
"""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 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       str		)     ->					Union[str, Any]:
  def get_masked_lm_array(__lowerCamelCase   :       str		):
    _snake_case					     =       f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    _snake_case					     =       tf.train.load_variable(__lowerCamelCase ,					__lowerCamelCase		)

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

    return torch.from_numpy(__lowerCamelCase		)

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

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

    return torch.from_numpy(__lowerCamelCase		)

  def get_encoder_layer_array(__lowerCamelCase   :       int ,					__lowerCamelCase   :       str		):
    _snake_case					     =       f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    _snake_case					     =       tf.train.load_variable(__lowerCamelCase ,					__lowerCamelCase		)

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

    return torch.from_numpy(__lowerCamelCase		)

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

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

    return torch.from_numpy(__lowerCamelCase		)

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

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

    # Self-attention
    _snake_case					     =       layer.attention.self

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

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

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

    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_attention_layer_norm/gamma'''		)
    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_attention_layer_norm/beta'''		)

    # Intermediate
    _snake_case					     =       layer.intermediate

    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_intermediate_dense/kernel'''		)
    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_intermediate_dense/bias'''		)

    # Output
    _snake_case					     =       layer.output

    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_output_dense/kernel'''		)
    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_output_dense/bias'''		)

    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_output_layer_norm/gamma'''		)
    _snake_case					     =       get_encoder_layer_array(__lowerCamelCase ,					'''_output_layer_norm/beta'''		)

  # Embeddings
  _snake_case					     =       get_encoder_array('''_position_embedding_layer/embeddings'''		)
  _snake_case					     =       get_encoder_array('''_type_embedding_layer/embeddings'''		)
  _snake_case					     =       get_encoder_array('''_embedding_norm_layer/gamma'''		)
  _snake_case					     =       get_encoder_array('''_embedding_norm_layer/beta'''		)

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

  _snake_case					     =       get_masked_lm_array('''dense/kernel'''		)
  _snake_case					     =       get_masked_lm_array('''dense/bias'''		)

  _snake_case					     =       get_masked_lm_array('''layer_norm/gamma'''		)
  _snake_case					     =       get_masked_lm_array('''layer_norm/beta'''		)

  _snake_case					     =       get_masked_lm_array('''embedding_table'''		)

  # Pooling
  _snake_case					     =       BertPooler(config=__lowerCamelCase		)
  _snake_case					     =       get_encoder_array('''_pooler_layer/kernel'''		)
  _snake_case					     =       get_encoder_array('''_pooler_layer/bias'''		)

  # Export final model
  model.save_pretrained(__lowerCamelCase		)

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

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


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





"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					"""masked_bert"""







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any=30522    ,      _lowerCamelCase			:			Union[str, Any]=768    ,      _lowerCamelCase			:			Tuple=12    ,      _lowerCamelCase			:			Any=12    ,      _lowerCamelCase			:			str=3072    ,      _lowerCamelCase			:			str="gelu"    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Dict=512    ,      _lowerCamelCase			:			List[Any]=2    ,      _lowerCamelCase			:			int=0.0_2    ,      _lowerCamelCase			:			Union[str, Any]=1e-12    ,      _lowerCamelCase			:			Union[str, Any]=0    ,      _lowerCamelCase			:			List[str]="topK"    ,      _lowerCamelCase			:			Optional[Any]="constant"    ,      _lowerCamelCase			:			Optional[Any]=0.0    ,      **_lowerCamelCase			:			str    ,      ):
       super().__init__(pad_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)

       _snake_case					     =       vocab_size
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       hidden_act
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       type_vocab_size
       _snake_case					     =       initializer_range
       _snake_case					     =       layer_norm_eps
       _snake_case					     =       pruning_method
       _snake_case					     =       mask_init
       _snake_case					     =       mask_scale



