


"""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   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str  ):
 def get_masked_lm_array(lowerCamelCase__	:							str  ):
  lowercase__       :       int				=						f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowercase__       :       Optional[int]				=						tf.train.load_variable(lowerCamelCase__       ,		lowerCamelCase__  )

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

  return torch.from_numpy(lowerCamelCase__  )

 def get_encoder_array(lowerCamelCase__	:							str  ):
  lowercase__       :       Dict				=						f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowercase__       :       Optional[int]				=						tf.train.load_variable(lowerCamelCase__       ,		lowerCamelCase__  )

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

  return torch.from_numpy(lowerCamelCase__  )

 def get_encoder_layer_array(lowerCamelCase__	:							int       ,		lowerCamelCase__	:							str  ):
  lowercase__       :       str				=						f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowercase__       :       Optional[Any]				=						tf.train.load_variable(lowerCamelCase__       ,		lowerCamelCase__  )

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

  return torch.from_numpy(lowerCamelCase__  )

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

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

  return torch.from_numpy(lowerCamelCase__  )

 print(f'''Loading model based on config from {config_path}...'''  )
 lowercase__       :       Optional[Any]				=						BertConfig.from_json_file(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						BertForMaskedLM(lowerCamelCase__  )

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

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

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

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

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

  lowercase__       :       Any				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_attention_layer_norm/gamma"""  )
  lowercase__       :       Optional[Any]				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_attention_layer_norm/beta"""  )

  # Intermediate
  lowercase__       :       BertIntermediate				=						layer.intermediate

  lowercase__       :       Union[str, Any]				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_intermediate_dense/kernel"""  )
  lowercase__       :       Optional[int]				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_intermediate_dense/bias"""  )

  # Output
  lowercase__       :       BertOutput				=						layer.output

  lowercase__       :       int				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_output_dense/kernel"""  )
  lowercase__       :       List[str]				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_output_dense/bias"""  )

  lowercase__       :       Any				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_output_layer_norm/gamma"""  )
  lowercase__       :       str				=						get_encoder_layer_array(lowerCamelCase__       ,		"""_output_layer_norm/beta"""  )

 # Embeddings
 lowercase__       :       int				=						get_encoder_array("""_position_embedding_layer/embeddings"""  )
 lowercase__       :       Any				=						get_encoder_array("""_type_embedding_layer/embeddings"""  )
 lowercase__       :       Optional[Any]				=						get_encoder_array("""_embedding_norm_layer/gamma"""  )
 lowercase__       :       Any				=						get_encoder_array("""_embedding_norm_layer/beta"""  )

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

 lowercase__       :       Tuple				=						get_masked_lm_array("""dense/kernel"""  )
 lowercase__       :       Dict				=						get_masked_lm_array("""dense/bias"""  )

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

 lowercase__       :       Optional[int]				=						get_masked_lm_array("""embedding_table"""  )

 # Pooling
 lowercase__       :       List[Any]				=						BertPooler(config=lowerCamelCase__  )
 lowercase__       :       BertPooler				=						get_encoder_array("""_pooler_layer/kernel"""  )
 lowercase__       :       BertPooler				=						get_encoder_array("""_pooler_layer/bias"""  )

 # Export final model
 model.save_pretrained(lowerCamelCase__  )

 # Integration test - should load without any errors ;)
 lowercase__       :       Any				=						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"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<mask>" ,     lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    # Mask token behave like a normal word, i.e. include the space before it
    lowercase__       :       Optional[int]				=						AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) else mask_token

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    lowercase__       :       Any				=						[self.cls_token_id]
    lowercase__       :       str				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

    if token_ids_a is None:
     return [1] + ([0] * len(lowerCamelCase__						)) + [1]
    return [1] + ([0] * len(lowerCamelCase__						)) + [1, 1] + ([0] * len(lowerCamelCase__						)) + [1]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[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 + sep + token_ids_a + sep						) * [0]


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Tuple				=						{}

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file ,     lowerCamelCase__						)
    elif not os.path.isfile(self.vocab_file						):
     with open(lowerCamelCase__ ,     """wb"""						) as fi:
      lowercase__       :       int				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (out_vocab_file,)