
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__		):
      def get_masked_lm_array(UpperCamelCase__		):
            UpperCamelCase__     : str											=					f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
            UpperCamelCase__     : Optional[int]											=					tf.train.load_variable(UpperCamelCase__			,		UpperCamelCase__		)

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

            return torch.from_numpy(UpperCamelCase__		)

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

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

            return torch.from_numpy(UpperCamelCase__		)

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

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

            return torch.from_numpy(UpperCamelCase__		)

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

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

            return torch.from_numpy(UpperCamelCase__		)

      print(f'''Loading model based on config from {config_path}...'''		)
      UpperCamelCase__     : List[str]											=					BertConfig.from_json_file(UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					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__     : Dict											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_query_dense/kernel'''			,		self_attn.query.weight.data.shape		)
            UpperCamelCase__     : Any											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_query_dense/bias'''			,		self_attn.query.bias.data.shape		)
            UpperCamelCase__     : str											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_key_dense/kernel'''			,		self_attn.key.weight.data.shape		)
            UpperCamelCase__     : List[Any]											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_key_dense/bias'''			,		self_attn.key.bias.data.shape		)
            UpperCamelCase__     : Any											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_value_dense/kernel'''			,		self_attn.value.weight.data.shape		)
            UpperCamelCase__     : Union[str, 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__     : str											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_output_dense/kernel'''			,		self_output.dense.weight.data.shape		)
            UpperCamelCase__     : Any											=					get_encoder_attention_layer_array(
                UpperCamelCase__			,		'''_output_dense/bias'''			,		self_output.dense.bias.data.shape		)

            UpperCamelCase__     : str											=					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__     : Tuple											=					get_encoder_layer_array(UpperCamelCase__			,		'''_intermediate_dense/kernel'''		)
            UpperCamelCase__     : Dict											=					get_encoder_layer_array(UpperCamelCase__			,		'''_intermediate_dense/bias'''		)

            # Output
            UpperCamelCase__     : BertOutput											=					layer.output

            UpperCamelCase__     : List[Any]											=					get_encoder_layer_array(UpperCamelCase__			,		'''_output_dense/kernel'''		)
            UpperCamelCase__     : int											=					get_encoder_layer_array(UpperCamelCase__			,		'''_output_dense/bias'''		)

            UpperCamelCase__     : Dict											=					get_encoder_layer_array(UpperCamelCase__			,		'''_output_layer_norm/gamma'''		)
            UpperCamelCase__     : Tuple											=					get_encoder_layer_array(UpperCamelCase__			,		'''_output_layer_norm/beta'''		)

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

      # LM Head
      UpperCamelCase__     : List[Any]											=					model.cls.predictions.transform

      UpperCamelCase__     : Union[str, Any]											=					get_masked_lm_array('''dense/kernel'''		)
      UpperCamelCase__     : Union[str, Any]											=					get_masked_lm_array('''dense/bias'''		)

      UpperCamelCase__     : Optional[int]											=					get_masked_lm_array('''layer_norm/gamma'''		)
      UpperCamelCase__     : Tuple											=					get_masked_lm_array('''layer_norm/beta'''		)

      UpperCamelCase__     : Tuple											=					get_masked_lm_array('''embedding_table'''		)

      # Pooling
      UpperCamelCase__     : List[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  =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  =parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase  ={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

lowerCamelCase  =logging.WARNING


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					os.getenv('''DATASETS_VERBOSITY'''			,		UpperCamelCase__		)
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                      f'''has to be one of: { ", ".join(log_levels.keys()		) }'''		)
      return _default_log_level


def 				SCREAMING_SNAKE_CASE_   (      ):
      return __name__.split('''.'''		)[0]


def 				SCREAMING_SNAKE_CASE_   (      ):
      return logging.getLogger(_get_library_name()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      # Apply our default configuration to the library root logger.
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(_get_default_logging_level()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(logging.NOTSET		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = None		):
      if name is None:
            UpperCamelCase__     : Union[str, Any]											=					_get_library_name()
      return logging.getLogger(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return _get_library_root_logger().getEffectiveLevel()


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      _get_library_root_logger().setLevel(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : str											=					False


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> int:  # pylint: disable=unused-argument




           """simple docstring"""







           UpperCamelCase__     : Dict											=					args[0] if args else None






     def __iter__(				self	)	-> Dict:




           """simple docstring"""







           return iter(self._iterator	)






     def __getattr__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           def empty_fn(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	):  # pylint: disable=unused-argument
                 return

           return empty_fn






     def __enter__(				self	)	-> Optional[int]:




           """simple docstring"""







           return self






     def __exit__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           return
lowerCamelCase  =True




class 					_lowerCamelCase			:





     """simple docstring"""







     def __call__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if _tqdm_active and not disable:
                 return tqdm_lib.tqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           else:
                 return EmptyTqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					None
           if _tqdm_active:
                 return tqdm_lib.tqdm.set_lock(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           if _tqdm_active:
                 return tqdm_lib.tqdm.get_lock()
lowerCamelCase  =_tqdm_cls()


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      return bool(_tqdm_active		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Dict											=					True


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Union[str, Any]											=					False


