






"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


A__	:    List[str]			 = logging.get_logger(__name__)

A__	:    Optional[Any]			 = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__	:    List[Any]			 = {
    'vocab_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

A__	:    str			 = {
    'yjernite/retribert-base-uncased': 512,
}


A__	:    Dict			 = {
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[Any]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Any             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:List[str]             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:int             =			PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase		:Optional[Any]             =			RetriBertTokenizer
			_UpperCAmelCase		:Any             =			["input_ids", "attention_mask"]


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  Dict=None       ,	snake_case__					:  List[str]=True       ,	snake_case__					:  Optional[Any]="[UNK]"       ,	snake_case__					:  Optional[int]="[SEP]"       ,	snake_case__					:  Optional[Any]="[PAD]"       ,	snake_case__					:  List[str]="[CLS]"       ,	snake_case__					:  Tuple="[MASK]"       ,	snake_case__					:  List[str]=True       ,	snake_case__					:  int=None       ,	**snake_case__					:  str       ,	):
										super().__init__(
										    snake_case__       ,	tokenizer_file=snake_case__       ,	do_lower_case=snake_case__       ,	unk_token=snake_case__       ,	sep_token=snake_case__       ,	pad_token=snake_case__       ,	cls_token=snake_case__       ,	mask_token=snake_case__       ,	tokenize_chinese_chars=snake_case__       ,	strip_accents=snake_case__       ,	**snake_case__       ,	)

										lowerCamelCase_	:							List[str]									=json.loads(self.backend_tokenizer.normalizer.__getstate__() )
										if (
										    normalizer_state.get("lowercase"       ,	snake_case__ ) != do_lower_case
										    or normalizer_state.get("strip_accents"       ,	snake_case__ ) != strip_accents
										    or normalizer_state.get("handle_chinese_chars"       ,	snake_case__ ) != tokenize_chinese_chars
										):
																	lowerCamelCase_	:							str									=getattr(snake_case__       ,	normalizer_state.pop("type" ) )
																	lowerCamelCase_	:							Tuple									=do_lower_case
																	lowerCamelCase_	:							Tuple									=strip_accents
																	lowerCamelCase_	:							Optional[int]									=tokenize_chinese_chars
																	lowerCamelCase_	:							List[Any]									=normalizer_class(**snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=do_lower_case


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  List[Any]=None ):
										lowerCamelCase_	:							List[Any]									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

										return output


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							Tuple									=[self.sep_token_id]
										lowerCamelCase_	:							int									=[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      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  str       ,	snake_case__					:  Optional[str] = None ):
										lowerCamelCase_	:							List[str]									=self._tokenizer.model.save(snake_case__       ,	name=snake_case__ )
										return tuple(snake_case__ )











"""simple docstring"""



from typing import TYPE_CHECKING

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


A__	:    Optional[int]			 = {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = ['Speech2TextTokenizer']

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[Any]			 = ['Speech2TextFeatureExtractor']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = [
						    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFSpeech2TextForConditionalGeneration',
						    'TFSpeech2TextModel',
						    'TFSpeech2TextPreTrainedModel',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[int]			 = [
						    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2TextForConditionalGeneration',
						    'Speech2TextModel',
						    'Speech2TextPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
						from .processing_speech_to_text import SpeechaTextProcessor

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_speech_to_text import SpeechaTextTokenizer

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_speech_to_text import (
												    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSpeechaTextForConditionalGeneration,
												    TFSpeechaTextModel,
												    TFSpeechaTextPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text import (
												    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextForConditionalGeneration,
												    SpeechaTextModel,
												    SpeechaTextPreTrainedModel,
												)

else:
						import sys

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



