

"""simple docstring"""





from argparse import ArgumentParser

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


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




def   _A  (  UpperCamelCase_					:	str)     ->						Union[str, Any]:
   '''simple docstring'''


   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCamelCase_):
         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  (  UpperCamelCase_					:	int)     ->						Any:
   '''simple docstring'''


   __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=UpperCamelCase_,      output_path=args.output,      input_path=args.input,      column=args.column if args.column else nlp.default_input_names,      overwrite=args.overwrite,      )
   return RunCommand(UpperCamelCase_,      UpperCamelCase_)




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      str, UpperCAmelCase__							:      Pipeline, UpperCAmelCase__							:      PipelineDataFormat     ):
    __lowercase								=						nlp
    __lowercase								=						reader


 @staticmethod
 def  _lowercase			(      UpperCAmelCase__							:      ArgumentParser     ):
    __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  _lowercase			(      self							:      Any     ):
    __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__     )







"""simple docstring"""





import numpy

# List of input, output pairs
_a							=							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a							=							(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_a							=							[2, 4, 1, 5]
_a							=							len(train_data)
_a							=							0.009




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[Any]="train")     ->						Optional[Any]:
   '''simple docstring'''


   return calculate_hypothesis_value(UpperCamelCase_,      UpperCamelCase_) - output(
       UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''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   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						int:
   '''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   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple=m)     ->						int:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(UpperCamelCase_):
      if index == -1:
         summation_value += _error(UpperCamelCase_)
      else:
         summation_value += _error(UpperCamelCase_) * train_data[i][0][index]
   return summation_value




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   __lowercase								=						summation_of_cost_derivative(UpperCamelCase_,      UpperCamelCase_) / m
   return cost_derivative_value




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowercase								=						0.000_002
   __lowercase								=						0
   __lowercase								=						0
   while True:
      j += 1
      __lowercase								=						[0, 0, 0, 0]
      for i in range(0,      len(UpperCamelCase_)):
         __lowercase								=						get_cost_derivative(i - 1)
         __lowercase								=						(
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          UpperCamelCase_,      UpperCamelCase_,      atol=UpperCamelCase_,      rtol=UpperCamelCase_,      ):
         break
      __lowercase								=						temp_parameter_vector
   print(("Number of iterations:", j))




def   _A  (  )     ->						int:
   '''simple docstring'''


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


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




