





'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


snake_case_       :    Tuple          =					logging.get_logger(__name__)


class    __a (lowerCamelCase       ):
							__a   :				List[Any]       =    "speech-encoder-decoder"
							__a   :				Optional[Any]       =    True

							def __init__(							self							:       Union[str, Any]    ,       **__magic_name__							:       str				)   ->    int:







													"""simple docstring"""
													super().__init__(**__magic_name__				)
													if "encoder" not in kwargs or "decoder" not in kwargs:
																			raise ValueError(
																			    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
																			    F""" `decoder` sub-configurations are passed, but only {kwargs}"""				)

													UpperCAmelCase_     : str         =					kwargs.pop('''encoder'''				)
													UpperCAmelCase_     : Union[str, Any]         =					encoder_config.pop('''model_type'''				)
													UpperCAmelCase_     : Optional[int]         =					kwargs.pop('''decoder'''				)
													UpperCAmelCase_     : Union[str, Any]         =					decoder_config.pop('''model_type'''				)

													UpperCAmelCase_     : Any         =					AutoConfig.for_model(__magic_name__    ,       **__magic_name__				)
													UpperCAmelCase_     : Tuple         =					AutoConfig.for_model(__magic_name__    ,       **__magic_name__				)
													UpperCAmelCase_     : Tuple         =					True

							@classmethod
							def       UpperCAmelCase__		(							cls							:       Optional[int]    ,       __magic_name__							:       PretrainedConfig    ,       __magic_name__							:       PretrainedConfig    ,       **__magic_name__							:       Optional[int]				)   ->    PretrainedConfig:







													"""simple docstring"""
													logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
													UpperCAmelCase_     : Optional[Any]         =					True
													UpperCAmelCase_     : str         =					True

													return cls(encoder=encoder_config.to_dict()    ,       decoder=decoder_config.to_dict()    ,       **__magic_name__				)



							def       UpperCAmelCase__		(							self							:       str				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					copy.deepcopy(self.__dict__				)
													UpperCAmelCase_     : List[str]         =					self.encoder.to_dict()
													UpperCAmelCase_     : Union[str, Any]         =					self.decoder.to_dict()
													UpperCAmelCase_     : Any         =					self.__class__.model_type
													return output












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





