


from argparse import ArgumentParser

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


lowercase_	   =     logging.get_logger(__name__)  # pylint: disable=invalid-name
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
	if not path:
		return "pipe"

	for ext in PipelineDataFormat.SUPPORTED_FORMATS:
		if path.endswith(SCREAMING_SNAKE_CASE__						):
			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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
	__lowerCamelCase		:							int		 =						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		:							str		 =						PipelineDataFormat.from_str(
	    format=SCREAMING_SNAKE_CASE__      ,	output_path=args.output      ,	input_path=args.input      ,	column=args.column if args.column else nlp.default_input_names      ,	overwrite=args.overwrite      ,	)
	return RunCommand(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)



class 	A_ (	__UpperCamelCase   ):




						'''simple docstring'''


						def __init__(    self:	List[str]						,				a:	Pipeline						,				a:	PipelineDataFormat    ):
							__lowerCamelCase		:							str		 =						nlp
							__lowerCamelCase		:							int		 =						reader




						@staticmethod
						def 					_snake_case     (    a:	ArgumentParser    ):
							__lowerCamelCase		:							Optional[Any]		 =						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 					_snake_case     (    self:	Dict    ):
							__lowerCamelCase		:							Any		 =						self._nlp, []

							for entry in self._reader:
								__lowerCamelCase		:							Tuple		 =						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		:							List[str]		 =						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    )





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						len(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

 # for each arr value, a sum of zero(0) can be formed by not taking any element
 # hence True/1
 for i in range(arr_len + 1						):
  __lowerCamelCase		:							Dict		 =						True

 # sum is not zero and set is empty then false
 for i in range(1      ,	required_sum + 1						):
  __lowerCamelCase		:							Union[str, Any]		 =						False

 for i in range(1      ,	arr_len + 1						):
  for j in range(1      ,	required_sum + 1						):
   if arr[i - 1] > j:
    __lowerCamelCase		:							Tuple		 =						subset[i - 1][j]
   if arr[i - 1] <= j:
    __lowerCamelCase		:							List[Any]		 =						subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

 return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

