



"""simple docstring"""






from argparse import ArgumentParser

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


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




def 			snake_case		(		lowerCAmelCase_	)						->				Any:
  if not path:
    return "pipe"

  for ext in PipelineDataFormat.SUPPORTED_FORMATS:
    if path.endswith(lowerCAmelCase_	):
      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 			snake_case		(		lowerCAmelCase_	)						->				List[str]:
  _snake_case   	=      pipeline(
      task=args.task  ,						model=args.model if args.model else None  ,						config=args.config  ,						tokenizer=args.tokenizer  ,						device=args.device  ,						)
  _snake_case   	=      try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
  _snake_case   	=      PipelineDataFormat.from_str(
      format=lowerCAmelCase_  ,						output_path=args.output  ,						input_path=args.input  ,						column=args.column if args.column else nlp.default_input_names  ,						overwrite=args.overwrite  ,						)
  return RunCommand(lowerCAmelCase_  ,						lowerCAmelCase_	)







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	Pipeline							,       __lowerCamelCase    :	PipelineDataFormat				):



     """simple docstring"""
     _snake_case   	=      nlp
     _snake_case   	=      reader



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	ArgumentParser				):



     """simple docstring"""
     _snake_case   	=      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=__lowerCamelCase							,       help='''Path to the file to use for inference'''				)
     run_parser.add_argument('''--output'''							,       type=__lowerCamelCase							,       help='''Path to the file that will be used post to write results.'''				)
     run_parser.add_argument('''--model'''							,       type=__lowerCamelCase							,       help='''Name or path to the model to instantiate.'''				)
     run_parser.add_argument('''--config'''							,       type=__lowerCamelCase							,       help='''Name or path to the model\'s config to instantiate.'''				)
     run_parser.add_argument(
         '''--tokenizer'''							,       type=__lowerCamelCase							,       help='''Name of the tokenizer to use. (default: same as the model name)'''				)
     run_parser.add_argument(
         '''--column'''							,       type=__lowerCamelCase							,       help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''							,       )
     run_parser.add_argument(
         '''--format'''							,       type=__lowerCamelCase							,       default='''infer'''							,       choices=PipelineDataFormat.SUPPORTED_FORMATS							,       help='''Input format to read from'''							,       )
     run_parser.add_argument(
         '''--device'''							,       type=__lowerCamelCase							,       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=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self._nlp, []

     for entry in self._reader:
       _snake_case   	=      nlp(**__lowerCamelCase				) if self._reader.is_multi_columns else nlp(__lowerCamelCase				)
       if isinstance(__lowerCamelCase							,       __lowerCamelCase				):
         outputs.append(__lowerCamelCase				)
       else:
         outputs += output

        # Saving data
     if self._nlp.binary_output:
       _snake_case   	=      self._reader.save_binary(__lowerCamelCase				)
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
     else:
       self._reader.save(__lowerCamelCase				)








from __future__ import annotations

from collections.abc import Sequence
from typing import Literal
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str       )					->				str | Literal[False]:
	__A :			Tuple		    =			list(a__       )
	__A :			Optional[int]		    =			list(a__       )
	__A :			int		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count += 1
			__A :			int		    =			"""_"""
	if count > 1:
		return False
	else:
		return "".join(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]       )					->				list[str]:
	__A :			Optional[Any]		    =			[]
	while True:
		__A :			Tuple		    =			["""$"""] * len(a__       )
		__A :			Union[str, Any]		    =			[]
		for i in range(len(a__       )       ):
			for j in range(i + 1			,len(a__       )       ):
				__A :			int		    =			compare_string(binary[i]			,binary[j]       )
				if k is False:
					__A :			List[str]		    =			"""*"""
					__A :			Any		    =			"""*"""
					temp.append("""X"""       )
		for i in range(len(a__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(a__       ) == 0:
			return pi
		__A :			Optional[Any]		    =			list(set(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Sequence[float]       )					->				list[str]:
	__A :			List[str]		    =			[]
	for minterm in minterms:
		__A :			List[Any]		    =			""""""
		for _ in range(a__       ):
			__A :			Union[str, Any]		    =			str(minterm % 2       ) + string
			minterm //= 2
		temp.append(a__       )
	return temp
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   int       )					->				bool:
	__A :			Optional[Any]		    =			list(a__       )
	__A :			Tuple		    =			list(a__       )
	__A :			Any		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[list[int]]			,a__    :   list[str]       )					->				list[str]:
	__A :			Optional[int]		    =			[]
	__A :			Tuple		    =			[0] * len(a__       )
	for i in range(len(chart[0]       )       ):
		__A :			str		    =			0
		__A :			Any		    =			-1
		for j in range(len(a__       )       ):
			if chart[j][i] == 1:
				count += 1
				__A :			Optional[Any]		    =			j
		if count == 1:
			__A :			int		    =			1
	for i in range(len(a__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(a__       )       ):
						__A :			List[str]		    =			0
			temp.append(prime_implicants[i]       )
	while True:
		__A :			Optional[Any]		    =			0
		__A :			Any		    =			-1
		__A :			int		    =			0
		for i in range(len(a__       )       ):
			__A :			List[Any]		    =			chart[i].count(1       )
			if count_n > max_n:
				__A :			Dict		    =			count_n
				__A :			Tuple		    =			i

		if max_n == 0:
			return temp

		temp.append(prime_implicants[rem]       )

		for i in range(len(chart[0]       )       ):
			if chart[rem][i] == 1:
				for j in range(len(a__       )       ):
					__A :			Union[str, Any]		    =			0
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]			,a__    :   list[str]       )					->				list[list[int]]:
	__A :			Any		    =			[[0 for x in range(len(a__       )       )] for x in range(len(a__       )       )]
	for i in range(len(a__       )       ):
		__A :			List[Any]		    =			prime_implicants[i].count("""_"""       )
		for j in range(len(a__       )       ):
			if is_for_table(prime_implicants[i]			,binary[j]			,a__       ):
				__A :			Union[str, Any]		    =			1

	return chart
def 							__SCREAMING_SNAKE_CASE     (   )					->				None:
	__A :			Any		    =			int(input("""Enter the no. of variables\n"""       )       )
	__A :			List[str]		    =			[
	    float(a__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	__A :			Dict		    =			decimal_to_binary(a__			,a__       )

	__A :			Union[str, Any]		    =			check(a__       )
	print("""Prime Implicants are:"""       )
	print(a__       )
	__A :			Optional[Any]		    =			prime_implicant_chart(a__			,a__       )

	__A :			Any		    =			selection(a__			,a__       )
	print("""Essential Prime Implicants are:"""       )
	print(a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





