

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig







def      a__ (						a__   ,					a__   ,					a__=0		):




     """simple docstring"""


     if name is None:
          __SCREAMING_SNAKE_CASE						     =   None
     else:
          __SCREAMING_SNAKE_CASE						     =   '''.''' * max(0   ,					spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
          __SCREAMING_SNAKE_CASE						     =   fmt.format(SCREAMING_SNAKE_CASE__		)

     # Print and recurse (if needed).
     if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__		):
          if msg is not None:
               print(SCREAMING_SNAKE_CASE__		)
          for k in val.keys():
               recursive_print(SCREAMING_SNAKE_CASE__   ,					val[k]   ,					spaces + 2		)
     elif isinstance(SCREAMING_SNAKE_CASE__   ,					torch.Tensor		):
          print(SCREAMING_SNAKE_CASE__   ,					""":"""   ,					val.size()		)
     else:
          print(SCREAMING_SNAKE_CASE__   ,					""":"""   ,					SCREAMING_SNAKE_CASE__		)







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          __SCREAMING_SNAKE_CASE						     =   (num_heads, hidden_size, num_splits) + input_shape[1:]
          __SCREAMING_SNAKE_CASE						     =   param.view(*SCREAMING_SNAKE_CASE__		)
          __SCREAMING_SNAKE_CASE						     =   param.transpose(0   ,					2		)
          __SCREAMING_SNAKE_CASE						     =   param.transpose(1   ,					2		).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          __SCREAMING_SNAKE_CASE						     =   (num_heads, num_splits, hidden_size) + input_shape[1:]
          __SCREAMING_SNAKE_CASE						     =   param.view(*SCREAMING_SNAKE_CASE__		)
          __SCREAMING_SNAKE_CASE						     =   param.transpose(0   ,					1		).contiguous()
     __SCREAMING_SNAKE_CASE						     =   param.view(*SCREAMING_SNAKE_CASE__		)
     return param







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   {}

     # old versions did not store training args
     __SCREAMING_SNAKE_CASE						     =   input_state_dict.get("""args"""   ,					SCREAMING_SNAKE_CASE__		)
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          __SCREAMING_SNAKE_CASE						     =   ds_args.tokenizer_type
          __SCREAMING_SNAKE_CASE						     =   ds_args.padded_vocab_size
          __SCREAMING_SNAKE_CASE						     =   ds_args.max_position_embeddings
          __SCREAMING_SNAKE_CASE						     =   ds_args.hidden_size
          __SCREAMING_SNAKE_CASE						     =   ds_args.num_layers
          __SCREAMING_SNAKE_CASE						     =   ds_args.num_attention_heads
          __SCREAMING_SNAKE_CASE						     =   ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

     # The number of heads.
     __SCREAMING_SNAKE_CASE						     =   config.num_attention_heads
     # The hidden_size per head.
     __SCREAMING_SNAKE_CASE						     =   config.hidden_size // heads
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          __SCREAMING_SNAKE_CASE						     =   input_state_dict['''checkpoint_version''']
     else:
          __SCREAMING_SNAKE_CASE						     =   0.0

     # The model.
     __SCREAMING_SNAKE_CASE						     =   input_state_dict['''model''']
     # The language model.
     __SCREAMING_SNAKE_CASE						     =   model['''language_model''']
     # The embeddings.
     __SCREAMING_SNAKE_CASE						     =   lm['''embedding''']

     # The word embeddings.
     __SCREAMING_SNAKE_CASE						     =   embeddings['''word_embeddings''']['''weight''']
     # Truncate the embedding table to vocab_size rows.
     __SCREAMING_SNAKE_CASE						     =   word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     __SCREAMING_SNAKE_CASE						     =   word_embeddings

     # The position embeddings.
     __SCREAMING_SNAKE_CASE						     =   embeddings['''position_embeddings''']['''weight''']
     assert pos_embeddings.size(0		) == config.max_position_embeddings and pos_embeddings.size(1		) == config.hidden_size
     # Store the position embeddings.
     __SCREAMING_SNAKE_CASE						     =   pos_embeddings

     # The token-type embeddings.
     __SCREAMING_SNAKE_CASE						     =   embeddings['''tokentype_embeddings''']['''weight''']
     # Store the position embeddings.
     __SCREAMING_SNAKE_CASE						     =   tokentype_embeddings

     # The transformer.
     __SCREAMING_SNAKE_CASE						     =   lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

     # The regex to extract layer names.
     __SCREAMING_SNAKE_CASE						     =   re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""		)

