



'''simple docstring'''





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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						"▁"

lowercase_	 =						{"vocab_file": "prophetnet.tokenizer"}

lowercase_	 =						{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase_	 =						{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase_	 =						{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						collections.OrderedDict()
     with open(__A						,			'''r'''						,			encoding='''utf-8''') as reader:
          _a											=						reader.readlines()
     for index, token in enumerate(__A):
          _a											=						token.rstrip('''\n''')
          _a											=						index
     return vocab






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Any    = VOCAB_FILES_NAMES
 __lowerCamelCase					: List[Any]    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: Optional[Any]    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: List[Any]    = ['input_ids', 'attention_mask']



 def __init__(self				,     A				,     A="[SEP]"				,     A="[SEP]"				,     A="[SEP]"				,     A="[UNK]"				,     A="[PAD]"				,     A="[CLS]"				,     A="[MASK]"				,     A = None				,     **A				,     )					->  None:


      """simple docstring"""



      _a											=						{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=A				,     eos_token=A				,     sep_token=A				,     unk_token=A				,     pad_token=A				,     cls_token=A				,     mask_token=A				,     sp_model_kwargs=self.sp_model_kwargs				,     **A				,     )

      try:
           import sentencepiece as spm
      except ImportError:
           logger.warning(
               '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
               ''' pip install sentencepiece''' )
           raise

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(str(A ) )
      _a											=						vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # put special tokens and [unused] tokens into the vocab
      _a											=						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

      for i in range(10 ):
           _a											=						f'''[unused{i}]'''
           _a											=						5 + i

      # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
      _a											=						12
      _a											=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(A )



 def __getstate__(self )					->  int:


      """simple docstring"""



      _a											=						self.__dict__.copy()
      _a											=						None
      return state



 def __setstate__(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						d
      try:
           import sentencepiece as spm
      except ImportError:
           logger.warning(
               '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
               ''' pip install sentencepiece''' )
           raise

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

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(self.vocab_file )



 def       a__						(self				,     A				,     A = None				,     A = False )					->  List[int]:


      """simple docstring"""



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

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



 def       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



      _a											=						[self.sep_token_id]

      if token_ids_a is None:
           return len(token_ids_a + sep ) * [0]
      return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]



 @property
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      return len(self.sp_model ) + self.fairseq_offset



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
      vocab.update(self.added_tokens_encoder )
      return vocab



 def       a__						(self				,     A )					->  str:


      """simple docstring"""



      return self.sp_model.encode(A				,     out_type=A )



 def       a__						(self				,     A )					->  Any:


      """simple docstring"""



      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      _a											=						self.sp_model.PieceToId(A )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



 def       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      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       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						''''''.join(A ).replace(A				,     ''' ''' ).strip()
      return out_string



 def       a__						(self				,     A				,     A = None )					->  Tuple[str]:


      """simple docstring"""



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

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

      return (out_vocab_file,)



 def       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
      _a											=						[self.sep_token_id]
      return token_ids_a + sep + token_ids_a + sep








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 'pegasus'
 __lowerCamelCase					: Tuple    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self				,     A=50_265				,     A=1_024				,     A=12				,     A=4_096				,     A=16				,     A=12				,     A=4_096				,     A=16				,     A=0.0				,     A=0.0				,     A=True				,     A=True				,     A="gelu"				,     A=1_024				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=0				,     A=False				,     A=0				,     A=1				,     A=1				,     **A				,     )					->  str:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						max_position_embeddings
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						use_cache
      _a											=						encoder_layers
      _a											=						scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=A				,     eos_token_id=A				,     is_encoder_decoder=A				,     decoder_start_token_id=A				,     forced_eos_token_id=A				,     **A				,     )



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model



