
'''simple docstring'''



import numpy as np
import requests
def       lowerCAmelCase_		( )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = requests.get(
       """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
       """master/Week1/ADRvsRating.csv"""  )
   _SCREAMING_SNAKE_CASE          = response.text.splitlines()
   _SCREAMING_SNAKE_CASE          = []
   for item in lines:
      _SCREAMING_SNAKE_CASE          = item.split(""","""  )
      data.append(SCREAMING_SNAKE_CASE_  )
   data.pop(0  )  # This is for removing the labels from the list
   _SCREAMING_SNAKE_CASE          = np.matrix(SCREAMING_SNAKE_CASE_  )
   return dataset
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = len_data

   _SCREAMING_SNAKE_CASE          = np.dot(SCREAMING_SNAKE_CASE_			,					data_x.transpose()  )
   prod -= data_y.transpose()
   _SCREAMING_SNAKE_CASE          = np.dot(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = theta - (alpha / n) * sum_grad
   return theta
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = np.dot(SCREAMING_SNAKE_CASE_			,					data_x.transpose()  )
   prod -= data_y.transpose()
   _SCREAMING_SNAKE_CASE          = np.sum(np.square(SCREAMING_SNAKE_CASE_  )  )
   _SCREAMING_SNAKE_CASE          = sum_elem / (2 * len_data)
   return error
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = 10_00_00
   _SCREAMING_SNAKE_CASE          = 0.000_1550

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

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

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

   return theta
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = sum(abs(y - predicted_y[i]  ) for i, y in enumerate(SCREAMING_SNAKE_CASE_  )  )
   return total / len(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( )   ->   Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = collect_dataset()

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

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


if __name__ == "__main__":
     main()








'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 2:
             raise ValueError(
                 F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                 """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                 """'gated-gelu' or 'relu'"""					)

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






