




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_pegasus import PegasusTokenizer
else:
   snake_case__			:    List[Any]			     =      None


snake_case__			:    int			     =      logging.get_logger(__name__)


snake_case__			:    Dict			     =      '''▁'''

snake_case__			:    Optional[Any]			     =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__			:    Optional[int]			     =      {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

snake_case__			:    Dict			     =      {
    '''google/pegasus-xsum''': 5_1_2,
}





class      snake_case	(						lowerCAmelCase__				):


   '''simple docstring'''







   UpperCamelCase__						:							Optional[Any] 	=   VOCAB_FILES_NAMES
   UpperCamelCase__						:							Optional[Any] 	=   PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase__						:							List[Any] 	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase__						:							str 	=   PegasusTokenizer
   UpperCamelCase__						:							Union[str, Any] 	=   ["input_ids", "attention_mask"]




   def __init__(       self  :						List[Any]    ,     lowerCamelCase_  :						Optional[Any]=None    ,     lowerCamelCase_  :						Union[str, Any]=None    ,     lowerCamelCase_  :						Optional[int]="<pad>"    ,     lowerCamelCase_  :						Optional[Any]="</s>"    ,     lowerCamelCase_  :						Any="<unk>"    ,     lowerCamelCase_  :						Optional[Any]="<mask_2>"    ,     lowerCamelCase_  :						Dict="<mask_1>"    ,     lowerCamelCase_  :						Any=None    ,     lowerCamelCase_  :						Tuple=103    ,     **lowerCamelCase_  :						int    ,     )       ->Any:
     '''simple docstring'''







     UpperCAmelCase__         = offset

     if additional_special_tokens is not None:
       if not isinstance(_lowerCamelCase    ,     _lowerCamelCase      ):
         raise TypeError(
             f'''additional_special_tokens should be of type {type(_lowerCamelCase      )}, but is'''
             f''' {type(_lowerCamelCase      )}'''      )

       UpperCAmelCase__         = (
           ([mask_token_sent] + additional_special_tokens)
           if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
           else additional_special_tokens
       )
       # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
       additional_special_tokens_extended += [
           f'''<unk_{i}>''' for i in range(len(_lowerCamelCase      )    ,     self.offset - 1      )
       ]

       if len(set(_lowerCamelCase      )      ) != len(_lowerCamelCase      ):
         raise ValueError(
             """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
             f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
       UpperCAmelCase__         = additional_special_tokens_extended
     else:
       UpperCAmelCase__         = [mask_token_sent] if mask_token_sent is not None else []
       additional_special_tokens += [f'''<unk_{i}>''' for i in range(2    ,     self.offset      )]

     super().__init__(
         _lowerCamelCase    ,     tokenizer_file=_lowerCamelCase    ,     pad_token=_lowerCamelCase    ,     eos_token=_lowerCamelCase    ,     unk_token=_lowerCamelCase    ,     mask_token=_lowerCamelCase    ,     mask_token_sent=_lowerCamelCase    ,     offset=_lowerCamelCase    ,     additional_special_tokens=_lowerCamelCase    ,     **_lowerCamelCase    ,     )
     UpperCAmelCase__         = vocab_file
     UpperCAmelCase__         = False if not self.vocab_file else True




   def 						UpperCAmelCase							(       self  :						List[Any]    ,     lowerCamelCase_  :						List[str]      )       ->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__         = set(self.all_special_ids      )  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
       raise ValueError(
           """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
           f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

     return [1 if x in all_special_ids else 0 for x in seq]




   def 						UpperCAmelCase							(       self  :						Any    ,     lowerCamelCase_  :						Optional[int]    ,     lowerCamelCase_  :						int = None    ,     lowerCamelCase_  :						List[str] = False      )       ->Dict:
     '''simple docstring'''







     if already_has_special_tokens:
       return self._special_token_mask(_lowerCamelCase      )
     elif token_ids_a is None:
       return self._special_token_mask(_lowerCamelCase      ) + [1]
     else:
       return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




   def 						UpperCAmelCase							(       self  :						Any    ,     lowerCamelCase_  :						Dict    ,     lowerCamelCase_  :						Dict=None      )       ->Union[str, Any]:
     '''simple docstring'''







     if token_ids_a is None:
       return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]







   def 						UpperCAmelCase							(       self  :						Any    ,     lowerCamelCase_  :						Union[str, Any]    ,     lowerCamelCase_  :						Optional[int] = None      )       ->Any:
     '''simple docstring'''







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
           """tokenizer."""      )

     if not os.path.isdir(_lowerCamelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
     UpperCAmelCase__         = 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      ):
       copyfile(self.vocab_file    ,     _lowerCamelCase      )

     return (out_vocab_file,)







"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

