



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_bart import BartTokenizer


_snake_case		=  logging.get_logger(__name__)


_snake_case		=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
_snake_case		=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

_snake_case		=  {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		VOCAB_FILES_NAMES
							lowerCamelCase__   			=		PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__   			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__   			=		['input_ids', 'attention_mask']
							lowerCamelCase__   			=		BartTokenizer







							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,			)

														_lowerCAmelCase     :							Tuple    =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
														if pre_tok_state.get("add_prefix_space",			__a) != add_prefix_space:
																					_lowerCAmelCase     :							List[Any]    =    getattr(__a,			pre_tok_state.pop("type"))
																					_lowerCAmelCase     :							Any    =    add_prefix_space
																					_lowerCAmelCase     :							Any    =    pre_tok_class(**__a)

														_lowerCAmelCase     :							Dict    =    add_prefix_space

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

																					_lowerCAmelCase     :							List[str]    =    False

																					if state.get("add_prefix_space",			__a) != add_prefix_space:
																												_lowerCAmelCase     :							Union[str, Any]    =    add_prefix_space
																												_lowerCAmelCase     :							List[Any]    =    True

																					if state.get("trim_offsets",			__a) != trim_offsets:
																												_lowerCAmelCase     :							Any    =    trim_offsets
																												_lowerCAmelCase     :							Union[str, Any]    =    True

																					if changes_to_apply:
																												_lowerCAmelCase     :							Optional[int]    =    getattr(__a,			state.pop("type"))
																												_lowerCAmelCase     :							Optional[Any]    =    component_class(**__a)
																												setattr(self.backend_tokenizer,			__a,			__a)







							@property
							def    snake_case__      (   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    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    AddedToken(__a,			lstrip=__a,			rstrip=__a) if isinstance(__a,			__a) else value
														_lowerCAmelCase     :							Optional[int]    =    value







							def    snake_case__      (   self,			*__a,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    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    snake_case__      (   self,			*__a,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    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    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self._tokenizer.model.save(__a,			name=__a)
														return tuple(__a)







							def    snake_case__      (   self,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    [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    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    [self.sep_token_id]
														_lowerCAmelCase     :							Optional[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]



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
