





'''simple docstring'''






from ... import PretrainedConfig


A__    :       Tuple              ={
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Optional[int]											=       NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
      _lowercase:      int											=       '''nezha'''




      def __init__(      self      :			str      ,      __snake_case      :			Union[str, Any]=2_11_28      ,      __snake_case      :			Any=7_68      ,      __snake_case      :			Union[str, Any]=12      ,      __snake_case      :			Union[str, Any]=12      ,      __snake_case      :			int=30_72      ,      __snake_case      :			List[Any]="gelu"      ,      __snake_case      :			Any=0.1      ,      __snake_case      :			Union[str, Any]=0.1      ,      __snake_case      :			Dict=5_12      ,      __snake_case      :			Tuple=64      ,      __snake_case      :			Tuple=2      ,      __snake_case      :			Dict=0.02      ,      __snake_case      :			Optional[int]=1E-1_2      ,      __snake_case      :			Tuple=0.1      ,      __snake_case      :			int=0      ,      __snake_case      :			int=2      ,      __snake_case      :			Optional[Any]=3      ,      __snake_case      :			Tuple=True      ,      **__snake_case      :			Optional[int]      ,      )    ->				Union[str, Any]:
            super().__init__(pad_token_id=__snake_case      ,      bos_token_id=__snake_case      ,      eos_token_id=__snake_case      ,      **__snake_case  )

            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							hidden_act
            _lowerCAmelCase												=							intermediate_size
            _lowerCAmelCase												=							hidden_dropout_prob
            _lowerCAmelCase												=							attention_probs_dropout_prob
            _lowerCAmelCase												=							max_position_embeddings
            _lowerCAmelCase												=							max_relative_position
            _lowerCAmelCase												=							type_vocab_size
            _lowerCAmelCase												=							initializer_range
            _lowerCAmelCase												=							layer_norm_eps
            _lowerCAmelCase												=							classifier_dropout
            _lowerCAmelCase												=							use_cache













'''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							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_layer_array(lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_attention_layer_array(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )
            _lowerCAmelCase												=							array.reshape(lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

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

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

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

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

      # Embeddings
      _lowerCAmelCase												=							get_encoder_array("""_position_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_type_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/gamma"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/beta"""  )

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

      _lowerCAmelCase												=							get_masked_lm_array("""dense/kernel"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""dense/bias"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/gamma"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/beta"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

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

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


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






