




'''simple docstring'''


import numpy as np
import requests
def 				SCREAMING_SNAKE_CASE(      ) ->   Union[str, Any]:
     A:		Any        =		requests.get(
         '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
         '''master/Week1/ADRvsRating.csv'''  )
     A:		Tuple        =		response.text.splitlines()
     A:		Optional[int]        =		[]
     for item in lines:
          A:		Union[str, Any]        =		item.split(''','''  )
          data.append(__lowercase  )
     data.pop(0  )  # This is for removing the labels from the list
     A:		Optional[Any]        =		np.matrix(__lowercase  )
     return dataset
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  ) ->   List[str]:
     A:		str        =		len_data

     A:		List[str]        =		np.dot(__lowercase		,							data_x.transpose()  )
     prod -= data_y.transpose()
     A:		Union[str, Any]        =		np.dot(__lowercase		,							__lowercase  )
     A:		Any        =		theta - (alpha / n) * sum_grad
     return theta
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase  ) ->   List[Any]:
     A:		Any        =		np.dot(__lowercase		,							data_x.transpose()  )
     prod -= data_y.transpose()
     A:		str        =		np.sum(np.square(__lowercase  )  )
     A:		Optional[Any]        =		sum_elem / (2 * len_data)
     return error
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   Optional[int]:
     A:		str        =		1_0_0_0_0_0
     A:		Tuple        =		0.0_0_0_1_5_5_0

     A:		List[Any]        =		data_x.shape[1]
     A:		List[str]        =		data_x.shape[0] - 1

     A:		Any        =		np.zeros((1, no_features)  )

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

     return theta
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   Any:
     A:		Optional[int]        =		sum(abs(y - predicted_y[i]  ) for i, y in enumerate(__lowercase  )  )
     return total / len(__lowercase  )
def 				SCREAMING_SNAKE_CASE(      ) ->   List[Any]:
     A:		Optional[Any]        =		collect_dataset()

     A:		List[str]        =		data.shape[0]
     A:		Optional[int]        =		np.c_[np.ones(__lowercase  ), data[:, :-1]].astype(__lowercase  )
     A:		List[str]        =		data[:, -1].astype(__lowercase  )

     A:		Optional[Any]        =		run_linear_regression(__lowercase		,							__lowercase  )
     A:		List[str]        =		theta.shape[1]
     print('''Resultant Feature vector : '''  )
     for i in range(0		,							__lowercase  ):
          print(F"""{theta[0, i]:.5f}"""  )


if __name__ == "__main__":
     main()












'''simple docstring'''


from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		int:







       '''simple docstring'''



       return True




  def 				_snake_case    (   self							:       Optional[int]  ) ->		List[str]:







       '''simple docstring'''



       A:		Optional[Any]        =		CpmTokenizer.from_pretrained('''TsinghuaAI/CPM-Generate'''  )
       A:		Optional[int]        =		'''Hugging Face大法好，谁用谁知道。'''
       A:		str        =		'''Hugging Face大法好,谁用谁知道。<unk>'''
       A:		Dict        =		'''▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'''.split()

       A:		int        =		tokenizer.tokenize(SCREAMING_SNAKE_CASE_  )
       self.assertListEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		tokens + [tokenizer.unk_token]

       A:		str        =		[1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_  )				,     SCREAMING_SNAKE_CASE_  )

       A:		Any        =		tokenizer.decode(SCREAMING_SNAKE_CASE_  )
       self.assertEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )






