

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__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str      )  ->						List[Any]:
  def get_masked_lm_array(UpperCAmelCase							:      str      ):
    UpperCAmelCase	:							Union[str, Any]						=			f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    UpperCAmelCase	:							Optional[int]						=			tf.train.load_variable(UpperCAmelCase    ,			UpperCAmelCase      )

    if "kernel" in name:
      UpperCAmelCase	:							List[Any]						=			array.transpose()

    return torch.from_numpy(UpperCAmelCase      )

  def get_encoder_array(UpperCAmelCase							:      str      ):
    UpperCAmelCase	:							List[str]						=			f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    UpperCAmelCase	:							str						=			tf.train.load_variable(UpperCAmelCase    ,			UpperCAmelCase      )

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

    return torch.from_numpy(UpperCAmelCase      )

  def get_encoder_layer_array(UpperCAmelCase							:      int    ,			UpperCAmelCase							:      str      ):
    UpperCAmelCase	:							Optional[Any]						=			f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    UpperCAmelCase	:							Optional[Any]						=			tf.train.load_variable(UpperCAmelCase    ,			UpperCAmelCase      )

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

    return torch.from_numpy(UpperCAmelCase      )

  def get_encoder_attention_layer_array(UpperCAmelCase							:      int    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Any      ):
    UpperCAmelCase	:							List[str]						=			f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    UpperCAmelCase	:							int						=			tf.train.load_variable(UpperCAmelCase    ,			UpperCAmelCase      )
    UpperCAmelCase	:							Optional[Any]						=			array.reshape(UpperCAmelCase      )

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

    return torch.from_numpy(UpperCAmelCase      )

  print(f'''Loading model based on config from {config_path}...'''      )
  UpperCAmelCase	:							Any						=			BertConfig.from_json_file(UpperCAmelCase      )
  UpperCAmelCase	:							int						=			BertForMaskedLM(UpperCAmelCase      )

  # 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	:							int						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_query_dense/kernel'''    ,			self_attn.query.weight.data.shape      )
    UpperCAmelCase	:							Dict						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_query_dense/bias'''    ,			self_attn.query.bias.data.shape      )
    UpperCAmelCase	:							Any						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_key_dense/kernel'''    ,			self_attn.key.weight.data.shape      )
    UpperCAmelCase	:							List[str]						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_key_dense/bias'''    ,			self_attn.key.bias.data.shape      )
    UpperCAmelCase	:							Dict						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_value_dense/kernel'''    ,			self_attn.value.weight.data.shape      )
    UpperCAmelCase	:							Any						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_value_dense/bias'''    ,			self_attn.value.bias.data.shape      )

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

    UpperCAmelCase	:							Optional[int]						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_output_dense/kernel'''    ,			self_output.dense.weight.data.shape      )
    UpperCAmelCase	:							Tuple						=			get_encoder_attention_layer_array(
        UpperCAmelCase    ,			'''_output_dense/bias'''    ,			self_output.dense.bias.data.shape      )

    UpperCAmelCase	:							int						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_attention_layer_norm/gamma'''      )
    UpperCAmelCase	:							Any						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_attention_layer_norm/beta'''      )

    # Intermediate
    UpperCAmelCase	:							BertIntermediate						=			layer.intermediate

    UpperCAmelCase	:							Union[str, Any]						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_intermediate_dense/kernel'''      )
    UpperCAmelCase	:							Dict						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_intermediate_dense/bias'''      )

    # Output
    UpperCAmelCase	:							BertOutput						=			layer.output

    UpperCAmelCase	:							Any						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_output_dense/kernel'''      )
    UpperCAmelCase	:							List[str]						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_output_dense/bias'''      )

    UpperCAmelCase	:							List[Any]						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_output_layer_norm/gamma'''      )
    UpperCAmelCase	:							Any						=			get_encoder_layer_array(UpperCAmelCase    ,			'''_output_layer_norm/beta'''      )

  # Embeddings
  UpperCAmelCase	:							Optional[Any]						=			get_encoder_array('''_position_embedding_layer/embeddings'''      )
  UpperCAmelCase	:							List[Any]						=			get_encoder_array('''_type_embedding_layer/embeddings'''      )
  UpperCAmelCase	:							Any						=			get_encoder_array('''_embedding_norm_layer/gamma'''      )
  UpperCAmelCase	:							Optional[Any]						=			get_encoder_array('''_embedding_norm_layer/beta'''      )

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

  UpperCAmelCase	:							Any						=			get_masked_lm_array('''dense/kernel'''      )
  UpperCAmelCase	:							List[Any]						=			get_masked_lm_array('''dense/bias'''      )

  UpperCAmelCase	:							Dict						=			get_masked_lm_array('''layer_norm/gamma'''      )
  UpperCAmelCase	:							Any						=			get_masked_lm_array('''layer_norm/beta'''      )

  UpperCAmelCase	:							List[Any]						=			get_masked_lm_array('''embedding_table'''      )

  # Pooling
  UpperCAmelCase	:							str						=			BertPooler(config=UpperCAmelCase      )
  UpperCAmelCase	:							BertPooler						=			get_encoder_array('''_pooler_layer/kernel'''      )
  UpperCAmelCase	:							BertPooler						=			get_encoder_array('''_pooler_layer/bias'''      )

  # Export final model
  model.save_pretrained(UpperCAmelCase      )

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

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


if __name__ == "__main__":
     _lowerCamelCase    :       Tuple        =							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[int]        =							parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int      )  ->						str:
  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''      )
  UpperCAmelCase	:							List[Any]						=			str(bin(UpperCAmelCase      )      )[2:]  # remove the leading "0b"
  UpperCAmelCase	:							List[str]						=			str(bin(UpperCAmelCase      )      )[2:]
  UpperCAmelCase	:							Optional[Any]						=			max(len(UpperCAmelCase      )    ,			len(UpperCAmelCase      )      )
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)      )      )
      for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase      )    ,			b_binary.zfill(UpperCAmelCase      )      )      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





