



"""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_longformer import LongformerTokenizer


A    =	logging.get_logger(__name__)

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

A    =	{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

A    =	{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}

class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    VOCAB_FILES_NAMES
			__magic_name__    				=    PRETRAINED_VOCAB_FILES_MAP
			__magic_name__    				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__magic_name__    				=    ["""input_ids""", """attention_mask"""]
			__magic_name__    				=    LongformerTokenizer



			def __init__( self       :							int      ,  _lowercase       :							Any=None      ,  _lowercase       :							Any=None      ,  _lowercase       :							List[Any]=None      ,  _lowercase       :							List[str]="replace"      ,  _lowercase       :							Union[str, Any]="<s>"      ,  _lowercase       :							Optional[int]="</s>"      ,  _lowercase       :							Any="</s>"      ,  _lowercase       :							Any="<s>"      ,  _lowercase       :							List[str]="<unk>"      ,  _lowercase       :							List[Any]="<pad>"      ,  _lowercase       :							Tuple="<mask>"      ,  _lowercase       :							Tuple=False      ,  _lowercase       :							Optional[Any]=True      ,  **_lowercase       :							str      ,  )       ->				Optional[Any]:
									super().__init__(
									    _lowercase      ,  _lowercase      ,  tokenizer_file=_lowercase      ,  errors=_lowercase      ,  bos_token=_lowercase      ,  eos_token=_lowercase      ,  sep_token=_lowercase      ,  cls_token=_lowercase      ,  unk_token=_lowercase      ,  pad_token=_lowercase      ,  mask_token=_lowercase      ,  add_prefix_space=_lowercase      ,  trim_offsets=_lowercase      ,  **_lowercase      ,  )

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

									snake_case      : int							=   add_prefix_space

									snake_case      : str							=   "post_processor"
									snake_case      : Dict							=   getattr(self.backend_tokenizer      ,  _lowercase      ,  _lowercase		)
									if tokenizer_component_instance:
															snake_case      : 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:
																					snake_case      : Tuple							=   tuple(state["sep"]		)
															if "cls" in state:
																					snake_case      : int							=   tuple(state["cls"]		)

															snake_case      : List[str]							=   False

															if state.get("add_prefix_space"      ,  _lowercase		) != add_prefix_space:
																					snake_case      : List[Any]							=   add_prefix_space
																					snake_case      : Tuple							=   True

															if state.get("trim_offsets"      ,  _lowercase		) != trim_offsets:
																					snake_case      : int							=   trim_offsets
																					snake_case      : Dict							=   True

															if changes_to_apply:
																					snake_case      : Dict							=   getattr(_lowercase      ,  state.pop("type"		)		)
																					snake_case      : Optional[int]							=   component_class(**_lowercase		)
																					setattr(self.backend_tokenizer      ,  _lowercase      ,  _lowercase		)



			@property
			def 							__lowercase				( self       :							Dict		)       ->				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 							__lowercase				( self       :							List[str]      ,  _lowercase       :							Dict		)       ->				Optional[int]:
									snake_case      : Union[str, Any]							=   AddedToken(_lowercase      ,  lstrip=_lowercase      ,  rstrip=_lowercase		) if isinstance(_lowercase      ,  _lowercase		) else value
									snake_case      : List[str]							=   value



			def 							__lowercase				( self       :							Tuple      ,  *_lowercase       :							Union[str, Any]      ,  **_lowercase       :							List[str]		)       ->				BatchEncoding:
									snake_case      : Any							=   kwargs.get("is_split_into_words"      ,  _lowercase		)
									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(*_lowercase      ,  **_lowercase		)



			def 							__lowercase				( self       :							Union[str, Any]      ,  *_lowercase       :							Optional[Any]      ,  **_lowercase       :							Union[str, Any]		)       ->				BatchEncoding:
									snake_case      : Union[str, Any]							=   kwargs.get("is_split_into_words"      ,  _lowercase		)

									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(*_lowercase      ,  **_lowercase		)



			def 							__lowercase				( self       :							Optional[int]      ,  _lowercase       :							str      ,  _lowercase       :							Optional[str] = None		)       ->				Tuple[str]:
									snake_case      : List[str]							=   self._tokenizer.model.save(_lowercase      ,  name=_lowercase		)
									return tuple(_lowercase		)



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							Dict      ,  _lowercase       :							Dict=None		)       ->				Optional[Any]:
									snake_case      : Optional[Any]							=   [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 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							List[int]      ,  _lowercase       :							Optional[List[int]] = None		)       ->				List[int]:
									snake_case      : Any							=   [self.sep_token_id]
									snake_case      : 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]








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CamembertForCausalLM',
			    'CamembertForMaskedLM',
			    'CamembertForMultipleChoice',
			    'CamembertForQuestionAnswering',
			    'CamembertForSequenceClassification',
			    'CamembertForTokenClassification',
			    'CamembertModel',
			    'CamembertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFCamembertForCausalLM',
			    'TFCamembertForMaskedLM',
			    'TFCamembertForMultipleChoice',
			    'TFCamembertForQuestionAnswering',
			    'TFCamembertForSequenceClassification',
			    'TFCamembertForTokenClassification',
			    'TFCamembertModel',
			    'TFCamembertPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert import CamembertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert_fast import CamembertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_camembert import (
						    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CamembertForCausalLM,
						    CamembertForMaskedLM,
						    CamembertForMultipleChoice,
						    CamembertForQuestionAnswering,
						    CamembertForSequenceClassification,
						    CamembertForTokenClassification,
						    CamembertModel,
						    CamembertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_camembert import (
						    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCamembertForCausalLM,
						    TFCamembertForMaskedLM,
						    TFCamembertForMultipleChoice,
						    TFCamembertForQuestionAnswering,
						    TFCamembertForSequenceClassification,
						    TFCamembertForTokenClassification,
						    TFCamembertModel,
						    TFCamembertPreTrainedModel,
						)

else:
			import sys

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



