





import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  return EnvironmentCommand()
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  return EnvironmentCommand(args.accelerate_config_file						)







class 				__lowerCAmelCase       (    UpperCamelCase__):







      @staticmethod
      def    _lowercase  (    lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					Tuple										=parser.add_parser("env"							)
        download_parser.set_defaults(func=lowerCAmelCase__							)
        download_parser.add_argument(
            "--accelerate-config_file"		,      default=lowerCAmelCase__		,      help="The accelerate config file to use for the default values in the launching script."		,      )
        download_parser.set_defaults(func=lowerCAmelCase__							)







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Optional[Any]										=accelerate_config_file







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					List[str]										="not installed"
        if is_safetensors_available():
          import safetensors

          a__	:					Tuple										=safetensors.__version__
        elif importlib.util.find_spec("safetensors"							) is not None:
          import safetensors

          a__	:					Tuple										=F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

        a__	:					Union[str, Any]										="not installed"
        a__	:					Union[str, Any]										="not found"
        if is_accelerate_available():
          import accelerate
          from accelerate.commands.config import default_config_file, load_config_from_file

          a__	:					List[str]										=accelerate.__version__
          # Get the default from the config file.
          if self._accelerate_config_file is not None or os.path.isfile(lowerCAmelCase__							):
            a__	:					int										=load_config_from_file(self._accelerate_config_file							).to_dict()

          a__	:					Union[str, Any]										=(
              "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]							)
              if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							)
              else F'''\t{accelerate_config}'''
          )

        a__	:					Tuple										="not installed"
        a__	:					List[str]										="NA"
        if is_torch_available():
          import torch

          a__	:					List[Any]										=torch.__version__
          a__	:					Tuple										=torch.cuda.is_available()

        a__	:					Optional[Any]										="not installed"
        a__	:					Optional[int]										="NA"
        if is_tf_available():
          import tensorflow as tf

          a__	:					List[str]										=tf.__version__
          try:
            # deprecated in v2.1
            a__	:					List[str]										=tf.test.is_gpu_available()
          except AttributeError:
            # returns list of devices, convert to bool
            a__	:					Optional[Any]										=bool(tf.config.list_physical_devices("GPU"							)							)

        a__	:					Optional[Any]										="not installed"
        a__	:					Union[str, Any]										="not installed"
        a__	:					Optional[int]										="not installed"
        a__	:					Optional[int]										="NA"
        if is_flax_available():
          import flax
          import jax
          import jaxlib

          a__	:					int										=flax.__version__
          a__	:					Dict										=jax.__version__
          a__	:					str										=jaxlib.__version__
          a__	:					List[str]										=jax.lib.xla_bridge.get_backend().platform

        a__	:					Any										={
            "`transformers` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "Huggingface_hub version": huggingface_hub.__version__,
            "Safetensors version": F'''{safetensors_version}''',
            "Accelerate version": F'''{accelerate_version}''',
            "Accelerate config": F'''{accelerate_config_str}''',
            "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
            "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
            "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
            "Jax version": F'''{jax_version}''',
            "JaxLib version": F'''{jaxlib_version}''',
            "Using GPU in script?": "<fill in>",
            "Using distributed or parallel set-up in script?": "<fill in>",
        }

        print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"							)
        print(self.format_dict(lowerCAmelCase__							)							)

        return info







      @staticmethod
      def    _lowercase  (    lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]							) + "\n"













import numpy

# List of input, output pairs
UpperCAmelCase	:	str		     =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	:	Optional[int]		     =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	:	str		     =       [2, 4, 1, 5]
UpperCAmelCase	:	List[str]		     =       len(train_data)
UpperCAmelCase	:	Dict		     =       0.0_0_9
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Tuple										=0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   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       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """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       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   int=m						):





  """simple docstring"""



  a__	:					Any										=0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					Any										=summation_of_cost_derivative(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def      _A       (		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__	:					Dict										=0.0_0_0_0_0_2
  a__	:					Union[str, Any]										=0
  a__	:					Any										=0
  while True:
    j += 1
    a__	:					Any										=[0, 0, 0, 0]
    for i in range(0       ,       len(SCREAMING_SNAKE_CASE						)						):
      a__	:					Tuple										=get_cost_derivative(i - 1						)
      a__	:					List[Any]										=(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       atol=SCREAMING_SNAKE_CASE       ,       rtol=SCREAMING_SNAKE_CASE       ,       ):
      break
    a__	:					Optional[Any]										=temp_parameter_vector
  print(("Number of iterations:", j)						)
def      _A       (		):





  """simple docstring"""



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


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






