

'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


lowerCAmelCase_     =      logging.get_logger(__name__)







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''vision-text-dual-encoder'''
				snake_case_							     = True

				def __init__(       self,						lowercase_=512,						lowercase_=2.6_592,						**lowercase_ )    ->							Tuple:
					super().__init__(**lowercase_ )

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

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

					snake_case							=					kwargs.pop('vision_config' )
					snake_case							=					kwargs.pop('text_config' )

					snake_case							=					vision_config.pop('model_type' )
					snake_case							=					text_config.pop('model_type' )

					if vision_model_type == "clip":
						snake_case							=					AutoConfig.for_model(lowercase_,						**lowercase_ ).vision_config
					elif vision_model_type == "clip_vision_model":
						snake_case							=					CLIPVisionConfig(**lowercase_ )
					else:
						snake_case							=					AutoConfig.for_model(lowercase_,						**lowercase_ )

					snake_case							=					AutoConfig.for_model(lowercase_,						**lowercase_ )

					snake_case							=					projection_dim
					snake_case							=					logit_scale_init_value

				@classmethod
				def 					_lowerCamelCase    (       cls,						lowercase_,						lowercase_,						**lowercase_ )    ->							Optional[int]:
					return cls(vision_config=vision_config.to_dict(),						text_config=text_config.to_dict(),						**lowercase_ )






				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					copy.deepcopy(self.__dict__ )
					snake_case							=					self.vision_config.to_dict()
					snake_case							=					self.text_config.to_dict()
					snake_case							=					self.__class__.model_type
					return output












import numpy as np
import requests


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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta - (alpha / n) * sum_grad
       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

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

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

       for i in range(0     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    run_steep_gradient_descent(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              _UpperCamelCase														=    sum_of_square_error(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              print(f'''At Iteration {i + 1} - Error is {error:.5f}'''       )

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

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

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


if __name__ == "__main__":
       main()






