAmelCase__		)
							if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase__		)		) == 0		)
							else:
													check_copies.is_copy_consistent(f.name			,	overwrite=UpperCAmelCase__		)
													with open(UpperCAmelCase__			,	"r"		) as f:
																			self.assertTrue(f.read()			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"		)
							self.assertEqual(UpperCAmelCase__			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							# Base copy consistency
							self.check_copy_consistency(
							    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"			,	"BertLMPredictionHead"			,	REFERENCE_CODE + "\n"			,	)

							# With no empty line at the end
							self.check_copy_consistency(
							    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"			,	"BertLMPredictionHead"			,	UpperCAmelCase__			,	)

							# Copy consistency with rename
							self.check_copy_consistency(
							    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"			,	"TestModelLMPredictionHead"			,	re.sub("Bert"			,	"TestModel"			,	UpperCAmelCase__		)			,	)

							# Copy consistency with a really long name
							A__							=       "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
							self.check_copy_consistency(
							    F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""			,	F"""{long_class_name}LMPredictionHead"""			,	re.sub("Bert"			,	UpperCAmelCase__			,	UpperCAmelCase__		)			,	)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"			,	"TestModelLMPredictionHead"			,	UpperCAmelCase__			,	overwrite_result=re.sub("Bert"			,	"TestModel"			,	UpperCAmelCase__		)			,	)



	def 			__A							(       self		):
							A__							=       check_copies.LOCALIZED_READMES["README_zh-hans.md"]

							A__							=       (
							    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
							    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
							    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
							    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
							    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
							    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
							    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
							    " method has been applied to compress GPT2 into"
							    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
							    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
							    " Multilingual BERT into"
							    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
							    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
							    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
							    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
							    " Luong, Quoc V. Le, Christopher D. Manning."
							)
							A__							=       (
							    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
							    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
							    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
							    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
							)
							A__							=       (
							    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
							    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
							    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
							    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
							    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
							    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
							    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
							    " method has been applied to compress GPT2 into"
							    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
							    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
							    " Multilingual BERT into"
							    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
							    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
							    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
							    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
							    " Christopher D. Manning 发布。\n"
							)

							A__						,       A__							=       check_copies.convert_to_localized_md(
							    UpperCAmelCase__			,	UpperCAmelCase__			,	localized_readme["format_model_list"]		)

							self.assertFalse(UpperCAmelCase__		)
							self.assertEqual(UpperCAmelCase__			,	UpperCAmelCase__		)

							A__						,       A__							=       check_copies.convert_to_localized_md(
							    UpperCAmelCase__			,	UpperCAmelCase__			,	localized_readme["format_model_list"]		)

							# Check whether the number of models is equal to README.md after conversion.
							self.assertTrue(UpperCAmelCase__		)

							A__							=       (
							    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
							    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
							    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
							    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
							)
							A__							=       (
							    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
							    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
							    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
							    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
							)
							A__							=       (
							    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
							    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
							    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
							    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
							)

							A__						,       A__							=       check_copies.convert_to_localized_md(
							    UpperCAmelCase__			,	UpperCAmelCase__			,	localized_readme["format_model_list"]		)

							# Check if the model link is synchronized.
							self.assertEqual(UpperCAmelCase__			,	UpperCAmelCase__		)










from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCAmelCase_							:      Tuple      =  200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCAmelCase_							:      Any      =  50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCAmelCase_							:      Union[str, Any]      =  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       tuple[str, float]:





						"""simple docstring"""





						A__							=       len([g for position, g in enumerate(_A    ) if g == main_target[position]]    )
						return (item, float(_A    ))
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       tuple[str, str]:





						"""simple docstring"""





						A__							=       random.randint(0			,							len(_A    ) - 1    )
						A__							=       parent_a[:random_slice] + parent_a[random_slice:]
						A__							=       parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)
def  UpperCamelCase						(       _A			:				str			,							_A			:				list[str]    )->       str:





						"""simple docstring"""