     # The simple map of names for "automated" rules.
     __SCREAMING_SNAKE_CASE						     =   {
         '''attention.dense''': '''.attention.output.dense.''',
         '''self_attention.dense''': '''.attention.output.dense.''',
         '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
         '''mlp.dense_4h_to_h''': '''.output.dense.''',
     }

     # Keep track of the attention/query/value tensor.
     __SCREAMING_SNAKE_CASE						     =   None

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          __SCREAMING_SNAKE_CASE						     =   layer_re.match(SCREAMING_SNAKE_CASE__		)

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          __SCREAMING_SNAKE_CASE						     =   int(m.group(1		)		)
          # The name of the operation.
          __SCREAMING_SNAKE_CASE						     =   m.group(2		)
          # Is it a weight or a bias?
          __SCREAMING_SNAKE_CASE						     =   m.group(3		)

          # The name of the layer.
          __SCREAMING_SNAKE_CASE						     =   F'bert.encoder.layer.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith("""layernorm"""		):
               __SCREAMING_SNAKE_CASE						     =   '''attention.ln''' if op_name.startswith("""input"""		) else '''ln'''
               __SCREAMING_SNAKE_CASE						     =   val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Make sure the QKV pointer is nil.
               assert attention_qkv_weight is None, ""

               __SCREAMING_SNAKE_CASE						     =   fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					3   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__		)
               # Store the tensor as we need the bias as well to interleave QKV and biases.
               __SCREAMING_SNAKE_CASE						     =   out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               # Make sure we read the weight tensor.
               assert attention_qkv_weight is not None, ""

               # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
               __SCREAMING_SNAKE_CASE						     =   attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               __SCREAMING_SNAKE_CASE						     =   attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               __SCREAMING_SNAKE_CASE						     =   attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               __SCREAMING_SNAKE_CASE						     =   fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					3   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__		)
               # Split the bias.
               __SCREAMING_SNAKE_CASE						     =   out_val[0 * config.hidden_size : 1 * config.hidden_size]
               __SCREAMING_SNAKE_CASE						     =   out_val[1 * config.hidden_size : 2 * config.hidden_size]
               __SCREAMING_SNAKE_CASE						     =   out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               __SCREAMING_SNAKE_CASE						     =   q
               __SCREAMING_SNAKE_CASE						     =   q_bias
               __SCREAMING_SNAKE_CASE						     =   k
               __SCREAMING_SNAKE_CASE						     =   k_bias
               __SCREAMING_SNAKE_CASE						     =   v
               __SCREAMING_SNAKE_CASE						     =   v_bias

               # Clear the stored tensor.
               __SCREAMING_SNAKE_CASE						     =   None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               __SCREAMING_SNAKE_CASE						     =   megatron_to_transformers[op_name]
               __SCREAMING_SNAKE_CASE						     =   val

    # The final layernorm.
     __SCREAMING_SNAKE_CASE						     =   transformer['''final_layernorm.weight''']
     __SCREAMING_SNAKE_CASE						     =   transformer['''final_layernorm.bias''']

     # The pooler.
     __SCREAMING_SNAKE_CASE						     =   lm['''pooler''']

     # Store the matrix and the bias.
     __SCREAMING_SNAKE_CASE						     =   pooler['''dense.weight''']
     __SCREAMING_SNAKE_CASE						     =   pooler['''dense.bias''']

     # The LM head from Megatron (for RACE).
     __SCREAMING_SNAKE_CASE						     =   model['''lm_head''']

     # The transform matrix.
     __SCREAMING_SNAKE_CASE						     =   lm_head['''dense.weight''']
     __SCREAMING_SNAKE_CASE						     =   lm_head['''dense.bias''']

     # The transform LN.
     __SCREAMING_SNAKE_CASE						     =   lm_head['''layernorm.weight''']
     __SCREAMING_SNAKE_CASE						     =   lm_head['''layernorm.bias''']

     # For the decoder, we replicate the weights.
     __SCREAMING_SNAKE_CASE						     =   word_embeddings
     __SCREAMING_SNAKE_CASE						     =   lm_head['''bias''']

     # The classifier from Megatron (for MLNI).
     __SCREAMING_SNAKE_CASE						     =   model['''binary_head''']

     # Store the classifier.
     __SCREAMING_SNAKE_CASE						     =   binary_head['''weight''']
     __SCREAMING_SNAKE_CASE						     =   binary_head['''bias''']

