from argparse import ArgumentParser

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


SCREAMING_SNAKE_CASE__		:       List[str]       						=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      _a					(   lowercase__	:      str			):







     '''simple docstring'''


     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      _a					(   lowercase__	:      Dict			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Any      =		pipeline(
         task=args.task  ,	model=args.model if args.model else None  ,	config=args.config  ,	tokenizer=args.tokenizer  ,	device=args.device  ,	)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		try_infer_format_from_ext(args.input			) if args.format == 'infer' else args.format
     SCREAMING_SNAKE_CASE__					:					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    snake_case       (   UpperCamelCase_    ):




  def __init__(   self  :						Any  ,			a_  :						Pipeline  ,			a_  :						PipelineDataFormat					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		nlp
       SCREAMING_SNAKE_CASE__					:					Tuple      =		reader




  @staticmethod
  def 						__lowercase(   a_  :						ArgumentParser					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					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 						__lowercase(   self  :						Any					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		self._nlp, []

       for entry in self._reader:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		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 math
from collections.abc import Callable


def       UpperCamelCase__(			UpperCamelCase__     :			Callable[[int | float], int | float]					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int = 1_00					,	)->float:
       A__		   =		x_start
       A__		   =		fnc(UpperCamelCase__    )
       A__		   =		0.0

       for _ in range(UpperCamelCase__    ):
              # Approximates curve as a sequence of linear lines and sums their length
              A__		   =		(x_end - x_start) / steps + xa
              A__		   =		fnc(UpperCamelCase__    )
              length += math.hypot(xa - xa					,	fxa - fxa    )

              # Increment step
              A__		   =		xa
              A__		   =		fxa

       return length


if __name__ == "__main__":


       def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]    )->Dict:
              return math.sin(10 * x    )

       print('f(x) = sin(10 * x)')
       print('The length of the curve from x = -10 to x = 10 is:')
       a__: str							   =						10
       while i <= 100_000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10





