


"""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       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     def get_masked_lm_array(_lowerCAmelCase     :					str       ):
          _a    	=				f'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
          _a    	=				tf.train.load_variable(_lowerCAmelCase,     _lowerCAmelCase       )

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

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

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

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_attention_layer_norm/gamma'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_attention_layer_norm/beta'''       )

          # Intermediate
          _a    	=				layer.intermediate

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_intermediate_dense/kernel'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_intermediate_dense/bias'''       )

          # Output
          _a    	=				layer.output

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_dense/kernel'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_dense/bias'''       )

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_layer_norm/gamma'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_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=_lowerCAmelCase       )
     _a    	=				get_encoder_array('''_pooler_layer/kernel'''       )
     _a    	=				get_encoder_array('''_pooler_layer/bias'''       )

     # Export final model
     model.save_pretrained(_lowerCAmelCase       )

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

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


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



"""simple docstring"""






from typing import Any
def       A_   (   _lowerCAmelCase     :					list       ):






     """simple docstring"""

     if not input_list:
          return []
     _a    	=				[input_list.count(_lowerCAmelCase       ) for value in input_list]
     _a    	=				max(_lowerCAmelCase       )  # Gets the maximum count in the input list.
     # Gets values of modes
     return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase       ) if value == y}       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()