
from argparse import ArgumentParser

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


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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str       )  ->     Dict:
						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_	:	str       )  ->     Dict:
						_lowercase					       =					pipeline(
						    task=args.task		,   model=args.model if args.model else None		,   config=args.config		,   tokenizer=args.tokenizer		,   device=args.device		,   )
						_lowercase					       =					try_infer_format_from_ext(args.input       ) if args.format == """infer""" else args.format
						_lowercase					       =					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_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					nlp
									_lowercase					       =					reader






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






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self._nlp, []

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

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

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int = 2_00_00_00       )  ->     int:
						_lowercase					       =					[0 for i in range(n + 1       )]
						_lowercase					       =					1
						_lowercase					       =					1

						for i in range(2		,   int(n**0.5       ) + 1       ):
												if primality_list[i] == 0:
																		for j in range(i * i		,   n + 1		,   SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					1
						_lowercase					       =					0
						for i in range(SCREAMING_SNAKE_CASE_       ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
		print(F'{solution() = }')