

"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


lowerCamelCase__			:					Any							  =					logging.get_logger(__name__)

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

lowerCamelCase__			:					List[Any]							  =					{
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

lowerCamelCase__			:					Dict							  =					{
    '''microsoft/deberta-base''': 5_12,
    '''microsoft/deberta-large''': 5_12,
    '''microsoft/deberta-xlarge''': 5_12,
    '''microsoft/deberta-base-mnli''': 5_12,
    '''microsoft/deberta-large-mnli''': 5_12,
    '''microsoft/deberta-xlarge-mnli''': 5_12,
}

lowerCamelCase__			:					Optional[Any]							  =					{
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}






class       _UpperCAmelCase     (						__a):
			__a	:    str			   =							VOCAB_FILES_NAMES
			__a	:    Any			   =							PRETRAINED_VOCAB_FILES_MAP
			__a	:    List[str]			   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a	:    Any			   =							["""input_ids""", """attention_mask""", """token_type_ids"""]
			__a	:    Optional[Any]			   =							DebertaTokenizer
			def __init__(						self ,						_A=None ,						_A=None ,						_A=None ,						_A="replace" ,						_A="[CLS]" ,						_A="[SEP]" ,						_A="[SEP]" ,						_A="[CLS]" ,						_A="[UNK]" ,						_A="[PAD]" ,						_A="[MASK]" ,						_A=False ,						**_A ,						)      ->      int:






				'''simple docstring'''







				super().__init__(
				    _A ,						_A ,						tokenizer_file=_A ,						errors=_A ,						bos_token=_A ,						eos_token=_A ,						unk_token=_A ,						sep_token=_A ,						cls_token=_A ,						pad_token=_A ,						mask_token=_A ,						add_prefix_space=_A ,						**_A ,						)
				_UpperCAmelCase      :							str       =   kwargs.pop("""add_bos_token""" ,						_A       )

				_UpperCAmelCase      :							Dict       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
				if pre_tok_state.get("""add_prefix_space""" ,						_A       ) != add_prefix_space:
					_UpperCAmelCase      :							int       =   getattr(_A ,						pre_tok_state.pop("""type"""       )       )
					_UpperCAmelCase      :							List[str]       =   add_prefix_space
					_UpperCAmelCase      :							Dict       =   pre_tok_class(**_A       )

				_UpperCAmelCase      :							List[str]       =   add_prefix_space
			@property
			def 							__snake_case	(						self       )      ->      str:






				'''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       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   AddedToken(_A ,						lstrip=_A ,						rstrip=_A       ) if isinstance(_A ,						_A       ) else value
				_UpperCAmelCase      :							List[str]       =   value
			def 							__snake_case	(						self ,						_A ,						_A = None       )      ->      List[int]:






				'''simple docstring'''







				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_UpperCAmelCase      :							List[Any]       =   [self.cls_token_id]
				_UpperCAmelCase      :							int       =   [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep
			def 							__snake_case	(						self ,						_A ,						_A = None       )      ->      List[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   [self.sep_token_id]
				_UpperCAmelCase      :							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       ) * [0] + len(token_ids_a + sep       ) * [1]
			def 							__snake_case	(						self ,						*_A ,						**_A       )      ->      BatchEncoding:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   kwargs.get("""is_split_into_words""" ,						_A       )
				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(*_A ,						**_A       )
			def 							__snake_case	(						self ,						*_A ,						**_A       )      ->      BatchEncoding:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   kwargs.get("""is_split_into_words""" ,						_A       )

				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(*_A ,						**_A       )
			def 							__snake_case	(						self ,						_A ,						_A = None       )      ->      Tuple[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   self._tokenizer.model.save(_A ,						name=_A       )
				return tuple(_A       )
			def 							__snake_case	(						self ,						_A       )      ->      List[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   []
				for is_user, text in conversation.iter_texts():
					input_ids.extend(self.encode(_A ,						add_special_tokens=_A       ) + [self.eos_token_id]       )

				if len(_A       ) > self.model_max_length:
					_UpperCAmelCase      :							Union[str, Any]       =   input_ids[-self.model_max_length :]
				return input_ids








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			:					Any							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





