


'''simple docstring'''
from argparse import ArgumentParser

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


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





def    a__			(   lowerCAmelCase__		)       ->      Dict:



    if not path:
        return "pipe"

    for ext in PipelineDataFormat.SUPPORTED_FORMATS:
        if path.endswith(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    a__			(   lowerCAmelCase__		)       ->      Union[str, Any]:
    UpperCAmelCase__     :						str						=							pipeline(
        task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
    UpperCAmelCase__     :						List[str]						=							try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
    UpperCAmelCase__     :						Tuple						=							PipelineDataFormat.from_str(
        format=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(snake_case__    ,   snake_case__		)







class      lowerCamelCase_  (      __snake_case ):






      def __init__(   self							:       Optional[Any]		,		_A							:       Optional[Any]		,		_A							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							nlp
          UpperCAmelCase__     :						str						=							reader






      @staticmethod
      def lowercase_				(   _A							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							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							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							self._nlp, []

          for entry in self._reader:
              UpperCAmelCase__     :						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:
              UpperCAmelCase__     :						Optional[int]						=							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					)


'''simple docstring'''




import numpy

# List of input, output pairs
lowercase     :							Any       						=     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase     :							str       						=     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowercase     :							Union[str, Any]       						=     [2, 4, 1, 5]
lowercase     :							Any       						=     len(train_data)
lowercase     :							Optional[int]       						=     0.0_09







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__="train"		):




    '''simple docstring'''



    return calculate_hypothesis_value(snake_case__						,							snake_case__		) - output(
        snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    for i in range(len(snake_case__		) - 1		):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0]		)
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0]		)
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__=m		):




    '''simple docstring'''



    A			:  List[Any]   =		0
    for i in range(snake_case__		):
        if index == -1:
            summation_value += _error(snake_case__		)
        else:
            summation_value += _error(snake_case__		) * train_data[i][0][index]
    return summation_value







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		summation_of_cost_derivative(snake_case__						,							snake_case__		) / m
    return cost_derivative_value







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A			:  Dict   =		0.00_00_02
    A			:  Optional[Any]   =		0
    A			:  int   =		0
    while True:
        j += 1
        A			:  List[str]   =		[0, 0, 0, 0]
        for i in range(0						,							len(snake_case__		)		):
            A			:  Union[str, Any]   =		get_cost_derivative(i - 1		)
            A			:  Tuple   =		(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            snake_case__						,							snake_case__						,							atol=snake_case__						,							rtol=snake_case__						,							):
            break
        A			:  List[Any]   =		temp_parameter_vector
    print(('''Number of iterations:''', j)		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    for i in range(len(snake_case__		)		):
        print(('''Actual output value:''', output(snake_case__						,							'''test'''		))		)
        print(('''Hypothesis output:''', calculate_hypothesis_value(snake_case__						,							'''test'''		))		)


if __name__ == "__main__":
      run_gradient_descent()
      print('\nTesting gradient descent for a linear hypothesis function.\n')
      test_gradient_descent()




