


"""simple docstring"""




import numpy as np
import requests
def     UpperCAmelCase_ (							)					->      Optional[int]:







 """simple docstring"""


 _lowercase											=requests.get(
     '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
     '''master/Week1/ADRvsRating.csv'''						)
 _lowercase											=response.text.splitlines()
 _lowercase											=[]
 for item in lines:
  _lowercase											=item.split(''','''						)
  data.append(snake_case__						)
 data.pop(0						)  # This is for removing the labels from the list
 _lowercase											=np.matrix(snake_case__						)
 return dataset
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      str:







 """simple docstring"""


 _lowercase											=len_data

 _lowercase											=np.dot(snake_case__					,						data_x.transpose()						)
 prod -= data_y.transpose()
 _lowercase											=np.dot(snake_case__					,						snake_case__						)
 _lowercase											=theta - (alpha / n) * sum_grad
 return theta
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      List[str]:







 """simple docstring"""


 _lowercase											=np.dot(snake_case__					,						data_x.transpose()						)
 prod -= data_y.transpose()
 _lowercase											=np.sum(np.square(snake_case__						)						)
 _lowercase											=sum_elem / (2 * len_data)
 return error
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      str:







 """simple docstring"""


 _lowercase											=100000
 _lowercase											=0.0_00_15_50

 _lowercase											=data_x.shape[1]
 _lowercase											=data_x.shape[0] - 1

 _lowercase											=np.zeros((1, no_features)						)

 for i in range(0					,						snake_case__						):
  _lowercase											=run_steep_gradient_descent(snake_case__					,						snake_case__					,						snake_case__					,						snake_case__					,						snake_case__						)
  _lowercase											=sum_of_square_error(snake_case__					,						snake_case__					,						snake_case__					,						snake_case__						)
  print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

 return theta
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      List[str]:







 """simple docstring"""


 _lowercase											=sum(abs(y - predicted_y[i]						) for i, y in enumerate(snake_case__						)						)
 return total / len(snake_case__						)
def     UpperCAmelCase_ (							)					->      Optional[int]:







 """simple docstring"""


 _lowercase											=collect_dataset()

 _lowercase											=data.shape[0]
 _lowercase											=np.c_[np.ones(snake_case__						), data[:, :-1]].astype(snake_case__						)
 _lowercase											=data[:, -1].astype(snake_case__						)

 _lowercase											=run_linear_regression(snake_case__					,						snake_case__						)
 _lowercase											=theta.shape[1]
 print('''Resultant Feature vector : '''						)
 for i in range(0					,						snake_case__						):
  print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
  main()





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=BertConfig.from_pretrained(
     '''bert-large-cased'''					,						vocab_size=__snake_case					,						max_position_embeddings=512					,						is_decoder=__snake_case					,						add_cross_attention=__snake_case					,						)
 _lowercase											=bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 _lowercase											=BertGenerationConfig(**__snake_case						)
 if is_encoder:
  _lowercase											=BertGenerationEncoder(__snake_case						)
 else:
  _lowercase											=BertGenerationDecoder(__snake_case						)
 print(F"Building PyTorch model from configuration: {config}"						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     __snake_case					,						__snake_case					,						model_class='''bert'''					,						is_encoder_named_decoder=__snake_case					,						is_encoder=__snake_case					,						)

 # Save pytorch-model
 print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
 model.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--is_encoder_named_decoder''',
      action='''store_true''',
      help='''If decoder has to be renamed to encoder in PyTorch model.''',
  )
  parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
  parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
  UpperCAmelCase__            =							parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )

