

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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	def get_masked_lm_array(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[str]									= f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		UpperCAmelCase__:	Union[str, Any]									= tf.train.load_variable(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

		if "kernel" in name:
			UpperCAmelCase__:	Optional[int]									= array.transpose()

		return torch.from_numpy(SCREAMING_SNAKE_CASE      )

	def get_encoder_array(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[str]									= f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		UpperCAmelCase__:	Any									= tf.train.load_variable(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

		if "kernel" in name:
			UpperCAmelCase__:	List[str]									= array.transpose()

		return torch.from_numpy(SCREAMING_SNAKE_CASE      )

	def get_encoder_layer_array(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[str]									= f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		UpperCAmelCase__:	Dict									= tf.train.load_variable(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

		if "kernel" in name:
			UpperCAmelCase__:	List[str]									= array.transpose()

		return torch.from_numpy(SCREAMING_SNAKE_CASE      )

	def get_encoder_attention_layer_array(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	str									= f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		UpperCAmelCase__:	int									= tf.train.load_variable(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Any									= array.reshape(SCREAMING_SNAKE_CASE      )

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

		return torch.from_numpy(SCREAMING_SNAKE_CASE      )

	print(f"Loading model based on config from {config_path}..."      )
	UpperCAmelCase__:	Optional[Any]									= BertConfig.from_json_file(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Union[str, Any]									= BertForMaskedLM(SCREAMING_SNAKE_CASE      )

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

		# Self-attention
		UpperCAmelCase__:	BertSelfAttention									= layer.attention.self

		UpperCAmelCase__:	str									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_query_dense/kernel"	,self_attn.query.weight.data.shape      )
		UpperCAmelCase__:	Optional[Any]									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_query_dense/bias"	,self_attn.query.bias.data.shape      )
		UpperCAmelCase__:	List[str]									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_key_dense/kernel"	,self_attn.key.weight.data.shape      )
		UpperCAmelCase__:	Optional[int]									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_key_dense/bias"	,self_attn.key.bias.data.shape      )
		UpperCAmelCase__:	List[str]									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_value_dense/kernel"	,self_attn.value.weight.data.shape      )
		UpperCAmelCase__:	str									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_value_dense/bias"	,self_attn.value.bias.data.shape      )

		# Self-attention Output
		UpperCAmelCase__:	BertSelfOutput									= layer.attention.output

		UpperCAmelCase__:	Dict									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_output_dense/kernel"	,self_output.dense.weight.data.shape      )
		UpperCAmelCase__:	List[str]									= get_encoder_attention_layer_array(
		    SCREAMING_SNAKE_CASE	,"_output_dense/bias"	,self_output.dense.bias.data.shape      )

		UpperCAmelCase__:	Union[str, Any]									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_attention_layer_norm/gamma"      )
		UpperCAmelCase__:	Optional[int]									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_attention_layer_norm/beta"      )

		# Intermediate
		UpperCAmelCase__:	BertIntermediate									= layer.intermediate

		UpperCAmelCase__:	Optional[int]									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_intermediate_dense/kernel"      )
		UpperCAmelCase__:	List[Any]									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_intermediate_dense/bias"      )

		# Output
		UpperCAmelCase__:	BertOutput									= layer.output

		UpperCAmelCase__:	Any									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_output_dense/kernel"      )
		UpperCAmelCase__:	Union[str, Any]									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_output_dense/bias"      )

		UpperCAmelCase__:	str									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_output_layer_norm/gamma"      )
		UpperCAmelCase__:	Union[str, Any]									= get_encoder_layer_array(SCREAMING_SNAKE_CASE	,"_output_layer_norm/beta"      )

	# Embeddings
	UpperCAmelCase__:	Tuple									= get_encoder_array("_position_embedding_layer/embeddings"      )
	UpperCAmelCase__:	List[Any]									= get_encoder_array("_type_embedding_layer/embeddings"      )
	UpperCAmelCase__:	Union[str, Any]									= get_encoder_array("_embedding_norm_layer/gamma"      )
	UpperCAmelCase__:	Optional[int]									= get_encoder_array("_embedding_norm_layer/beta"      )

	# LM Head
	UpperCAmelCase__:	int									= model.cls.predictions.transform

	UpperCAmelCase__:	str									= get_masked_lm_array("dense/kernel"      )
	UpperCAmelCase__:	Any									= get_masked_lm_array("dense/bias"      )

	UpperCAmelCase__:	Optional[int]									= get_masked_lm_array("layer_norm/gamma"      )
	UpperCAmelCase__:	Optional[int]									= get_masked_lm_array("layer_norm/beta"      )

	UpperCAmelCase__:	Union[str, Any]									= get_masked_lm_array("embedding_table"      )

	# Pooling
	UpperCAmelCase__:	Tuple									= BertPooler(config=SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	BertPooler									= get_encoder_array("_pooler_layer/kernel"      )
	UpperCAmelCase__:	BertPooler									= get_encoder_array("_pooler_layer/bias"      )

	# Export final model
	model.save_pretrained(SCREAMING_SNAKE_CASE      )

	# Integration test - should load without any errors ;)
	UpperCAmelCase__:	Union[str, Any]									= BertForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE      )
	print(new_model.eval()      )

	print("Model conversion was done sucessfully!"      )


if __name__ == "__main__":
					_lowerCAmelCase  :      Optional[Any]			 =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.""",
					)
					_lowerCAmelCase  :      Optional[Any]			 =parser.parse_args()
					convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= int(SCREAMING_SNAKE_CASE      )
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= divmod(SCREAMING_SNAKE_CASE	,2      )
	return binary_recursive(SCREAMING_SNAKE_CASE      ) + str(SCREAMING_SNAKE_CASE      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= str(SCREAMING_SNAKE_CASE      ).strip()
	if not number:
		raise ValueError("No input value was provided"      )
	UpperCAmelCase__:	Tuple									= "-" if number.startswith("-"      ) else ""
	UpperCAmelCase__:	Any									= number.lstrip("-"      )
	if not number.isnumeric():
		raise ValueError("Input value is not an integer"      )
	return f"{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE      )      )}"


if __name__ == "__main__":
					from doctest import testmod

					testmod()