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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[Any]:
				def get_masked_lm_array(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
								SCREAMING_SNAKE_CASE__		  =				tf.train.load_variable(_snake_case  ,					_snake_case       )

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

								return torch.from_numpy(_snake_case       )

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

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

								return torch.from_numpy(_snake_case       )

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

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

								return torch.from_numpy(_snake_case       )

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

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

								return torch.from_numpy(_snake_case       )

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

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

								# Self-attention
								SCREAMING_SNAKE_CASE__		  =				layer.attention.self

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

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

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

								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_attention_layer_norm/gamma"       )
								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_attention_layer_norm/beta"       )

								# Intermediate
								SCREAMING_SNAKE_CASE__		  =				layer.intermediate

								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_intermediate_dense/kernel"       )
								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_intermediate_dense/bias"       )

								# Output
								SCREAMING_SNAKE_CASE__		  =				layer.output

								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_output_dense/kernel"       )
								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_output_dense/bias"       )

								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_output_layer_norm/gamma"       )
								SCREAMING_SNAKE_CASE__		  =				get_encoder_layer_array(_snake_case  ,					"_output_layer_norm/beta"       )

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

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

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

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

				SCREAMING_SNAKE_CASE__		  =				get_masked_lm_array("embedding_table"       )

				# Pooling
				SCREAMING_SNAKE_CASE__		  =				BertPooler(config=_snake_case       )
				SCREAMING_SNAKE_CASE__		  =				get_encoder_array("_pooler_layer/kernel"       )
				SCREAMING_SNAKE_CASE__		  =				get_encoder_array("_pooler_layer/bias"       )

				# Export final model
				model.save_pretrained(_snake_case       )

				# Integration test - should load without any errors ;)
				SCREAMING_SNAKE_CASE__		  =				BertForMaskedLM.from_pretrained(_snake_case       )
				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)





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




