



"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A:     Optional[int]										=	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.
A:     List[str]										=	'''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''



class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   '''simple docstring'''





   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			tempfile.mkdtemp()
    os.makedirs(os.path.join(self.transformer_dir   ,  """models/bert/"""		)		)
    UpperCAmelCase			:      List[Any]			=			self.transformer_dir
    shutil.copy(
        os.path.join(_SCREAMING_SNAKE_CASE   ,  """src/transformers/models/bert/modeling_bert.py"""		)   ,  os.path.join(self.transformer_dir   ,  """models/bert/modeling_bert.py"""		)   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""src/transformers"""
    shutil.rmtree(self.transformer_dir		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   List[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""		)
    self.assertEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    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"""   ,  _SCREAMING_SNAKE_CASE   ,  )

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

    # Copy consistency with a really long name
    UpperCAmelCase			:      List[Any]			=			"""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"""   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  )

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




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

    UpperCAmelCase			:      Tuple			=			(
        """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."""
    )
    UpperCAmelCase			:      Optional[int]			=			(
        """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"""
    )
    UpperCAmelCase			:      Optional[int]			=			(
        """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"""
    )

    UpperCAmelCase			,	UpperCAmelCase			:      Any			=			check_copies.convert_to_localized_md(
        _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  localized_readme["""format_model_list"""]		)

    self.assertFalse(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			,	UpperCAmelCase			:      List[str]			=			check_copies.convert_to_localized_md(
        _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  localized_readme["""format_model_list"""]		)

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

    UpperCAmelCase			:      List[str]			=			(
        """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."""
    )
    UpperCAmelCase			:      int			=			(
        """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"""
    )
    UpperCAmelCase			:      str			=			(
        """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"""
    )

    UpperCAmelCase			,	UpperCAmelCase			:      Any			=			check_copies.convert_to_localized_md(
        _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  localized_readme["""format_model_list"""]		)

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





"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:     Union[str, Any]										=	2_0_0
# 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.
A:     int										=	5_0
# 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.
A:     Tuple										=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Tuple			=			len([g for position, g in enumerate(UpperCamelCase		) if g == main_target[position]]		)
 return (item, float(UpperCamelCase		))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			random.randint(0    ,	len(UpperCamelCase		) - 1		)
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]		):
 UpperCAmelCase			:      str			=			list(UpperCamelCase		)
 if random.uniform(0    ,	1		) < MUTATION_PROBABILITY:
  UpperCAmelCase			:      int			=			random.choice(UpperCamelCase		)
 return "".join(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	tuple[str, float]    ,	UpperCamelCase		:	list[tuple[str, float]]    ,	UpperCamelCase		:	list[str]    ,	):
 UpperCAmelCase			:      Optional[Any]			=			[]
 # Generate more children proportionally to the fitness score.
 UpperCAmelCase			:      Optional[Any]			=			int(parent_a[1] * 100		) + 1
 UpperCAmelCase			:      List[str]			=			10 if child_n >= 10 else child_n
 for _ in range(UpperCamelCase		):
  UpperCAmelCase			:      List[str]			=			population_score[random.randint(0    ,	UpperCamelCase		)][0]

  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			crossover(parent_a[0]    ,	UpperCamelCase		)
  # Append new string to the population list.
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
 return pop

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]    ,	UpperCamelCase		:	bool = True		):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  UpperCAmelCase			:      Dict			=			F"{N_POPULATION} must be bigger than {N_SELECTED}"
  raise ValueError(UpperCamelCase		)
 # Verify that the target contains no genes besides the ones inside genes variable.
 UpperCAmelCase			:      str			=			sorted({c for c in target if c not in genes}		)
 if not_in_genes_list:
  UpperCAmelCase			:      Optional[Any]			=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(UpperCamelCase		)

 # Generate random starting population.
 UpperCAmelCase			:      Optional[int]			=			[]
 for _ in range(UpperCamelCase		):
  population.append("""""".join([random.choice(UpperCamelCase		) for i in range(len(UpperCamelCase		)		)]		)		)

 # Just some logs to know what the algorithms is doing.
 UpperCAmelCase			,	UpperCAmelCase			:      Any			=			0, 0

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

  # 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.
  UpperCAmelCase			:      str			=			[evaluate(UpperCamelCase    ,	UpperCamelCase		) for item in population]

  # Check if there is a matching evolution.
  UpperCAmelCase			:      Union[str, Any]			=			sorted(UpperCamelCase    ,	key=lambda UpperCamelCase		: x[1]    ,	reverse=UpperCamelCase		)
  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.
  UpperCAmelCase			:      Tuple			=			population[: int(N_POPULATION / 3		)]
  population.clear()
  population.extend(UpperCamelCase		)
  # Normalize population score to be between 0 and 1.
  UpperCAmelCase			:      List[str]			=			[
      (item, score / len(UpperCamelCase		)) for item, score in population_score
  ]

  # This is selection
  for i in range(UpperCamelCase		):
   population.extend(select(population_score[int(UpperCamelCase		)]    ,	UpperCamelCase    ,	UpperCamelCase		)		)
   # 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(UpperCamelCase		) > N_POPULATION:
    break


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