'''simple docstring'''






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
    import torch

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm


SCREAMING_SNAKE_CASE_     :   Tuple	   =			logging.get_logger(__name__)




@dataclass
class    a (       __a				):



  """simple docstring"""
  __lowerCAmelCase				      =					[
      "no_inference",
      "no_cuda",
      "no_tpu",
      "no_speed",
      "no_memory",
      "no_env_print",
      "no_multi_process",
  ]







  def __init__(							self   ,    **snake_case_					):

    '''simple docstring'''







    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        __UpperCAmelCase:					Tuple		= deprecated_arg[3:]
        setattr(self   ,    lowerCAmelCase_   ,    not kwargs.pop(lowerCAmelCase_					)					)
        logger.warning(
            F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
            F''' {positive_arg}={kwargs[positive_arg]}'''					)

    __UpperCAmelCase:					Optional[Any]		= kwargs.pop("""torchscript"""   ,    self.torchscript					)
    __UpperCAmelCase:					int		= kwargs.pop("""torch_xla_tpu_print_metrics"""   ,    self.torch_xla_tpu_print_metrics					)
    __UpperCAmelCase:					List[Any]		= kwargs.pop("""fp16_opt_level"""   ,    self.fpaa_opt_level					)
    super().__init__(**lowerCAmelCase_					)

  __lowerCAmelCase				      =					field(default=__a						,  metadata={"""help""": """Trace the models using torchscript"""}				)
  __lowerCAmelCase				      =					field(default=__a						,  metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}				)
  __lowerCAmelCase				      =					field(
      default="""O1"""						,  metadata={
          """help""": (
              """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
              """See details at https://nvidia.github.io/apex/amp.html"""
          )
      }						,  )







  @cached_property
  def    lowercase_  (							self					):

    '''simple docstring'''







    requires_backends(self   ,    ["""torch"""]					)
    logger.info("""PyTorch: setting up devices"""					)
    if not self.cuda:
      __UpperCAmelCase:					int		= torch.device("""cpu"""					)
      __UpperCAmelCase:					int		= 0
    elif is_torch_tpu_available():
      __UpperCAmelCase:					Union[str, Any]		= xm.xla_device()
      __UpperCAmelCase:					Optional[Any]		= 0
    else:
      __UpperCAmelCase:					List[str]		= torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
      __UpperCAmelCase:					int		= torch.cuda.device_count()
    return device, n_gpu







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return is_torch_tpu_available() and self.tpu







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    requires_backends(self   ,    ["""torch"""]					)
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    requires_backends(self   ,    ["""torch"""]					)
    return self._setup_devices[0]







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    requires_backends(self   ,    ["""torch"""]					)
    return self._setup_devices[1]







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.n_gpu > 0
'''simple docstring'''






import numpy

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






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  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  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






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






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


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