
"""simple docstring"""





import copy
import os
from typing import Union

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bridgetower_vision_model'



		def __init__( self    ,		__a=7_68    ,		__a=12    ,		__a=3    ,		__a=16    ,		__a=2_88    ,		__a=1    ,		__a=1e-05    ,		__a=False    ,		__a=True    ,		__a=False    ,		**__a    ,		)	->	List[Any]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  initializer_factor
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  stop_gradient
							_UpperCamelCase										=  share_layernorm
							_UpperCamelCase										=  remove_last_layer




		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	"PretrainedConfig":






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  cls.get_config_dict(__a    ,		**__a)

							if config_dict.get('''model_type''') == "bridgetower":
												_UpperCamelCase										=  config_dict['''text_config''']

							if "model_type" in config_dict and hasattr(cls    ,		'''model_type''') and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
												    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

							return cls.from_dict(__a    ,		**__a)






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bridgetower_text_model'



		def __init__( self    ,		__a=5_02_65    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=1    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_14    ,		__a=1    ,		__a=1e-05    ,		__a=1    ,		__a=0    ,		__a=2    ,		__a="absolute"    ,		__a=True    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							super().__init__(**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  initializer_factor
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  pad_token_id
							_UpperCamelCase										=  bos_token_id
							_UpperCamelCase										=  eos_token_id




		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	"PretrainedConfig":






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  cls.get_config_dict(__a    ,		**__a)

							if config_dict.get('''model_type''') == "bridgetower":
												_UpperCamelCase										=  config_dict['''text_config''']

							if "model_type" in config_dict and hasattr(cls    ,		'''model_type''') and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
												    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

							return cls.from_dict(__a    ,		**__a)






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bridgetower'



		def __init__( self    ,		__a=True    ,		__a="gelu"    ,		__a=7_68    ,		__a=1    ,		__a=1e-05    ,		__a=False    ,		__a="add"    ,		__a=12    ,		__a=6    ,		__a=False    ,		__a=False    ,		__a=None    ,		__a=None    ,		**__a    ,		)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  kwargs.pop('''text_config_dict'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''vision_config_dict'''    ,		__a)

							super().__init__(**__a)
							_UpperCamelCase										=  share_cross_modal_transformer_layers
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  initializer_factor
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  share_link_tower_layers
							_UpperCamelCase										=  link_tower_type
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  tie_word_embeddings
							_UpperCamelCase										=  init_layernorm_from_vision_encoder

							if text_config is None:
												_UpperCamelCase										=  {}
												logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''')

							if vision_config is None:
												_UpperCamelCase										=  {}
												logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.''')

							_UpperCamelCase										=  BridgeTowerTextConfig(**__a)
							_UpperCamelCase										=  BridgeTowerVisionConfig(**__a)



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		__a    ,		**__a)	->	Union[str, Any]:






							'''simple docstring'''
							return cls(text_config=text_config.to_dict()    ,		vision_config=vision_config.to_dict()    ,		**__a)




		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							_UpperCamelCase										=  self.text_config.to_dict()
							_UpperCamelCase										=  self.vision_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output








"""simple docstring"""





import numpy






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							_UpperCamelCase										=  numpy.random.rand(
							    self.input_array.shape[1]    ,		4)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							_UpperCamelCase										=  numpy.random.rand(
							    4    ,		3)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							_UpperCamelCase										=  numpy.random.rand(3    ,		1)

							# Real output values provided.
							_UpperCamelCase										=  output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							_UpperCamelCase										=  numpy.zeros(output_array.shape)



		def 						UpperCAmelCase							( self)	->	numpy.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(self.input_array    ,		self.input_layer_and_first_hidden_layer_weights))

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer    ,		self.first_hidden_layer_and_second_hidden_layer_weights    ,		))

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,		self.second_hidden_layer_and_output_layer_weights    ,		))

							return self.layer_between_second_hidden_layer_and_output



		def 						UpperCAmelCase							( self)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T    ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output)    ,		)
							_UpperCamelCase										=  numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T    ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output)    ,		self.second_hidden_layer_and_output_layer_weights.T    ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,		)
							_UpperCamelCase										=  numpy.dot(
							    self.input_array.T    ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output)    ,		self.second_hidden_layer_and_output_layer_weights.T    ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,		self.first_hidden_layer_and_second_hidden_layer_weights.T    ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer)    ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							for iteration in range(1    ,		iterations + 1):
												_UpperCamelCase										=  self.feedforward()
												self.back_propagation()
												if give_loss:
																	_UpperCamelCase										=  numpy.mean(numpy.square(output - self.feedforward()))
																	print(F'''Iteration {iteration} Loss: {loss}''')




		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  input_arr

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(self.array    ,		self.input_layer_and_first_hidden_layer_weights))

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer    ,		self.first_hidden_layer_and_second_hidden_layer_weights    ,		))

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,		self.second_hidden_layer_and_output_layer_weights    ,		))

							return int(self.layer_between_second_hidden_layer_and_output > 0.6)





def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





					return 1 / (1 + numpy.exp(-value     ))



def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





					return (value) * (1 - (value))



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  numpy.array(
					    (
					        [0, 0, 0],
					        [0, 0, 1],
					        [0, 1, 0],
					        [0, 1, 1],
					        [1, 0, 0],
					        [1, 0, 1],
					        [1, 1, 0],
					        [1, 1, 1],
					    ),					dtype=numpy.floataa,					)

					# True output values for the given input values.
					_UpperCamelCase										=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]),					dtype=numpy.floataa     )

					# Calling neural network class.
					_UpperCamelCase										=  TwoHiddenLayerNeuralNetwork(
					    input_array=__snake_case,					output_array=__snake_case     )

					# Calling training function.
					# Set give_loss to True if you want to see loss in every iteration.
					neural_network.train(output=__snake_case,					iterations=10,					give_loss=__snake_case     )

					return neural_network.predict(numpy.array(([1, 1, 1]),					dtype=numpy.floataa     )     )


if __name__ == "__main__":
				example()






