





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	:					int			  =					logging.get_logger(__name__)


__lowerCamelCase	:					Optional[Any]			  =					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCamelCase	:					List[str]			  =					{
    """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	:					Union[str, Any]			  =					{
    """RUCAIBox/mvp""": 1024,
}



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict						   =       VOCAB_FILES_NAMES
      _UpperCAmelCase     :List[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase     :Any						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase     :str						   =       ['input_ids', 'attention_mask']
      _UpperCAmelCase     :Optional[Any]						   =       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_	,							):
       '''simple docstring'''




       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_	,							)

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

       UpperCamelCase     : Optional[Any]	     =       add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase     : int	     =       "post_processor"
       UpperCamelCase     : Optional[int]	     =       getattr(self.backend_tokenizer	,							A_	,							A_       )
       if tokenizer_component_instance:
        UpperCamelCase     : List[str]	     =       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:
         UpperCamelCase     : Any	     =       tuple(state["sep"]       )
        if "cls" in state:
         UpperCamelCase     : Optional[int]	     =       tuple(state["cls"]       )

        UpperCamelCase     : Dict	     =       False

        if state.get("add_prefix_space"	,							A_       ) != add_prefix_space:
         UpperCamelCase     : List[Any]	     =       add_prefix_space
         UpperCamelCase     : Any	     =       True

        if state.get("trim_offsets"	,							A_       ) != trim_offsets:
         UpperCamelCase     : Union[str, Any]	     =       trim_offsets
         UpperCamelCase     : Tuple	     =       True

        if changes_to_apply:
         UpperCamelCase     : Union[str, Any]	     =       getattr(A_	,							state.pop("type"       )       )
         UpperCamelCase     : List[str]	     =       component_class(**A_       )
         setattr(self.backend_tokenizer	,							A_	,							A_       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''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 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else value
       UpperCamelCase     : Optional[Any]	     =       value





      def 	__UpperCamelCase(			self	,							*A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       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_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       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       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self._tokenizer.model.save(A_	,							name=A_       )
       return tuple(A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_=None       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       [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       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       [self.sep_token_id]
       UpperCamelCase     : int	     =       [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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase	:					str			  =					{"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""EncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""TFEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Union[str, Any]			  =					["""FlaxEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __lowerCamelCase	:					Dict			  =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
