





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase_        =							logging.get_logger(__name__)


lowerCAmelCase_        =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_        =							{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase_        =							{
    '''RUCAIBox/mvp''': 1_0_2_4,
}



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Tuple			     = VOCAB_FILES_NAMES
			lowerCamelCase_	:	int			     = PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_	:	Any			     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_	:	Tuple			     = ['''input_ids''', '''attention_mask''']
			lowerCamelCase_	:	str			     = MvpTokenizer






			def __init__(self   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__="replace"   ,				__magic_name__="<s>"   ,				__magic_name__="</s>"   ,				__magic_name__="</s>"   ,				__magic_name__="<s>"   ,				__magic_name__="<unk>"   ,				__magic_name__="<pad>"   ,				__magic_name__="<mask>"   ,				__magic_name__=False   ,				__magic_name__=True   ,				**__magic_name__   ,				)	->  Tuple:


							'''simple docstring'''



							super().__init__(
							    __magic_name__   ,				__magic_name__   ,				tokenizer_file=__magic_name__   ,				errors=__magic_name__   ,				bos_token=__magic_name__   ,				eos_token=__magic_name__   ,				sep_token=__magic_name__   ,				cls_token=__magic_name__   ,				unk_token=__magic_name__   ,				pad_token=__magic_name__   ,				mask_token=__magic_name__   ,				add_prefix_space=__magic_name__   ,				trim_offsets=__magic_name__   ,				**__magic_name__   ,				)

							snake_case_		:       List[Any]           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
							if pre_tok_state.get('''add_prefix_space'''   ,				__magic_name__       ) != add_prefix_space:
											snake_case_		:       Optional[int]           =    getattr(__magic_name__   ,				pre_tok_state.pop('''type'''       )       )
											snake_case_		:       Any           =    add_prefix_space
											snake_case_		:       List[Any]           =    pre_tok_class(**__magic_name__       )

							snake_case_		:       int           =    add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							snake_case_		:       Tuple           =    '''post_processor'''
							snake_case_		:       Optional[Any]           =    getattr(self.backend_tokenizer   ,				__magic_name__   ,				__magic_name__       )
							if tokenizer_component_instance:
											snake_case_		:       List[Any]           =    json.loads(tokenizer_component_instance.__getstate__()       )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															snake_case_		:       List[str]           =    tuple(state['''sep''']       )
											if "cls" in state:
															snake_case_		:       str           =    tuple(state['''cls''']       )

											snake_case_		:       Tuple           =    False

											if state.get('''add_prefix_space'''   ,				__magic_name__       ) != add_prefix_space:
															snake_case_		:       int           =    add_prefix_space
															snake_case_		:       Dict           =    True

											if state.get('''trim_offsets'''   ,				__magic_name__       ) != trim_offsets:
															snake_case_		:       Optional[Any]           =    trim_offsets
															snake_case_		:       str           =    True

											if changes_to_apply:
															snake_case_		:       Optional[Any]           =    getattr(__magic_name__   ,				state.pop('''type'''       )       )
															snake_case_		:       Optional[int]           =    component_class(**__magic_name__       )
															setattr(self.backend_tokenizer   ,				__magic_name__   ,				__magic_name__       )






			@property
			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							if self._mask_token is None:
											if self.verbose:
															logger.error('''Using mask_token, but it is not set yet.'''       )
											return None
							return str(self._mask_token       )






			@mask_token.setter
			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Tuple           =    AddedToken(__magic_name__   ,				lstrip=__magic_name__   ,				rstrip=__magic_name__       ) if isinstance(__magic_name__   ,				__magic_name__       ) else value
							snake_case_		:       Union[str, Any]           =    value






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  BatchEncoding:


							'''simple docstring'''



							snake_case_		:       Any           =    kwargs.get('''is_split_into_words'''   ,				__magic_name__       )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''       )

							return super()._batch_encode_plus(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  BatchEncoding:


							'''simple docstring'''



							snake_case_		:       Tuple           =    kwargs.get('''is_split_into_words'''   ,				__magic_name__       )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''       )

							return super()._encode_plus(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Tuple[str]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    self._tokenizer.model.save(__magic_name__   ,				name=__magic_name__       )
							return tuple(__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__=None       )	->  str:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  List[int]:


							'''simple docstring'''



							snake_case_		:       int           =    [self.sep_token_id]
							snake_case_		:       List[str]           =    [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]








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)