
"""simple docstring"""







import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

lowercase_         =       logging.get_logger(__name__)


lowercase_         =       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_         =       {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

lowercase_         =       {"facebook/blenderbot-3B": 128}



class 		UpperCAmelCase_						(lowerCAmelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Dict					 =     VOCAB_FILES_NAMES
 UpperCamelCase_    :  int					 =     PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase_    :  str					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase_    :  Any					 =     ['input_ids', 'attention_mask']
 UpperCamelCase_    :  List[str]					 =     BlenderbotTokenizer




 def __init__(			self							:			Dict  , a_							:			Optional[Any]=None  , a_							:			Dict=None  , a_							:			Optional[int]=None  , a_							:			int="replace"  , a_							:			Dict="<s>"  , a_							:			Tuple="</s>"  , a_							:			List[str]="</s>"  , a_							:			Dict="<s>"  , a_							:			Tuple="<unk>"  , a_							:			str="<pad>"  , a_							:			str="<mask>"  , a_							:			Union[str, Any]=False  , a_							:			Dict=True  , **a_							:			Any  , )->       int:



      """simple docstring"""

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

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

      UpperCAmelCase_    :     List[Any]            =	add_prefix_space

      UpperCAmelCase_    :     Tuple            =	"""post_processor"""
      UpperCAmelCase_    :     int            =	getattr(self.backend_tokenizer  , __lowerCAmelCase  , __lowerCAmelCase			)
      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_    :     Dict            =	tuple(state["""sep"""]			)
           if "cls" in state:
                UpperCAmelCase_    :     Any            =	tuple(state["""cls"""]			)

           UpperCAmelCase_    :     Optional[int]            =	False

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

           if state.get("""trim_offsets"""  , __lowerCAmelCase			) != trim_offsets:
                UpperCAmelCase_    :     Union[str, Any]            =	trim_offsets
                UpperCAmelCase_    :     List[Any]            =	True

           if changes_to_apply:
                UpperCAmelCase_    :     List[Any]            =	getattr(__lowerCAmelCase  , state.pop("""type"""			)			)
                UpperCAmelCase_    :     List[Any]            =	component_class(**__lowerCAmelCase			)
                setattr(self.backend_tokenizer  , __lowerCAmelCase  , __lowerCAmelCase			)




 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 	a   (			self							:			Any			)->       Tuple:



      """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 	a   (			self							:			Tuple  , a_							:			List[Any]			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	AddedToken(__lowerCAmelCase  , lstrip=__lowerCAmelCase  , rstrip=__lowerCAmelCase			) if isinstance(__lowerCAmelCase  , __lowerCAmelCase			) else value
      UpperCAmelCase_    :     List[Any]            =	value




 def 	a   (			self							:			Optional[Any]  , *a_							:			str  , **a_							:			Optional[Any]			)->       Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	kwargs.get("""is_split_into_words"""  , __lowerCAmelCase			)
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

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




 def 	a   (			self							:			List[Any]  , *a_							:			Any  , **a_							:			List[str]			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	kwargs.get("""is_split_into_words"""  , __lowerCAmelCase			)

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

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




 def 	a   (			self							:			Optional[int]  , a_							:			str  , a_							:			Optional[str] = None			)->       Tuple:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	self._tokenizer.model.save(__lowerCAmelCase  , name=__lowerCAmelCase			)
      return tuple(__lowerCAmelCase			)




 def 	a   (			self							:			Any  , a_							:			List[int]  , a_							:			Optional[List[int]] = None			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	[self.sep_token_id]
      UpperCAmelCase_    :     Union[str, Any]            =	[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]




 def 	a   (			self							:			Dict  , a_							:			List[int]  , a_							:			Optional[List[int]] = None			)->       Any:



      """simple docstring"""

      return token_ids_a + [self.eos_token_id]






 def 	a   (			self							:			Dict  , a_							:			"Conversation"			)->       Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	[]
      for is_user, text in conversation.iter_texts():
           if is_user:
                # We need to space prefix as it's being done within blenderbot
                inputs.append(""" """ + text			)
           else:
                # Generated responses should contain them already.
                inputs.append(__lowerCAmelCase			)

      UpperCAmelCase_    :     Optional[Any]            =	"""  """.join(__lowerCAmelCase			)
      UpperCAmelCase_    :     Optional[Any]            =	self.encode(__lowerCAmelCase			)
      if len(__lowerCAmelCase			) > self.model_max_length:
           UpperCAmelCase_    :     Optional[int]            =	input_ids[-self.model_max_length :]
           logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''			)
      return input_ids


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
