

'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase :      str					 =     logging.get_logger(__name__)







class 							A__    (       A__  ):
  A__		 =				['input_ids', 'attention_mask']





  def __init__(		self      :	Any							,					_a      :	List[str]="</s>"							,					_a      :	Optional[int]="<unk>"							,					_a      :	Optional[Any]="<pad>"							,					_a      :	Optional[int]=125							,					_a      :	Optional[Any]=None							,					**_a      :	Optional[Any]							,					)	->    None:





       '''simple docstring'''






       if extra_ids > 0 and additional_special_tokens is None:
            _SCREAMING_SNAKE_CASE							    =[f"<extra_id_{i}>" for i in range(_a )]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            _SCREAMING_SNAKE_CASE							    =len(set(filter(lambda _a : bool('extra_id' in str(_a ) )							,					_a ) ) )
            if extra_tokens != extra_ids:
                 raise ValueError(
                     f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                     ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                     ' extra_ids tokens' )

       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else pad_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else eos_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else unk_token

       super().__init__(
           eos_token=_a							,					unk_token=_a							,					pad_token=_a							,					extra_ids=_a							,					additional_special_tokens=_a							,					**_a							,					)

       _SCREAMING_SNAKE_CASE							    =extra_ids

       _SCREAMING_SNAKE_CASE							    =2**8  # utf is 8 bits

       # define special tokens dict
       _SCREAMING_SNAKE_CASE							    ={
           self.pad_token: 0,
           self.eos_token: 1,
           self.unk_token: 2,
       }
       _SCREAMING_SNAKE_CASE							    =len(self.special_tokens_encoder )
       _SCREAMING_SNAKE_CASE							    =len(_a )
       for i, token in enumerate(_a ):
            _SCREAMING_SNAKE_CASE							    =self.vocab_size + i - n
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.special_tokens_encoder.items()}





  @property
  def 						A  (		self      :	str )	->    Dict:





       '''simple docstring'''






       return self._utf_vocab_size + self._num_special_tokens + self._extra_ids





  def 						A  (		self      :	str							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None							,					_a      :	bool = False )	->    List[int]:





       '''simple docstring'''






       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_a							,					token_ids_a=_a							,					already_has_special_tokens=_a )

       # normal case: some special tokens
       if token_ids_a is None:
            return ([0] * len(_a )) + [1]
       return ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]





  def 						A  (		self      :	str							,					_a      :	List[int] )	->    List[int]:





       '''simple docstring'''






       if len(_a ) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
                ' eos tokens being added.' )
            return token_ids
       else:
            return token_ids + [self.eos_token_id]





  def 						A  (		self      :	Union[str, Any]							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.eos_token_id]

       if token_ids_a is None:
            return len(token_ids_a + eos ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos ) * [0]





  def 						A  (		self      :	Optional[int]							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._add_eos_if_not_present(_a )
       if token_ids_a is None:
            return token_ids_a
       else:
            _SCREAMING_SNAKE_CASE							    =self._add_eos_if_not_present(_a )
            return token_ids_a + token_ids_a





  def 						A  (		self      :	List[Any]							,					_a      :	str )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[chr(_a ) for i in text.encode('utf-8' )]
       return tokens





  def 						A  (		self      :	List[Any]							,					_a      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






       if token in self.special_tokens_encoder:
            _SCREAMING_SNAKE_CASE							    =self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
            _SCREAMING_SNAKE_CASE							    =self.added_tokens_encoder[token]
       elif len(_a ) != 1:
            _SCREAMING_SNAKE_CASE							    =self.unk_token_id
       else:
            _SCREAMING_SNAKE_CASE							    =ord(_a ) + self._num_special_tokens
       return token_id





  def 						A  (		self      :	Tuple							,					_a      :	Optional[int] )	->    str:





       '''simple docstring'''






       if index in self.special_tokens_decoder:
            _SCREAMING_SNAKE_CASE							    =self.special_tokens_decoder[index]
       else:
            _SCREAMING_SNAKE_CASE							    =chr(index - self._num_special_tokens )
       return token





  def 						A  (		self      :	int							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =b''
       for token in tokens:
            if token in self.special_tokens_decoder:
                 _SCREAMING_SNAKE_CASE							    =self.special_tokens_decoder[token].encode('utf-8' )
            elif token in self.added_tokens_decoder:
                 _SCREAMING_SNAKE_CASE							    =self.special_tokens_decoder[token].encode('utf-8' )
            elif token in self.special_tokens_encoder:
                 _SCREAMING_SNAKE_CASE							    =token.encode('utf-8' )
            elif token in self.added_tokens_encoder:
                 _SCREAMING_SNAKE_CASE							    =token.encode('utf-8' )
            else:
                 _SCREAMING_SNAKE_CASE							    =bytes([ord(_a )] )
            bstring += tok_string
       _SCREAMING_SNAKE_CASE							    =bstring.decode('utf-8'							,					errors='ignore' )
       return string





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





       '''simple docstring'''






       return ()




from typing import TYPE_CHECKING

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


lowerCAmelCase       =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TapasForMaskedLM',
			    'TapasForQuestionAnswering',
			    'TapasForSequenceClassification',
			    'TapasModel',
			    'TapasPreTrainedModel',
			    'load_tf_weights_in_tapas',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFTapasForMaskedLM',
			    'TFTapasForQuestionAnswering',
			    'TFTapasForSequenceClassification',
			    'TFTapasModel',
			    'TFTapasPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
			from .tokenization_tapas import TapasTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tapas import (
						    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TapasForMaskedLM,
						    TapasForQuestionAnswering,
						    TapasForSequenceClassification,
						    TapasModel,
						    TapasPreTrainedModel,
						    load_tf_weights_in_tapas,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_tapas import (
						    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTapasForMaskedLM,
						    TFTapasForQuestionAnswering,
						    TFTapasForSequenceClassification,
						    TFTapasModel,
						    TFTapasPreTrainedModel,
						)


else:
			import sys

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


