



'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase						      =       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.
__lowerCAmelCase						      =       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.
__lowerCAmelCase						      =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, float]:
		_a					:				List[Any]				=							len([g for position, g in enumerate(lowerCAmelCase_  ) if g == main_target[position]]  )
		return (item, float(lowerCAmelCase_  ))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, str]:
		_a					:				Dict				=							random.randint(0						,      len(lowerCAmelCase_  ) - 1  )
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				Optional[Any]				=							list(lowerCAmelCase_  )
		if random.uniform(0						,      1  ) < MUTATION_PROBABILITY:
				_a					:				Optional[int]				=							random.choice(lowerCAmelCase_  )
		return "".join(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				list[str]:
		_a					:				List[str]				=							[]
		# Generate more children proportionally to the fitness score.
		_a					:				Tuple				=							int(parent_a[1] * 100  ) + 1
		_a					:				Tuple				=							10 if child_n >= 10 else child_n
		for _ in range(lowerCAmelCase_  ):
				_a					:				Any				=							population_score[random.randint(0						,      lowerCAmelCase_  )][0]

				_a					:				Tuple				=							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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = True  )      ->				tuple[int, int, str]:

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				_a					:				Dict				=							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.
		_a					:				Optional[int]				=							sorted({c for c in target if c not in genes}  )
		if not_in_genes_list:
				_a					:				List[Any]				=							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
				raise ValueError(lowerCAmelCase_  )

		# Generate random starting population.
		_a					:				Union[str, Any]				=							[]
		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.
		_a					:				Union[str, Any]				=							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.
				_a					:				Optional[Any]				=							[evaluate(lowerCAmelCase_						,      lowerCAmelCase_  ) for item in population]

				# Check if there is a matching evolution.
				_a					:				Tuple				=							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.
				_a					:				Dict				=							population[: int(N_POPULATION / 3  )]
				population.clear()
				population.extend(lowerCAmelCase_  )
				# Normalize population score to be between 0 and 1.
				_a					:				Tuple				=							[
				    (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__":
			__lowerCAmelCase						      =       (
			    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
			)
			__lowerCAmelCase						      =       list(
			    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
			    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
			)
			__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase						      =       basic(target_str, genes_list)
			print(
			    f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
			)









'''simple docstring'''

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__lowerCAmelCase						      =       logging.get_logger(__name__)

class      __magic_name__						(   enum.Enum       ):
							lowerCAmelCase	:   Union[str, Any]           = 0
							lowerCAmelCase	:   Dict           = 1

@add_end_docstrings(_UpperCamelCase       )
class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   int           = 'generated'







							def __init__(							self :	List[Any]     ,*_UpperCAmelCase :	Any     ,**_UpperCAmelCase :	Optional[int]		):
									super().__init__(*_UpperCAmelCase     ,**_UpperCAmelCase		)

									self.check_model_type(
									    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
									    if self.framework == 'tf'
									    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING		)







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Tuple=None     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Union[str, Any]=None     ,_UpperCAmelCase :	Dict=None     ,_UpperCAmelCase :	Optional[int]=None     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Tuple				=							{}
									if truncation is not None:
											_a					:				Union[str, Any]				=							truncation

									_a					:				List[str]				=							generate_kwargs

									_a					:				Optional[Any]				=							{}
									if return_tensors is not None and return_type is None:
											_a					:				Optional[int]				=							ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
											_a					:				List[Any]				=							return_type

									if clean_up_tokenization_spaces is not None:
											_a					:				Optional[int]				=							clean_up_tokenization_spaces

									if stop_sequence is not None:
											_a					:				List[str]				=							self.tokenizer.encode(_UpperCAmelCase     ,add_special_tokens=_UpperCAmelCase		)
											if len(_UpperCAmelCase		) > 1:
													warnings.warn(
													    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
													    ' the stop sequence will be used as the stop sequence string in the interim.'		)
											_a					:				List[Any]				=							stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									return True







							def 							__lowercase      (							self :	str     ,*_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Union[str, Any]		):
									_a					:				Optional[Any]				=							self.model.config.prefix if self.model.config.prefix is not None else ''
									if isinstance(args[0]     ,_UpperCAmelCase		):
											if self.tokenizer.pad_token_id is None:
													raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'		)
											_a					:				Optional[Any]				=							([prefix + arg for arg in args[0]],)
											_a					:				Any				=							True

									elif isinstance(args[0]     ,_UpperCAmelCase		):
											_a					:				List[str]				=							(prefix + args[0],)
											_a					:				str				=							False
									else:
											raise ValueError(
											    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""		)
									_a					:				Optional[Any]				=							self.tokenizer(*_UpperCAmelCase     ,padding=_UpperCAmelCase     ,truncation=_UpperCAmelCase     ,return_tensors=self.framework		)
									# This is produced by tokenizers but is an invalid generate kwargs
									if "token_type_ids" in inputs:
											del inputs["token_type_ids"]
									return inputs







							def __call__(							self :	Optional[int]     ,*_UpperCAmelCase :	List[Any]     ,**_UpperCAmelCase :	Any		):
									_a					:				Tuple				=							super().__call__(*_UpperCAmelCase     ,**_UpperCAmelCase		)
									if (
									    isinstance(args[0]     ,_UpperCAmelCase		)
									    and all(isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) for el in args[0]		)
									    and all(len(_UpperCAmelCase		) == 1 for res in result		)
									):
											return [res[0] for res in result]
									return result







