
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 (      lowercase					, lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		def get_masked_lm_array(lowercase				):
				UpperCamelCase						=  f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				UpperCamelCase						=  tf.train.load_variable(lowercase					, lowercase				)

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

				return torch.from_numpy(lowercase				)

		def get_encoder_array(lowercase				):
				UpperCamelCase						=  f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				UpperCamelCase						=  tf.train.load_variable(lowercase					, lowercase				)

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

				return torch.from_numpy(lowercase				)

		def get_encoder_layer_array(lowercase					, lowercase				):
				UpperCamelCase						=  f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				UpperCamelCase						=  tf.train.load_variable(lowercase					, lowercase				)

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

				return torch.from_numpy(lowercase				)

		def get_encoder_attention_layer_array(lowercase					, lowercase					, lowercase				):
				UpperCamelCase						=  f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				UpperCamelCase						=  tf.train.load_variable(lowercase					, lowercase				)
				UpperCamelCase						=  array.reshape(lowercase				)

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

				return torch.from_numpy(lowercase				)

		print(f'''Loading model based on config from {config_path}...'''				)
		UpperCamelCase						=  BertConfig.from_json_file(lowercase				)
		UpperCamelCase						=  BertForMaskedLM(lowercase				)

		# 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(
				    lowercase					, '_query_dense/kernel'					, self_attn.query.weight.data.shape				)
				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_query_dense/bias'					, self_attn.query.bias.data.shape				)
				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_key_dense/kernel'					, self_attn.key.weight.data.shape				)
				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_key_dense/bias'					, self_attn.key.bias.data.shape				)
				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_value_dense/kernel'					, self_attn.value.weight.data.shape				)
				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_value_dense/bias'					, self_attn.value.bias.data.shape				)

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

				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_output_dense/kernel'					, self_output.dense.weight.data.shape				)
				UpperCamelCase						=  get_encoder_attention_layer_array(
				    lowercase					, '_output_dense/bias'					, self_output.dense.bias.data.shape				)

				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_attention_layer_norm/gamma'				)
				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_attention_layer_norm/beta'				)

				# Intermediate
				UpperCamelCase						=  layer.intermediate

				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_intermediate_dense/kernel'				)
				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_intermediate_dense/bias'				)

				# Output
				UpperCamelCase						=  layer.output

				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_output_dense/kernel'				)
				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_output_dense/bias'				)

				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_output_layer_norm/gamma'				)
				UpperCamelCase						=  get_encoder_layer_array(lowercase					, '_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=lowercase				)
		UpperCamelCase						=  get_encoder_array('_pooler_layer/kernel'				)
		UpperCamelCase						=  get_encoder_array('_pooler_layer/bias'				)

		# Export final model
		model.save_pretrained(lowercase				)

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

		print('Model conversion was done sucessfully!'				)


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


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		if a < 0 or b < 0:
				raise ValueError('the value of both inputs must be positive'				)

		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"
		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"

		UpperCamelCase						=  max(len(lowercase				)					, len(lowercase				)				)

		return "0b" + "".join(
		    str(int(char_a != char_b				)				)
		    for char_a, char_b in zip(a_binary.zfill(lowercase				)					, b_binary.zfill(lowercase				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
