



import numpy as np
import requests






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						requests.get(
     "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
     "master/Week1/ADRvsRating.csv"  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						response.text.splitlines()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for item in lines:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						item.split(","  )
  data.append(__lowerCamelCase  )
 data.pop(0  )  # This is for removing the labels from the list
 __SCREAMING_SNAKE_CASE							:     List[str]							=						np.matrix(__lowerCamelCase  )
 return dataset






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Any		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[Any]							=						len_data

 __SCREAMING_SNAKE_CASE							:     Dict							=						np.dot(__lowerCamelCase		,						data_x.transpose()  )
 prod -= data_y.transpose()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						np.dot(__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						theta - (alpha / n) * sum_grad
 return theta






def 						_lowerCAmelCase    (					__lowerCamelCase :  Tuple		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.dot(__lowerCamelCase		,						data_x.transpose()  )
 prod -= data_y.transpose()
 __SCREAMING_SNAKE_CASE							:     Dict							=						np.sum(np.square(__lowerCamelCase  )  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						sum_elem / (2 * len_data)
 return error






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  List[str]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[Any]							=						100000
 __SCREAMING_SNAKE_CASE							:     int							=						0.0001550

 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						data_x.shape[1]
 __SCREAMING_SNAKE_CASE							:     Any							=						data_x.shape[0] - 1

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

 for i in range(0		,						__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						run_steep_gradient_descent(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						sum_of_square_error(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
  print(F"""At Iteration {i + 1} - Error is {error:.5f}"""  )

 return theta






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  Optional[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						sum(abs(y - predicted_y[i]  ) for i, y in enumerate(__lowerCamelCase  )  )
 return total / len(__lowerCamelCase  )






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						collect_dataset()

 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						data.shape[0]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						np.c_[np.ones(__lowerCamelCase  ), data[:, :-1]].astype(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						data[:, -1].astype(__lowerCamelCase  )

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


if __name__ == "__main__":
      main()










import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

        if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''       )

        if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''       )

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






        return cls(text_config=text_config.to_dict()						,    vision_config=vision_config.to_dict()						,    **a__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






