
"""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_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A						=			[
			    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'XLNetForMultipleChoice',
			    'XLNetForQuestionAnswering',
			    'XLNetForQuestionAnsweringSimple',
			    'XLNetForSequenceClassification',
			    'XLNetForTokenClassification',
			    'XLNetLMHeadModel',
			    'XLNetModel',
			    'XLNetPreTrainedModel',
			    'load_tf_weights_in_xlnet',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A						=			[
			    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFXLNetForMultipleChoice',
			    'TFXLNetForQuestionAnsweringSimple',
			    'TFXLNetForSequenceClassification',
			    'TFXLNetForTokenClassification',
			    'TFXLNetLMHeadModel',
			    'TFXLNetMainLayer',
			    'TFXLNetModel',
			    'TFXLNetPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

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








"""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


_A						=			logging.get_logger(__name__)


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

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

_A						=			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     ['input_ids', 'attention_mask']
	lowercase_				=     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_		,			)		->							Union[str, Any]:
			super().__init__(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			tokenizer_file=UpperCAmelCase_		,			errors=UpperCAmelCase_		,			bos_token=UpperCAmelCase_		,			eos_token=UpperCAmelCase_		,			sep_token=UpperCAmelCase_		,			cls_token=UpperCAmelCase_		,			unk_token=UpperCAmelCase_		,			pad_token=UpperCAmelCase_		,			mask_token=UpperCAmelCase_		,			add_prefix_space=UpperCAmelCase_		,			trim_offsets=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

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

			lowerCamelCase					:      Optional[Any]          =   add_prefix_space

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

					lowerCamelCase					:      Optional[Any]          =   False

					if state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
							lowerCamelCase					:      Dict          =   add_prefix_space
							lowerCamelCase					:      Tuple          =   True

					if state.get('trim_offsets'		,			UpperCAmelCase_ ) != trim_offsets:
							lowerCamelCase					:      Tuple          =   trim_offsets
							lowerCamelCase					:      Dict          =   True

					if changes_to_apply:
							lowerCamelCase					:      Optional[int]          =   getattr(UpperCAmelCase_		,			state.pop('type' ) )
							lowerCamelCase					:      Any          =   component_class(**UpperCAmelCase_ )
							setattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   AddedToken(UpperCAmelCase_		,			lstrip=UpperCAmelCase_		,			rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) else value
			lowerCamelCase					:      int          =   value




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      str          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			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(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      Optional[Any]          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			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(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      Any          =   self._tokenizer.model.save(UpperCAmelCase_		,			name=UpperCAmelCase_ )
			return tuple(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[Any]:
			lowerCamelCase					:      Union[str, 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       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			lowerCamelCase					:      str          =   [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]






