
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						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  def get_masked_lm_array(lowercase_ ):
    lowercase__	: Dict     		=   F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase__	: Union[str, Any]     		=   tf.train.load_variable(lowerCAmelCase_			,			lowerCAmelCase_ )

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

    return torch.from_numpy(lowerCAmelCase_ )

  def get_encoder_array(lowercase_ ):
    lowercase__	: Optional[Any]     		=   F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase__	: int     		=   tf.train.load_variable(lowerCAmelCase_			,			lowerCAmelCase_ )

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

    return torch.from_numpy(lowerCAmelCase_ )

  def get_encoder_layer_array(lowercase_			,			lowercase_ ):
    lowercase__	: int     		=   F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase__	: str     		=   tf.train.load_variable(lowerCAmelCase_			,			lowerCAmelCase_ )

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

    return torch.from_numpy(lowerCAmelCase_ )

  def get_encoder_attention_layer_array(lowercase_			,			lowercase_			,			lowercase_ ):
    lowercase__	: Dict     		=   F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase__	: Optional[int]     		=   tf.train.load_variable(lowerCAmelCase_			,			lowerCAmelCase_ )
    lowercase__	: Optional[int]     		=   array.reshape(lowerCAmelCase_ )

    if "kernel" in name:
      lowercase__	: List[str]     		=   array.transpose()

    return torch.from_numpy(lowerCAmelCase_ )

  print(F'Loading model based on config from {config_path}...' )
  lowercase__	: Any     		=   BertConfig.from_json_file(lowerCAmelCase_ )
  lowercase__	: Dict     		=   BertForMaskedLM(lowerCAmelCase_ )

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

    # Self-attention
    lowercase__	: int     		=   layer.attention.self

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

    # Self-attention Output
    lowercase__	: int     		=   layer.attention.output

    lowercase__	: Union[str, Any]     		=   get_encoder_attention_layer_array(
        lowerCAmelCase_			,			"""_output_dense/kernel"""			,			self_output.dense.weight.data.shape )
    lowercase__	: str     		=   get_encoder_attention_layer_array(
        lowerCAmelCase_			,			"""_output_dense/bias"""			,			self_output.dense.bias.data.shape )

    lowercase__	: Union[str, Any]     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_attention_layer_norm/gamma""" )
    lowercase__	: Union[str, Any]     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_attention_layer_norm/beta""" )

    # Intermediate
    lowercase__	: List[Any]     		=   layer.intermediate

    lowercase__	: List[Any]     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_intermediate_dense/kernel""" )
    lowercase__	: int     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_intermediate_dense/bias""" )

    # Output
    lowercase__	: Tuple     		=   layer.output

    lowercase__	: Optional[int]     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_output_dense/kernel""" )
    lowercase__	: Dict     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_output_dense/bias""" )

    lowercase__	: List[Any]     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_output_layer_norm/gamma""" )
    lowercase__	: List[Any]     		=   get_encoder_layer_array(lowerCAmelCase_			,			"""_output_layer_norm/beta""" )

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

  # LM Head
  lowercase__	: int     		=   model.cls.predictions.transform

  lowercase__	: List[str]     		=   get_masked_lm_array("""dense/kernel""" )
  lowercase__	: Tuple     		=   get_masked_lm_array("""dense/bias""" )

  lowercase__	: Optional[int]     		=   get_masked_lm_array("""layer_norm/gamma""" )
  lowercase__	: List[str]     		=   get_masked_lm_array("""layer_norm/beta""" )

  lowercase__	: str     		=   get_masked_lm_array("""embedding_table""" )

  # Pooling
  lowercase__	: int     		=   BertPooler(config=lowerCAmelCase_ )
  lowercase__	: List[str]     		=   get_encoder_array("""_pooler_layer/kernel""" )
  lowercase__	: Tuple     		=   get_encoder_array("""_pooler_layer/bias""" )

  # Export final model
  model.save_pretrained(lowerCAmelCase_ )

  # Integration test - should load without any errors ;)
  lowercase__	: Dict     		=   BertForMaskedLM.from_pretrained(lowerCAmelCase_ )
  print(new_model.eval() )

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


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











"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case   					=				{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case   					=				{
    '''junnyu/roformer_chinese_small''': 1_5_3_6,
    '''junnyu/roformer_chinese_base''': 1_5_3_6,
    '''junnyu/roformer_chinese_char_small''': 5_1_2,
    '''junnyu/roformer_chinese_char_base''': 5_1_2,
    '''junnyu/roformer_small_discriminator''': 1_2_8,
    '''junnyu/roformer_small_generator''': 1_2_8,
}


snake_case   					=				{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  VOCAB_FILES_NAMES
   A__		:	Tuple           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	List[Any]           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Dict           =  PRETRAINED_INIT_CONFIGURATION
   A__		:	Any           =  RoFormerTokenizer



   def __init__(       self    :	Optional[int]							,       __lowerCamelCase    :	Any=None							,       __lowerCamelCase    :	Optional[Any]=None							,       __lowerCamelCase    :	int=True							,       __lowerCamelCase    :	Any="[UNK]"							,       __lowerCamelCase    :	Tuple="[SEP]"							,       __lowerCamelCase    :	Optional[Any]="[PAD]"							,       __lowerCamelCase    :	Union[str, Any]="[CLS]"							,       __lowerCamelCase    :	int="[MASK]"							,       __lowerCamelCase    :	str=True							,       __lowerCamelCase    :	List[Any]=None							,       **__lowerCamelCase    :	Any							,       ):



     """simple docstring"""
     super().__init__(
         __lowerCamelCase							,       tokenizer_file=__lowerCamelCase							,       do_lower_case=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       sep_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       cls_token=__lowerCamelCase							,       mask_token=__lowerCamelCase							,       tokenize_chinese_chars=__lowerCamelCase							,       strip_accents=__lowerCamelCase							,       **__lowerCamelCase							,       )

     _snake_case   	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
     if (
         pre_tok_state.get('''lowercase'''							,       __lowerCamelCase				) != do_lower_case
         or pre_tok_state.get('''strip_accents'''							,       __lowerCamelCase				) != strip_accents
     ):
       _snake_case   	=      getattr(__lowerCamelCase							,       pre_tok_state.pop('''type'''				)				)
       _snake_case   	=      do_lower_case
       _snake_case   	=      strip_accents
       _snake_case   	=      pre_tok_class(**__lowerCamelCase				)

     _snake_case   	=      do_lower_case



   def __getstate__(       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.__dict__.copy()
     _snake_case   	=      BertPreTokenizer()
     return state



   def __setstate__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      d
     _snake_case   	=      self.__dict__['''_tokenizer'''].get_vocab()
     _snake_case   	=      PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	List[Any]=None				):



     """simple docstring"""
     _snake_case   	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [self.sep_token_id]
     _snake_case   	=      [self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep				) * [0]
     return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     _snake_case   	=      self._tokenizer.model.save(__lowerCamelCase							,       name=__lowerCamelCase				)
     return tuple(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	List[str]=False							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      BertPreTokenizer()
     return super().save_pretrained(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       **__lowerCamelCase				)





