




from argparse import ArgumentParser

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


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)  # pylint: disable=invalid-name

def 			_SCREAMING_SNAKE_CASE			(							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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    pipeline(
          task=args.task	,							model=args.model if args.model else None	,							config=args.config	,							tokenizer=args.tokenizer	,							device=args.device	,							)
      lowerCamelCase_		     =    try_infer_format_from_ext(args.input     ) if args.format == 'infer' else args.format
      lowerCamelCase_		     =    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   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  Tuple						,							A_					:  Pipeline						,							A_					:  PipelineDataFormat  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    nlp
       lowerCamelCase_		     =    reader

 @staticmethod
 def 				a__ (				A_					:  ArgumentParser  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    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 				a__ (				self					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_     ,				lowerCamelCase_		     =    self._nlp, []

       for entry in self._reader:
             lowerCamelCase_		     =    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_		     =    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 collections.abc import Callable

import numpy as np

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Callable	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float	,							lowercase	:       float     ):


      '''simple docstring'''



      lowerCamelCase_		     =    int(np.ceil((x_end - xa) / step_size     )     )
      lowerCamelCase_		     =    np.zeros((n + 1,)     )
      lowerCamelCase_		     =    ya
      lowerCamelCase_		     =    xa

      for k in range(lowercase     ):
            lowerCamelCase_		     =    y[k] + step_size * ode_func(lowercase	,							y[k]     )
            lowerCamelCase_		     =    y[k] + (
                (step_size / 2) * (ode_func(lowercase	,							y[k]     ) + ode_func(x + step_size	,							lowercase     ))
            )
            x += step_size

      return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()



