





'''simple docstring'''







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


_lowercase       						=			logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
_lowercase       						=			{
    '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',
    },
}

_lowercase       						=			{
    'RUCAIBox/mvp': 1_024,
}
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					VOCAB_FILES_NAMES
     _UpperCAmelCase										=					PRETRAINED_VOCAB_FILES_MAP
     _UpperCAmelCase										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCAmelCase										=					['''input_ids''', '''attention_mask''']
     _UpperCAmelCase										=					MvpTokenizer







     def __init__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__="replace"    ,   A__="<s>"    ,   A__="</s>"    ,   A__="</s>"    ,   A__="<s>"    ,   A__="<unk>"    ,   A__="<pad>"    ,   A__="<mask>"    ,   A__=False    ,   A__=True    ,   **A__    ,   ):
      super().__init__(
          A__    ,   A__    ,   tokenizer_file=A__    ,   errors=A__    ,   bos_token=A__    ,   eos_token=A__    ,   sep_token=A__    ,   cls_token=A__    ,   unk_token=A__    ,   pad_token=A__    ,   mask_token=A__    ,   add_prefix_space=A__    ,   trim_offsets=A__    ,   **A__    ,   )

      snake_case				=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
      if pre_tok_state.get('''add_prefix_space'''    ,   A__    ) != add_prefix_space:
       snake_case				=			getattr(A__    ,   pre_tok_state.pop('''type'''    )    )
       snake_case				=			add_prefix_space
       snake_case				=			pre_tok_class(**A__    )

      snake_case				=			add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      snake_case				=			'post_processor'
      snake_case				=			getattr(self.backend_tokenizer    ,   A__    ,   A__    )
      if tokenizer_component_instance:
       snake_case				=			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				=			tuple(state['''sep''']    )
       if "cls" in state:
        snake_case				=			tuple(state['''cls''']    )

       snake_case				=			False

       if state.get('''add_prefix_space'''    ,   A__    ) != add_prefix_space:
        snake_case				=			add_prefix_space
        snake_case				=			True

       if state.get('''trim_offsets'''    ,   A__    ) != trim_offsets:
        snake_case				=			trim_offsets
        snake_case				=			True

       if changes_to_apply:
        snake_case				=			getattr(A__    ,   state.pop('''type'''    )    )
        snake_case				=			component_class(**A__    )
        setattr(self.backend_tokenizer    ,   A__    ,   A__    )







     @property
     def     UpperCamelCase			( self    ):







      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     UpperCamelCase			( self    ,   A__    ):
      snake_case				=			AddedToken(A__    ,   lstrip=A__    ,   rstrip=A__    ) if isinstance(A__    ,   A__    ) else value
      snake_case				=			value







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ):
      snake_case				=			kwargs.get('''is_split_into_words'''    ,   A__    )

      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(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ):
      snake_case				=			kwargs.get('''is_split_into_words'''    ,   A__    )

      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(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ):
      snake_case				=			self._tokenizer.model.save(A__    ,   name=A__    )
      return tuple(A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=None    ):
      snake_case				=			[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     UpperCamelCase			( self    ,   A__    ,   A__ = None    ):
      snake_case				=			[self.sep_token_id]
      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 + sep + token_ids_a + sep    ) * [0]








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

