'''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	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[Any]:





  """simple docstring"""


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

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

    return torch.from_numpy(lowerCamelCase__						)

  def get_encoder_array(lowerCamelCase__						):
    __UpperCAmelCase  :				Any       				=		f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase  :				Tuple       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)

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

    return torch.from_numpy(lowerCamelCase__						)

  def get_encoder_layer_array(lowerCamelCase__							,    lowerCamelCase__						):
    __UpperCAmelCase  :				List[Any]       				=		f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase  :				Union[str, Any]       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)

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

    return torch.from_numpy(lowerCamelCase__						)

  def get_encoder_attention_layer_array(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						):
    __UpperCAmelCase  :				str       				=		f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    __UpperCAmelCase  :				Optional[Any]       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)
    __UpperCAmelCase  :				Tuple       				=		array.reshape(lowerCamelCase__						)

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

    return torch.from_numpy(lowerCamelCase__						)

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

  # 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  :				Union[str, Any]       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_query_dense/kernel"							,    self_attn.query.weight.data.shape						)
    __UpperCAmelCase  :				Dict       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_query_dense/bias"							,    self_attn.query.bias.data.shape						)
    __UpperCAmelCase  :				Tuple       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_key_dense/kernel"							,    self_attn.key.weight.data.shape						)
    __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_key_dense/bias"							,    self_attn.key.bias.data.shape						)
    __UpperCAmelCase  :				List[str]       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_value_dense/kernel"							,    self_attn.value.weight.data.shape						)
    __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_value_dense/bias"							,    self_attn.value.bias.data.shape						)

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

    __UpperCAmelCase  :				Any       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_output_dense/kernel"							,    self_output.dense.weight.data.shape						)
    __UpperCAmelCase  :				str       				=		get_encoder_attention_layer_array(
        lowerCamelCase__							,    "_output_dense/bias"							,    self_output.dense.bias.data.shape						)

    __UpperCAmelCase  :				List[str]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_attention_layer_norm/gamma"						)
    __UpperCAmelCase  :				Tuple       				=		get_encoder_layer_array(lowerCamelCase__							,    "_attention_layer_norm/beta"						)

    # Intermediate
    __UpperCAmelCase  :				BertIntermediate       				=		layer.intermediate

    __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_intermediate_dense/kernel"						)
    __UpperCAmelCase  :				Any       				=		get_encoder_layer_array(lowerCamelCase__							,    "_intermediate_dense/bias"						)

    # Output
    __UpperCAmelCase  :				BertOutput       				=		layer.output

    __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_dense/kernel"						)
    __UpperCAmelCase  :				Optional[int]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_dense/bias"						)

    __UpperCAmelCase  :				List[str]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_layer_norm/gamma"						)
    __UpperCAmelCase  :				List[str]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_layer_norm/beta"						)

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

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

  __UpperCAmelCase  :				List[Any]       				=		get_masked_lm_array("dense/kernel"						)
  __UpperCAmelCase  :				Optional[Any]       				=		get_masked_lm_array("dense/bias"						)

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

  __UpperCAmelCase  :				List[str]       				=		get_masked_lm_array("embedding_table"						)

  # Pooling
  __UpperCAmelCase  :				Union[str, Any]       				=		BertPooler(config=lowerCamelCase__						)
  __UpperCAmelCase  :				BertPooler       				=		get_encoder_array("_pooler_layer/kernel"						)
  __UpperCAmelCase  :				BertPooler       				=		get_encoder_array("_pooler_layer/bias"						)

  # Export final model
  model.save_pretrained(lowerCamelCase__						)

  # Integration test - should load without any errors ;)
  __UpperCAmelCase  :				Optional[int]       				=		BertForMaskedLM.from_pretrained(lowerCamelCase__						)
  print(new_model.eval()						)

  print("Model conversion was done sucessfully!"						)


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







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