							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,**_UpperCAmelCase :	int		):
									_a					:				Dict				=							self._parse_and_tokenize(_UpperCAmelCase     ,truncation=_UpperCAmelCase     ,**_UpperCAmelCase		)
									return inputs







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,**_UpperCAmelCase :	Tuple		):
									if self.framework == "pt":
											_a	, _a					:				int				=							model_inputs['input_ids'].shape
									elif self.framework == "tf":
											_a	, _a					:				Dict				=							tf.shape(model_inputs['input_ids']		).numpy()

									_a					:				Optional[Any]				=							generate_kwargs.get('min_length'     ,self.model.config.min_length		)
									_a					:				Optional[int]				=							generate_kwargs.get('max_length'     ,self.model.config.max_length		)
									self.check_inputs(_UpperCAmelCase     ,generate_kwargs['min_length']     ,generate_kwargs['max_length']		)
									_a					:				List[str]				=							self.model.generate(**_UpperCAmelCase     ,**_UpperCAmelCase		)
									_a					:				int				=							output_ids.shape[0]
									if self.framework == "pt":
											_a					:				int				=							output_ids.reshape(_UpperCAmelCase     ,out_b // in_b     ,*output_ids.shape[1:]		)
									elif self.framework == "tf":
											_a					:				Any				=							tf.reshape(_UpperCAmelCase     ,(in_b, out_b // in_b, *output_ids.shape[1:])		)
									return {"output_ids": output_ids}







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any=ReturnType.TEXT     ,_UpperCAmelCase :	Dict=False		):
									_a					:				Union[str, Any]				=							[]
									for output_ids in model_outputs["output_ids"][0]:
											if return_type == ReturnType.TENSORS:
													_a					:				int				=							{F"""{self.return_name}_token_ids""": output_ids}
											elif return_type == ReturnType.TEXT:
													_a					:				str				=							{
													    F"""{self.return_name}_text""": self.tokenizer.decode(
													        _UpperCAmelCase     ,skip_special_tokens=_UpperCAmelCase     ,clean_up_tokenization_spaces=_UpperCAmelCase     ,)
													}
											records.append(_UpperCAmelCase		)
									return records

@add_end_docstrings(_UpperCamelCase       )
class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Any           = 'summary'







							def __call__(							self :	Any     ,*_UpperCAmelCase :	Union[str, Any]     ,**_UpperCAmelCase :	List[str]		):
									return super().__call__(*_UpperCAmelCase     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									if max_length < min_length:
											logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""		)

									if input_length < max_length:
											logger.warning(
											    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
											    'a summarization task, where outputs shorter than the input are typically wanted, you might '
											    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""		)

@add_end_docstrings(_UpperCamelCase       )
class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   List[Any]           = 'translation'







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									if input_length > 0.9 * max_length:
											logger.warning(
											    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
											    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'		)
									return True







							def 							__lowercase      (							self :	Any     ,*_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	Any=None		):
									if getattr(self.tokenizer     ,'_build_translation_inputs'     ,_UpperCAmelCase		):
											return self.tokenizer._build_translation_inputs(
											    *_UpperCAmelCase     ,return_tensors=self.framework     ,truncation=_UpperCAmelCase     ,src_lang=_UpperCAmelCase     ,tgt_lang=_UpperCAmelCase		)
									else:
											return super()._parse_and_tokenize(*_UpperCAmelCase     ,truncation=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	List[str]=None     ,**_UpperCAmelCase :	Dict		):
									_a	, _a	, _a					:				str				=							super()._sanitize_parameters(**_UpperCAmelCase		)
									if src_lang is not None:
											_a					:				Optional[int]				=							src_lang
									if tgt_lang is not None:
											_a					:				List[Any]				=							tgt_lang
									if src_lang is None and tgt_lang is None:
											# Backward compatibility, direct arguments use is preferred.
											_a					:				int				=							kwargs.get('task'     ,self.task		)
											_a					:				int				=							task.split('_'		)
											if task and len(_UpperCAmelCase		) == 4:
													# translation, XX, to YY
													_a					:				List[Any]				=							items[1]
													_a					:				Any				=							items[3]
									return preprocess_params, forward_params, postprocess_params







							def __call__(							self :	Optional[Any]     ,*_UpperCAmelCase :	Tuple     ,**_UpperCAmelCase :	Optional[int]		):
									return super().__call__(*_UpperCAmelCase     ,**_UpperCAmelCase		)




