


'''simple docstring'''







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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """simple docstring"""




     def get_masked_lm_array(__SCREAMING_SNAKE_CASE		):
          __a       =   F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

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

          return torch.from_numpy(__SCREAMING_SNAKE_CASE		)

     def get_encoder_array(__SCREAMING_SNAKE_CASE		):
          __a       =   F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

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

          return torch.from_numpy(__SCREAMING_SNAKE_CASE		)

     def get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
          __a       =   F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

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

          return torch.from_numpy(__SCREAMING_SNAKE_CASE		)

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

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

          return torch.from_numpy(__SCREAMING_SNAKE_CASE		)

     print(F'''Loading model based on config from {config_path}...'''		)
     __a       =   BertConfig.from_json_file(__SCREAMING_SNAKE_CASE		)
     __a       =   BertForMaskedLM(__SCREAMING_SNAKE_CASE		)

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

          # Self-attention
          __a       =   layer.attention.self

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

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

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

          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_attention_layer_norm/gamma"""		)
          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_attention_layer_norm/beta"""		)

          # Intermediate
          __a       =   layer.intermediate

          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_intermediate_dense/kernel"""		)
          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_intermediate_dense/bias"""		)

          # Output
          __a       =   layer.output

          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_output_dense/kernel"""		)
          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_output_dense/bias"""		)

          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_output_layer_norm/gamma"""		)
          __a       =   get_encoder_layer_array(__SCREAMING_SNAKE_CASE				,  """_output_layer_norm/beta"""		)

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

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

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

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

     __a       =   get_masked_lm_array("""embedding_table"""		)

     # Pooling
     __a       =   BertPooler(config=__SCREAMING_SNAKE_CASE		)
     __a       =   get_encoder_array("""_pooler_layer/kernel"""		)
     __a       =   get_encoder_array("""_pooler_layer/bias"""		)

     # Export final model
     model.save_pretrained(__SCREAMING_SNAKE_CASE		)

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			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.',
 )
 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










'''simple docstring'''







import enum
import shutil
import sys


SCREAMING_SNAKE_CASE_      ,     SCREAMING_SNAKE_CASE_      =			shutil.get_terminal_size()

SCREAMING_SNAKE_CASE_      =			{'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}


class  lowerCAmelCase_						(       enum.Enum      ):





  """simple docstring"""



  a_       :List[Any]												=0
  a_       :Optional[Any]												=1




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=""		)				->					List[Any]:
     """simple docstring"""




     sys.stdout.write(str(__SCREAMING_SNAKE_CASE		) + end		)
     sys.stdout.flush()




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=""		)				->					Tuple:
     """simple docstring"""




     forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      )				->					Union[str, Any]:
     """simple docstring"""




     forceWrite("""\r"""		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''		)




def        __lowercase						(      )				->					int:
     """simple docstring"""




     forceWrite(""" """ * TERMINAL_WIDTH		)
     reset_cursor()




def        __lowercase						(      )				->					str:
     """simple docstring"""




     reset_cursor()
     forceWrite("""-""" * TERMINAL_WIDTH		)






