

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_UpperCamelCase 			=		logging.get_logger(__name__)  # pylint: disable=invalid-name







def 		_lowercase (     lowercase__						):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(lowercase__						):
                  return ext

      raise Exception(
          f"""Unable to determine file format from file extension {path}. """
          f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Any					=					pipeline(
          task=args.task   ,	model=args.model if args.model else None   ,	config=args.config   ,	tokenizer=args.tokenizer   ,	device=args.device   ,	)
      __lowerCAmelCase					:						Dict					=					try_infer_format_from_ext(args.input						) if args.format == '''infer''' else args.format
      __lowerCAmelCase					:						Dict					=					PipelineDataFormat.from_str(
          format=lowercase__   ,	output_path=args.output   ,	input_path=args.input   ,	column=args.column if args.column else nlp.default_input_names   ,	overwrite=args.overwrite   ,	)
      return RunCommand(lowercase__   ,	lowercase__						)







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					nlp
       __lowerCAmelCase					:						Tuple					=					reader
 @staticmethod
 def 	UpperCamelCase__  (							A_					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					parser.add_parser('''run'''      ,							help='''Run a pipeline through the CLI'''					)
       run_parser.add_argument('''--task'''      ,							choices=get_supported_tasks()      ,							help='''Task to run'''					)
       run_parser.add_argument('''--input'''      ,							type=A_      ,							help='''Path to the file to use for inference'''					)
       run_parser.add_argument('''--output'''      ,							type=A_      ,							help='''Path to the file that will be used post to write results.'''					)
       run_parser.add_argument('''--model'''      ,							type=A_      ,							help='''Name or path to the model to instantiate.'''					)
       run_parser.add_argument('''--config'''      ,							type=A_      ,							help='''Name or path to the model\'s config to instantiate.'''					)
       run_parser.add_argument(
           '''--tokenizer'''      ,							type=A_      ,							help='''Name of the tokenizer to use. (default: same as the model name)'''					)
       run_parser.add_argument(
           '''--column'''      ,							type=A_      ,							help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''      ,							)
       run_parser.add_argument(
           '''--format'''      ,							type=A_      ,							default='''infer'''      ,							choices=PipelineDataFormat.SUPPORTED_FORMATS      ,							help='''Input format to read from'''      ,							)
       run_parser.add_argument(
           '''--device'''      ,							type=A_      ,							default=-1      ,							help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''      ,							)
       run_parser.add_argument('''--overwrite'''      ,							action='''store_true'''      ,							help='''Allow overwriting the output file.'''					)
       run_parser.set_defaults(func=A_					)





 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase					:						Any					=					self._nlp, []

       for entry in self._reader:
             __lowerCAmelCase					:						Optional[int]					=					nlp(**A_					) if self._reader.is_multi_columns else nlp(A_					)
             if isinstance(A_      ,							A_					):
                   outputs.append(A_					)
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             __lowerCAmelCase					:						Union[str, Any]					=					self._reader.save_binary(A_					)
             logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""					)
       else:
             self._reader.save(A_					)





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCamelCase 			=		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 			=		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 			=		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					len([g for position, g in enumerate(lowercase__						) if g == main_target[position]]						)
      return (item, float(lowercase__						))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						str					=					random.randint(0   ,	len(lowercase__						) - 1						)
      __lowerCAmelCase					:						int					=					parent_a[:random_slice] + parent_a[random_slice:]
      __lowerCAmelCase					:						Dict					=					parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					list(lowercase__						)
      if random.uniform(0   ,	1						) < MUTATION_PROBABILITY:
            __lowerCAmelCase					:						int					=					random.choice(lowercase__						)
      return "".join(lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						str					=					[]
      # Generate more children proportionally to the fitness score.
      __lowerCAmelCase					:						str					=					int(parent_a[1] * 1_0_0						) + 1
      __lowerCAmelCase					:						Optional[Any]					=					1_0 if child_n >= 1_0 else child_n
      for _ in range(lowercase__						):
            __lowerCAmelCase					:						List[Any]					=					population_score[random.randint(0   ,	lowercase__						)][0]

            __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					crossover(parent_a[0]   ,	lowercase__						)
            # Append new string to the population list.
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
      return pop







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__ = True						):

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

      # Generate random starting population.
      __lowerCAmelCase					:						List[Any]					=					[]
      for _ in range(lowercase__						):
            population.append(''''''.join([random.choice(lowercase__						) for i in range(len(lowercase__						)						)]						)						)

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

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

            # 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.
            __lowerCAmelCase					:						Any					=					[evaluate(lowercase__   ,	lowercase__						) for item in population]

            # Check if there is a matching evolution.
            __lowerCAmelCase					:						Union[str, Any]					=					sorted(lowercase__   ,	key=lambda lowercase__						: x[1]   ,	reverse=lowercase__						)
            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 % 1_0 == 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.
            __lowerCAmelCase					:						Tuple					=					population[: int(N_POPULATION / 3						)]
            population.clear()
            population.extend(lowercase__						)
            # Normalize population score to be between 0 and 1.
            __lowerCAmelCase					:						List[Any]					=					[
                (item, score / len(lowercase__						)) for item, score in population_score
            ]

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


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