     # It should be done!
     return output_state_dict







def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   argparse.ArgumentParser()
     parser.add_argument("""--print-checkpoint-structure"""   ,					action="""store_true"""		)
     parser.add_argument("""path_to_checkpoint"""   ,					type=SCREAMING_SNAKE_CASE__   ,					help="""Path to the ZIP file containing the checkpoint"""		)
     parser.add_argument(
         """--config_file"""   ,					default=""""""   ,					type=SCREAMING_SNAKE_CASE__   ,					help="""An optional config json file describing the pre-trained model."""   ,					)
     __SCREAMING_SNAKE_CASE						     =   parser.parse_args()

     # Extract the basename.
     __SCREAMING_SNAKE_CASE						     =   os.path.dirname(args.path_to_checkpoint		)

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(F'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'		)
     if args.path_to_checkpoint.endswith(""".zip"""		):
          with zipfile.ZipFile(args.path_to_checkpoint   ,					"""r"""		) as checkpoint:
               with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt"""		) as pytorch_dict:
                    __SCREAMING_SNAKE_CASE						     =   torch.load(SCREAMING_SNAKE_CASE__   ,					map_location="""cpu"""		)
     else:
          __SCREAMING_SNAKE_CASE						     =   torch.load(args.path_to_checkpoint   ,					map_location="""cpu"""		)

     if args.config_file == "":
          # Default config of megatron-bert 345m
          __SCREAMING_SNAKE_CASE						     =   MegatronBertConfig()

          # different megatron-bert-*-345m models have different vocab sizes, so override the default
          # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
          __SCREAMING_SNAKE_CASE						     =   input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
     else:
          __SCREAMING_SNAKE_CASE						     =   MegatronBertConfig.from_json_file(args.config_file		)

     # Convert.
     print("""Converting"""		)
     __SCREAMING_SNAKE_CASE						     =   convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__		)

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__		)

     # Store the config to file.
     print("""Saving config"""		)
     config.save_pretrained(SCREAMING_SNAKE_CASE__		)

     # Store the state_dict to file.
     __SCREAMING_SNAKE_CASE						     =   os.path.join(SCREAMING_SNAKE_CASE__   ,					"""pytorch_model.bin"""		)
     print(F'Saving checkpoint to "{output_checkpoint_file}"'		)
     torch.save(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__		)


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################









'''simple docstring'''



import os
import re
import unicodedata
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


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)
UpperCAmelCase				: List[Any]         =  {'vocab_file': 'spiece.model'}

UpperCAmelCase				: Any         =  {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

UpperCAmelCase				: Tuple         =  {
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

UpperCAmelCase				: Union[str, Any]         =  '▁'







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   VOCAB_FILES_NAMES
 lowerCAmelCase__						      =   PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__						      =   ["input_ids", "token_type_ids"]
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : str=False      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=True      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : int="<unk>"      ,							__SCREAMING_SNAKE_CASE      : List[str]="[SEP]"      ,							__SCREAMING_SNAKE_CASE      : List[str]="<pad>"      ,							__SCREAMING_SNAKE_CASE      : List[Any]="[CLS]"      ,							__SCREAMING_SNAKE_CASE      : Dict="[MASK]"      ,							__SCREAMING_SNAKE_CASE      : Optional[Dict[str, Any]] = None      ,							**__SCREAMING_SNAKE_CASE      : Dict      ,							)						->				None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   (
          AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE      ,							normalized=__SCREAMING_SNAKE_CASE    )
          if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
          else mask_token
      )

      __SCREAMING_SNAKE_CASE						     =   {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=__SCREAMING_SNAKE_CASE      ,							remove_space=__SCREAMING_SNAKE_CASE      ,							keep_accents=__SCREAMING_SNAKE_CASE      ,							unk_token=__SCREAMING_SNAKE_CASE      ,							sep_token=__SCREAMING_SNAKE_CASE      ,							pad_token=__SCREAMING_SNAKE_CASE      ,							cls_token=__SCREAMING_SNAKE_CASE      ,							mask_token=__SCREAMING_SNAKE_CASE      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**__SCREAMING_SNAKE_CASE      ,							)

      __SCREAMING_SNAKE_CASE						     =   do_lower_case
      __SCREAMING_SNAKE_CASE						     =   remove_space
      __SCREAMING_SNAKE_CASE						     =   keep_accents
      __SCREAMING_SNAKE_CASE						     =   vocab_file

