

'''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        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str							, _UpperCamelCase						:     str				)      ->			Union[str, Any]:
     """simple docstring"""




     def get_masked_lm_array(_UpperCamelCase						:     str				):
          _SCREAMING_SNAKE_CASE							    =f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          _SCREAMING_SNAKE_CASE							    =tf.train.load_variable(__lowerCamelCase							, __lowerCamelCase				)

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

          return torch.from_numpy(__lowerCamelCase				)

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

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

          return torch.from_numpy(__lowerCamelCase				)

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

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

          return torch.from_numpy(__lowerCamelCase				)

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

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

          return torch.from_numpy(__lowerCamelCase				)

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

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

          # Self-attention
          _SCREAMING_SNAKE_CASE							    =layer.attention.self

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

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

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

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

          # Intermediate
          _SCREAMING_SNAKE_CASE							    =layer.intermediate

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

          # Output
          _SCREAMING_SNAKE_CASE							    =layer.output

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

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

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

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

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

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

     _SCREAMING_SNAKE_CASE							    =get_masked_lm_array('embedding_table'				)

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

     # Export final model
     model.save_pretrained(__lowerCamelCase				)

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

     print('Model conversion was done sucessfully!'				)


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


"""simple docstring"""



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser(
     description=(
         'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
         ' Distillation'
     )
 )
 parser.add_argument('--model_type', default='bert', choices=['bert'])
 parser.add_argument('--model_name', default='bert-base-uncased', type=str)
 parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
 parser.add_argument('--vocab_transform', action='store_true')
 UpperCAmelCase__          = parser.parse_args()

 if args.model_type == "bert":
  UpperCAmelCase__          = BertForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase__          = 'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 UpperCAmelCase__          = model.state_dict()
 UpperCAmelCase__          = {}

 for w in ["word_embeddings", "position_embeddings"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.{w}.weight"]
 for w in ["weight", "bias"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

 UpperCAmelCase__          = 0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
   ]
  std_idx += 1

 UpperCAmelCase__          = state_dict['cls.predictions.decoder.weight']
 UpperCAmelCase__          = state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.dense.{w}"]
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

 print(F"N layers selected for distillation: {std_idx}")
 print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

 print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
 torch.save(compressed_sd, args.dump_checkpoint)