						A__							=       list(_A    )
						if random.uniform(0			,							1    ) < MUTATION_PROBABILITY:
												A__							=       random.choice(_A    )
						return "".join(_A    )
def  UpperCamelCase						(       _A			:				tuple[str, float]			,							_A			:				list[tuple[str, float]]			,							_A			:				list[str]			,							)->       list[str]:





						"""simple docstring"""





						A__							=       []
						# Generate more children proportionally to the fitness score.
						A__							=       int(parent_a[1] * 100    ) + 1
						A__							=       10 if child_n >= 10 else child_n
						for _ in range(_A    ):
												A__							=       population_score[random.randint(0			,							_A    )][0]

												A__						,       A__							=       crossover(parent_a[0]			,							_A    )
												# Append new string to the population list.
												pop.append(mutate(_A			,							_A    )    )
												pop.append(mutate(_A			,							_A    )    )
						return pop
def  UpperCamelCase						(       _A			:				str			,							_A			:				list[str]			,							_A			:				bool = True    )->       tuple[int, int, str]:





						"""simple docstring"""





						if N_POPULATION < N_SELECTED:
												A__							=       f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
												raise ValueError(_A    )
						# Verify that the target contains no genes besides the ones inside genes variable.
						A__							=       sorted({c for c in target if c not in genes}    )
						if not_in_genes_list:
												A__							=       f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
												raise ValueError(_A    )

						# Generate random starting population.
						A__							=       []
						for _ in range(_A    ):
												population.append("".join([random.choice(_A    ) for i in range(len(_A    )    )]    )    )

						# Just some logs to know what the algorithms is doing.
						A__						,       A__							=       0, 0

						# This loop will end when we find a perfect match for our target.
						while True:
												generation += 1
												total_population += len(_A    )

												# Random population created. Now it's time to evaluate.

												# Adding a bit of concurrency can make everything faster,
												#
												# import concurrent.futures
												# population_score: list[tuple[str, float]] = []
												# with concurrent.futures.ThreadPoolExecutor(
												#                                   max_workers=NUM_WORKERS) as executor:
												#     futures = {executor.submit(evaluate, item) for item in population}
												#     concurrent.futures.wait(futures)
												#     population_score = [item.result() for item in futures]
												#
												# but with a simple algorithm like this, it will probably be slower.
												# We just need to call evaluate for every item inside the population.
												A__							=       [evaluate(_A			,							_A    ) for item in population]

												# Check if there is a matching evolution.
												A__							=       sorted(_A			,							key=lambda _A    : x[1]			,							reverse=_A    )
												if population_score[0][0] == target:
																		return (generation, total_population, population_score[0][0])

												# Print the best result every 10 generation.
												# Just to know that the algorithm is working.
												if debug and generation % 10 == 0:
																		print(
																		    f"""\nGeneration: {generation}"""
																		    f"""\nTotal Population:{total_population}"""
																		    f"""\nBest score: {population_score[0][1]}"""
																		    f"""\nBest string: {population_score[0][0]}"""    )

												# Flush the old population, keeping some of the best evolutions.
												# Keeping this avoid regression of evolution.
												A__							=       population[: int(N_POPULATION / 3    )]
												population.clear()
												population.extend(_A    )
												# Normalize population score to be between 0 and 1.
												A__							=       [
												    (item, score / len(_A    )) for item, score in population_score
												]

												# This is selection
												for i in range(_A    ):
																		population.extend(select(population_score[int(_A    )]			,							_A			,							_A    )    )
																		# Check if the population has already reached the maximum value and if so,
																		# break the cycle.  If this check is disabled, the algorithm will take
																		# forever to compute large strings, but will also calculate small strings in
																		# a far fewer generations.
																		if len(_A    ) > N_POPULATION:
																								break


if __name__ == "__main__":
						UpperCAmelCase_							:      Any      =  (
						    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
						)
						UpperCAmelCase_							:      List[str]      =  list(
						    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
						    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
						)
						UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  basic(target_str, genes_list)
						print(
						    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
						)





