


import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->str:

			def get_masked_lm_array(lowerCAmelCase_   ):
						UpperCAmelCase							=     F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						UpperCAmelCase							=     tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_   )

						if "kernel" in name:
									UpperCAmelCase							=     array.transpose()

						return torch.from_numpy(lowerCAmelCase_   )

			def get_encoder_array(lowerCAmelCase_   ):
						UpperCAmelCase							=     F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						UpperCAmelCase							=     tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_   )

						if "kernel" in name:
									UpperCAmelCase							=     array.transpose()

						return torch.from_numpy(lowerCAmelCase_   )

			def get_encoder_layer_array(lowerCAmelCase_  ,						lowerCAmelCase_   ):
						UpperCAmelCase							=     F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						UpperCAmelCase							=     tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_   )

						if "kernel" in name:
									UpperCAmelCase							=     array.transpose()

						return torch.from_numpy(lowerCAmelCase_   )

			def get_encoder_attention_layer_array(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):
						UpperCAmelCase							=     F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						UpperCAmelCase							=     tf.train.load_variable(lowerCAmelCase_  ,						lowerCAmelCase_   )
						UpperCAmelCase							=     array.reshape(lowerCAmelCase_   )

						if "kernel" in name:
									UpperCAmelCase							=     array.transpose()

						return torch.from_numpy(lowerCAmelCase_   )

			print(F"""Loading model based on config from {config_path}..."""   )
			UpperCAmelCase							=     BertConfig.from_json_file(lowerCAmelCase_   )
			UpperCAmelCase							=     BertForMaskedLM(lowerCAmelCase_   )

			# Layers
			for layer_index in range(0  ,						config.num_hidden_layers   ):
						UpperCAmelCase							=     model.bert.encoder.layer[layer_index]

						# Self-attention
						UpperCAmelCase							=     layer.attention.self

						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_query_dense/kernel"""  ,						self_attn.query.weight.data.shape   )
						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_query_dense/bias"""  ,						self_attn.query.bias.data.shape   )
						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_key_dense/kernel"""  ,						self_attn.key.weight.data.shape   )
						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_key_dense/bias"""  ,						self_attn.key.bias.data.shape   )
						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_value_dense/kernel"""  ,						self_attn.value.weight.data.shape   )
						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_value_dense/bias"""  ,						self_attn.value.bias.data.shape   )

						# Self-attention Output
						UpperCAmelCase							=     layer.attention.output

						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_output_dense/kernel"""  ,						self_output.dense.weight.data.shape   )
						UpperCAmelCase							=     get_encoder_attention_layer_array(
						    lowerCAmelCase_  ,						"""_output_dense/bias"""  ,						self_output.dense.bias.data.shape   )

						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_attention_layer_norm/gamma"""   )
						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_attention_layer_norm/beta"""   )

						# Intermediate
						UpperCAmelCase							=     layer.intermediate

						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_intermediate_dense/kernel"""   )
						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_intermediate_dense/bias"""   )

						# Output
						UpperCAmelCase							=     layer.output

						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_output_dense/kernel"""   )
						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_output_dense/bias"""   )

						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_output_layer_norm/gamma"""   )
						UpperCAmelCase							=     get_encoder_layer_array(lowerCAmelCase_  ,						"""_output_layer_norm/beta"""   )

			# Embeddings
			UpperCAmelCase							=     get_encoder_array("""_position_embedding_layer/embeddings"""   )
			UpperCAmelCase							=     get_encoder_array("""_type_embedding_layer/embeddings"""   )
			UpperCAmelCase							=     get_encoder_array("""_embedding_norm_layer/gamma"""   )
			UpperCAmelCase							=     get_encoder_array("""_embedding_norm_layer/beta"""   )

			# LM Head
			UpperCAmelCase							=     model.cls.predictions.transform

			UpperCAmelCase							=     get_masked_lm_array("""dense/kernel"""   )
			UpperCAmelCase							=     get_masked_lm_array("""dense/bias"""   )

			UpperCAmelCase							=     get_masked_lm_array("""layer_norm/gamma"""   )
			UpperCAmelCase							=     get_masked_lm_array("""layer_norm/beta"""   )

			UpperCAmelCase							=     get_masked_lm_array("""embedding_table"""   )

			# Pooling
			UpperCAmelCase							=     BertPooler(config=lowerCAmelCase_   )
			UpperCAmelCase							=     get_encoder_array("""_pooler_layer/kernel"""   )
			UpperCAmelCase							=     get_encoder_array("""_pooler_layer/bias"""   )

			# Export final model
			model.save_pretrained(lowerCAmelCase_   )

			# Integration test - should load without any errors ;)
			UpperCAmelCase							=     BertForMaskedLM.from_pretrained(lowerCAmelCase_   )
			print(new_model.eval()   )

			print("""Model conversion was done sucessfully!"""   )


if __name__ == "__main__":
	__a     =			argparse.ArgumentParser()
	parser.add_argument(
	    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
	)
	parser.add_argument(
	    """--bert_config_file""",
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""",
	    type=str,
	    required=True,
	    help="""Path to the output PyTorch model.""",
	)
	__a     =			parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   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        _lowercase				(		self						:					List[str]       )    ->   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        _lowercase				(		self						:					Optional[Any]       )    ->   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        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   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        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   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   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   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=lowerCAmelCase_  ,						output_array=lowerCAmelCase_   )

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=lowerCAmelCase_  ,						iterations=1_0  ,						give_loss=lowerCAmelCase_   )

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


if __name__ == "__main__":
 example()