      __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(__SCREAMING_SNAKE_CASE    )
 @property
 def    UpperCAmelCase__	(	self      : int    )						->				Optional[Any]:


      """simple docstring"""




      return len(self.sp_model    )
 def    UpperCAmelCase__	(	self      : Dict    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
      vocab.update(self.added_tokens_encoder    )
      return vocab
 def __getstate__(	self      : Any    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.__dict__.copy()
      __SCREAMING_SNAKE_CASE						     =   None
      return state
 def __setstate__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   d

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

      __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(self.vocab_file    )
 def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Optional[int]:


      """simple docstring"""




      if self.remove_space:
           __SCREAMING_SNAKE_CASE						     =   """ """.join(inputs.strip().split()    )
      else:
           __SCREAMING_SNAKE_CASE						     =   inputs
      __SCREAMING_SNAKE_CASE						     =   outputs.replace("""``"""      ,							"""\""""    ).replace("""''"""      ,							"""\""""    )

      if not self.keep_accents:
           __SCREAMING_SNAKE_CASE						     =   unicodedata.normalize("""NFKD"""      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   """""".join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE    )]    )
      if self.do_lower_case:
           __SCREAMING_SNAKE_CASE						     =   outputs.lower()

      return outputs
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : str    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.preprocess_text(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,							out_type=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   []
      for piece in pieces:
           if len(__SCREAMING_SNAKE_CASE    ) > 1 and piece[-1] == str(""","""    ) and piece[-2].isdigit():
                __SCREAMING_SNAKE_CASE						     =   self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE      ,							""""""    )    )
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]    ) == 1:
                          __SCREAMING_SNAKE_CASE						     =   cur_pieces[1:]
                     else:
                          __SCREAMING_SNAKE_CASE						     =   cur_pieces[0][1:]
                cur_pieces.append(piece[-1]    )
                new_pieces.extend(__SCREAMING_SNAKE_CASE    )
           else:
                new_pieces.append(__SCREAMING_SNAKE_CASE    )

      return new_pieces
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Optional[Any]:


      """simple docstring"""




      return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Optional[Any]:


      """simple docstring"""




      return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   []
      __SCREAMING_SNAKE_CASE						     =   """"""
      __SCREAMING_SNAKE_CASE						     =   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(__SCREAMING_SNAKE_CASE    ) + token
                __SCREAMING_SNAKE_CASE						     =   True
                __SCREAMING_SNAKE_CASE						     =   []
           else:
                current_sub_tokens.append(__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   False
      out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    )
      return out_string.strip()
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = None      ,							__SCREAMING_SNAKE_CASE      : bool = True      ,							**__SCREAMING_SNAKE_CASE      : List[str]      ,							)						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   kwargs.pop("""use_source_tokenizer"""      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE      ,							skip_special_tokens=__SCREAMING_SNAKE_CASE    )

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      __SCREAMING_SNAKE_CASE						     =   []
      __SCREAMING_SNAKE_CASE						     =   []
      for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
                continue
           if token in self.added_tokens_encoder:
                if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE    )    )
                     __SCREAMING_SNAKE_CASE						     =   []
                sub_texts.append(__SCREAMING_SNAKE_CASE    )
           else:
                current_sub_text.append(__SCREAMING_SNAKE_CASE    )
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE    )    )

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
           __SCREAMING_SNAKE_CASE						     =   re.sub(r"""(<unk>) """      ,							r"""\1"""      ,							""" """.join(__SCREAMING_SNAKE_CASE    )    )
      else:
           __SCREAMING_SNAKE_CASE						     =   """""".join(__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
           __SCREAMING_SNAKE_CASE						     =   self.clean_up_tokenization(__SCREAMING_SNAKE_CASE    )
           return clean_text
      else:
           return text
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [self.sep_token_id]
      __SCREAMING_SNAKE_CASE						     =   [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False    )						->				List[int]:


      """simple docstring"""




      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__SCREAMING_SNAKE_CASE      ,							token_ids_a=__SCREAMING_SNAKE_CASE      ,							already_has_special_tokens=__SCREAMING_SNAKE_CASE    )

      if token_ids_a is not None:
           return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1]
      return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1]
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [self.sep_token_id]
      __SCREAMING_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      : List[str]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[str] = None    )						->				Tuple[str]:


      """simple docstring"""




      if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
           return
      __SCREAMING_SNAKE_CASE						     =   os.path.join(
          __SCREAMING_SNAKE_CASE      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

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

      return (out_vocab_file,)






