


'''simple docstring'''



import numpy

# List of input, output pairs
A__						:   Any				=    (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
A__						:   Union[str, Any]				=    (((515, 22, 13), 555), ((61, 35, 49), 150))
A__						:   str				=    [2, 4, 1, 5]
A__						:   Dict				=    len(train_data)
A__						:   Tuple				=    0.0_0_9



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							List[Any]="train"				)	->						str:
     return calculate_hypothesis_value(UpperCAmelCase_ ,       UpperCAmelCase_				) - output(
         UpperCAmelCase_ ,       UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any]				)	->						str:
     __lowerCamelCase  :      Optional[int]														=						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      UpperCAmelCase__   (      UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							List[str]				)	->						Union[str, 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__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							Tuple				)	->						Tuple:
     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__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							int=m				)	->						Dict:
     __lowerCamelCase  :      List[str]														=						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      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int]				)	->						Optional[int]:
     __lowerCamelCase  :      str														=						summation_of_cost_derivative(UpperCAmelCase_ ,       UpperCAmelCase_				) / m
     return cost_derivative_value



def      UpperCAmelCase__   (      )	->						Optional[int]:
     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     __lowerCamelCase  :      List[str]														=						0.000_002
     __lowerCamelCase  :      str														=						0
     __lowerCamelCase  :      int														=						0
     while True:
          j += 1
          __lowerCamelCase  :      List[Any]														=						[0, 0, 0, 0]
          for i in range(0 ,       len(UpperCAmelCase_				)				):
               __lowerCamelCase  :      Any														=						get_cost_derivative(i - 1				)
               __lowerCamelCase  :      Tuple														=						(
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              UpperCAmelCase_ ,       UpperCAmelCase_ ,       atol=UpperCAmelCase_ ,       rtol=UpperCAmelCase_ ,       ):
               break
          __lowerCamelCase  :      Tuple														=						temp_parameter_vector
     print(('Number of iterations:', j)				)



def      UpperCAmelCase__   (      )	->						Tuple:
     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()










'''simple docstring'''



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A__						:   List[str]				=    """3"""

print("""Python version:""", sys.version)

print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
      print("""Torch version:""", None)

try:
      import transformers

      print("""transformers version:""", transformers.__version__)
except ImportError:
      print("""transformers version:""", None)






