
'''simple docstring'''




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 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    return EnvironmentCommand()







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    return EnvironmentCommand(args.accelerate_config_file		)







class 					A							(				__snake_case		):





     @staticmethod
     def 							__lowerCAmelCase				(				SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  Dict   =		parser.add_parser('''env'''      )
         download_parser.set_defaults(func=SCREAMING_SNAKE_CASE      )
         download_parser.add_argument(
             '''--accelerate-config_file'''		,     default=SCREAMING_SNAKE_CASE		,     help='''The accelerate config file to use for the default values in the launching script.'''		,     )
         download_parser.set_defaults(func=SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         A			:  Optional[Any]   =		accelerate_config_file





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






         """simple docstring"""






         A			:  List[Any]   =		'''not installed'''
         if is_safetensors_available():
             import safetensors

             A			:  Optional[int]   =		safetensors.__version__
         elif importlib.util.find_spec('''safetensors'''      ) is not None:
             import safetensors

             A			:  Optional[Any]   =		F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

         A			:  Optional[int]   =		'''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			:  Any   =		accelerate.__version__
             # Get the default from the config file.
             if self._accelerate_config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE      ):
                 A			:  Any   =		load_config_from_file(self._accelerate_config_file      ).to_dict()

             A			:  Any   =		(
                 '''\n'''.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]      )
                 if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
                 else F'\t{accelerate_config}'
             )

         A			:  List[Any]   =		'''not installed'''
         A			:  List[Any]   =		'''NA'''
         if is_torch_available():
             import torch

             A			:  List[Any]   =		torch.__version__
             A			:  int   =		torch.cuda.is_available()

         A			:  Any   =		'''not installed'''
         A			:  int   =		'''NA'''
         if is_tf_available():
             import tensorflow as tf

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

         A			:  int   =		'''not installed'''
         A			:  Dict   =		'''not installed'''
         A			:  List[Any]   =		'''not installed'''
         A			:  Optional[int]   =		'''NA'''
         if is_flax_available():
             import flax
             import jax
             import jaxlib

             A			:  str   =		flax.__version__
             A			:  Any   =		jax.__version__
             A			:  Dict   =		jaxlib.__version__
             A			:  Any   =		jax.lib.xla_bridge.get_backend().platform

         A			:  List[str]   =		{
             '''`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(SCREAMING_SNAKE_CASE      )      )

         return info





     @staticmethod
     def 							__lowerCAmelCase				(				SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






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






'''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()




