




'''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_bart import BartTokenizer


_lowerCAmelCase      	=					logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase      	=					{
    "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",
    },
}

_lowerCAmelCase      	=					{
    "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 					A		( lowerCAmelCase_		):




				'''simple docstring'''





				A      	=				VOCAB_FILES_NAMES
				A      	=				PRETRAINED_VOCAB_FILES_MAP
				A      	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A      	=				["input_ids", "attention_mask"]
				A      	=				BartTokenizer
				def __init__(self ,  _UpperCAmelCase=None ,  _UpperCAmelCase=None ,  _UpperCAmelCase=None ,  _UpperCAmelCase="replace" ,  _UpperCAmelCase="<s>" ,  _UpperCAmelCase="</s>" ,  _UpperCAmelCase="</s>" ,  _UpperCAmelCase="<s>" ,  _UpperCAmelCase="<unk>" ,  _UpperCAmelCase="<pad>" ,  _UpperCAmelCase="<mask>" ,  _UpperCAmelCase=False ,  _UpperCAmelCase=True ,  **_UpperCAmelCase ,  )	->       int:
							super().__init__(
							    _snake_case ,  _snake_case ,  tokenizer_file=_snake_case ,  errors=_snake_case ,  bos_token=_snake_case ,  eos_token=_snake_case ,  sep_token=_snake_case ,  cls_token=_snake_case ,  unk_token=_snake_case ,  pad_token=_snake_case ,  mask_token=_snake_case ,  add_prefix_space=_snake_case ,  trim_offsets=_snake_case ,  **_snake_case ,  )

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

							__UpperCamelCase      :			int									=   add_prefix_space

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

										__UpperCamelCase      :			int									=   False

										if state.get("add_prefix_space" ,  _snake_case			) != add_prefix_space:
													__UpperCamelCase      :			int									=   add_prefix_space
													__UpperCamelCase      :			Tuple									=   True

										if state.get("trim_offsets" ,  _snake_case			) != trim_offsets:
													__UpperCamelCase      :			Optional[Any]									=   trim_offsets
													__UpperCamelCase      :			Tuple									=   True

										if changes_to_apply:
													__UpperCamelCase      :			Union[str, Any]									=   getattr(_snake_case ,  state.pop("type"			)			)
													__UpperCamelCase      :			List[str]									=   component_class(**_snake_case			)
													setattr(self.backend_tokenizer ,  _snake_case ,  _snake_case			)
				@property
				def        a_       (self			)	->       str:
							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 ,  _UpperCAmelCase			)	->       Dict:
							__UpperCamelCase      :			List[str]									=   AddedToken(_snake_case ,  lstrip=_snake_case ,  rstrip=_snake_case			) if isinstance(_snake_case ,  _snake_case			) else value
							__UpperCamelCase      :			Union[str, Any]									=   value
				def        a_       (self ,  *_UpperCAmelCase ,  **_UpperCAmelCase			)	->       BatchEncoding:
							__UpperCamelCase      :			str									=   kwargs.get("is_split_into_words" ,  _snake_case			)

							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(*_snake_case ,  **_snake_case			)
				def        a_       (self ,  *_UpperCAmelCase ,  **_UpperCAmelCase			)	->       BatchEncoding:
							__UpperCamelCase      :			List[str]									=   kwargs.get("is_split_into_words" ,  _snake_case			)

							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(*_snake_case ,  **_snake_case			)
				def        a_       (self ,  _UpperCAmelCase ,  _UpperCAmelCase = None			)	->       Tuple[str]:
							__UpperCamelCase      :			Dict									=   self._tokenizer.model.save(_snake_case ,  name=_snake_case			)
							return tuple(_snake_case			)
				def        a_       (self ,  _UpperCAmelCase ,  _UpperCAmelCase=None			)	->       Dict:
							__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        a_       (self ,  _UpperCAmelCase ,  _UpperCAmelCase = None			)	->       List[int]:
							__UpperCamelCase      :			Any									=   [self.sep_token_id]
							__UpperCamelCase      :			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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   :Optional[Any]        =      {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :str        =      ["ReformerTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      ["ReformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ReformerAttention",
     "ReformerForMaskedLM",
     "ReformerForQuestionAnswering",
     "ReformerForSequenceClassification",
     "ReformerLayer",
     "ReformerModel",
     "ReformerModelWithLMHead",
     "ReformerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

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






