
"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase__		:List[str]			  =   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.
lowercase__		:Dict			  =   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.
lowercase__		:List[Any]			  =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						len([g for position, g in enumerate(lowerCAmelCase_		) if g == main_target[position]]		)
					return (item, float(lowerCAmelCase_		))



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						random.randint(0			,      len(lowerCAmelCase_		) - 1		)
					lowercase						     =						parent_a[:random_slice] + parent_a[random_slice:]
					lowercase						     =						parent_a[:random_slice] + parent_a[random_slice:]
					return (child_a, child_a)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						list(lowerCAmelCase_		)
					if random.uniform(0			,      1		) < MUTATION_PROBABILITY:
										lowercase						     =						random.choice(lowerCAmelCase_		)
					return "".join(lowerCAmelCase_		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      ):
					'''simple docstring'''
					lowercase						     =						[]
					# Generate more children proportionally to the fitness score.
					lowercase						     =						int(parent_a[1] * 100		) + 1
					lowercase						     =						10 if child_n >= 10 else child_n
					for _ in range(lowerCAmelCase_		):
										lowercase						     =						population_score[random.randint(0			,      lowerCAmelCase_		)][0]

										lowercase						     =						crossover(parent_a[0]			,      lowerCAmelCase_		)
										# Append new string to the population list.
										pop.append(mutate(lowerCAmelCase_			,      lowerCAmelCase_		)		)
										pop.append(mutate(lowerCAmelCase_			,      lowerCAmelCase_		)		)
					return pop



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__ = True		):
					'''simple docstring'''

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

					# Generate random starting population.
					lowercase						     =						[]
					for _ in range(lowerCAmelCase_		):
										population.append(''''''.join([random.choice(lowerCAmelCase_		) for i in range(len(lowerCAmelCase_		)		)]		)		)

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

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

										# 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.
										lowercase						     =						[evaluate(lowerCAmelCase_			,      lowerCAmelCase_		) for item in population]

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

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


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






import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__		:Optional[Any]			  =   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowercase__		:int			  =   direct_transformers_import(PATH_TO_TRANSFORMERS)

lowercase__		:List[Any]			  =   transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__		:List[str]			  =   {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						False
					for attribute in attributes:
										for modeling_source in source_strings:
															# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
															if (
															    f'config.{attribute}' in modeling_source
															    or f'getattr(config, "{attribute}"' in modeling_source
															    or f'getattr(self.config, "{attribute}"' in modeling_source
															):
																				lowercase						     =						True
															# Deal with multi-line cases
															elif (
															    re.search(
															        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'			,      lowerCAmelCase__			,      )
															    is not None
															):
																				lowercase						     =						True
															# `SequenceSummary` is called with `SequenceSummary(config)`
															elif attribute in [
															    "summary_type",
															    "summary_use_proj",
															    "summary_activation",
															    "summary_last_dropout",
															    "summary_proj_to_labels",
															    "summary_first_dropout",
															]:
																				if "SequenceSummary" in modeling_source:
																									lowercase						     =						True
															if attribute_used:
																				break
										if attribute_used:
															break

    # common and important attributes, even if they do not always appear in the modeling files
					lowercase						     =						[
					    '''bos_index''',
					    '''eos_index''',
					    '''pad_index''',
					    '''unk_index''',
					    '''mask_index''',
					    '''image_size''',
					    '''use_cache''',
					    '''out_features''',
					    '''out_indices''',
					]
					lowercase						     =						['''encoder_no_repeat_ngram_size''']

					# Special cases to be allowed
					lowercase						     =						True
					if not attribute_used:
										lowercase						     =						False
										for attribute in attributes:
															# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
															if attribute in ["is_encoder_decoder"] and default_value is True:
																				lowercase						     =						True
															elif attribute in ["tie_word_embeddings"] and default_value is False:
																				lowercase						     =						True

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				lowercase						     =						True
															elif attribute.endswith('''_token_id'''		):
																				lowercase						     =						True

															# configuration class specific cases
															if not case_allowed:
																				lowercase						     =						SPECIAL_CASES_TO_ALLOW.get(config_class.__name__			,      []		)
																				lowercase						     =						allowed_cases is True or attribute in allowed_cases

					return attribute_used or case_allowed



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						dict(inspect.signature(config_class.__init__		).parameters		)
					lowercase						     =						[x for x in list(signature.keys()		) if x not in ['''self''', '''kwargs''']]
					lowercase						     =						[signature[param].default for param in parameter_names]

					# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
					# as one variant is used, the test should pass
					lowercase						     =						{}
					if len(config_class.attribute_map		) > 0:
										lowercase						     =						{v: k for k, v in config_class.attribute_map.items()}

					# Get the path to modeling source files
					lowercase						     =						inspect.getsourcefile(lowerCAmelCase__		)
					lowercase						     =						os.path.dirname(lowerCAmelCase__		)
					# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
					lowercase						     =						[os.path.join(lowerCAmelCase__			,      lowerCAmelCase__		) for fn in os.listdir(lowerCAmelCase__		) if fn.startswith('''modeling_'''		)]

					# Get the source code strings
					lowercase						     =						[]
					for path in modeling_paths:
										if os.path.isfile(lowerCAmelCase__		):
															with open(lowerCAmelCase__		) as fp:
																				modeling_sources.append(fp.read()		)

					lowercase						     =						[]
					for config_param, default_value in zip(lowerCAmelCase__			,      lowerCAmelCase__		):
										# `attributes` here is all the variant names for `config_param`
										lowercase						     =						[config_param]
										# some configuration classes have non-empty `attribute_map`, and both names could be used in the
										# corresponding modeling files. As long as one of them appears, it is fine.
										if config_param in reversed_attribute_map:
															attributes.append(reversed_attribute_map[config_param]		)

										if not check_attribute_being_used(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
															unused_attributes.append(attributes[0]		)

					return sorted(lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						{}
					for _config_class in list(CONFIG_MAPPING.values()		):
										# Skip deprecated models
										if "models.deprecated" in _config_class.__module__:
															continue
										# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
										lowercase						     =						[
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class		)			,      lambda lowerCAmelCase__		: inspect.isclass(lowerCAmelCase__		)
										        and issubclass(lowerCAmelCase__			,      lowerCAmelCase__		)
										        and inspect.getmodule(lowerCAmelCase__		) == inspect.getmodule(_config_class		)			,      )
										]
										for config_class in config_classes_in_module:
															lowercase						     =						check_config_attributes_being_used(lowerCAmelCase__		)
															if len(lowerCAmelCase__		) > 0:
																				lowercase						     =						unused_attributes

					if len(lowerCAmelCase__		) > 0:
										lowercase						     =						'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
										for name, attributes in configs_with_unused_attributes.items():
															error += f'{name}: {attributes}\n'

										raise ValueError(lowerCAmelCase__		)


if __name__ == "__main__":
							check_config_attributes()




