



"""simple docstring"""






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					@cached_property
					def 						snake_case			(			self				:    int	)->   Optional[int]:
								return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''	)



					@cached_property
					def 						snake_case			(			self				:    int	)->   str:
								return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''	)



					def 						snake_case			(			self				:    List[Any]	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.tokenizer_ab
								lowerCamelCase__						:						Tuple						      =''' I am a small frog.'''
								lowerCamelCase__						:						Optional[int]						      =tok([src_text],    padding=lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
								lowerCamelCase__						:						str						      =tok.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase,    clean_up_tokenization_spaces=lowerCamelCase	)[0]
								assert src_text == decoded



					def 						snake_case			(			self				:    Union[str, Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						Any						      =self.rust_tokenizer_ab
								lowerCamelCase__						:						str						      =''' I am a small frog.'''
								lowerCamelCase__						:						Optional[int]						      =tok([src_text],    padding=lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
								lowerCamelCase__						:						Optional[Any]						      =tok.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase,    clean_up_tokenization_spaces=lowerCamelCase	)[0]
								assert src_text == decoded



					def 						snake_case			(			self				:    List[str]	)->   Union[str, Any]:
								assert self.tokenizer_ab.add_prefix_space
								assert self.tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[5502, 2], [5502, 2]]




					def 						snake_case			(			self				:    Optional[Any]	)->   str:
								assert self.rust_tokenizer_ab.add_prefix_space
								assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[5502, 2], [5502, 2]]





"""simple docstring"""


import copy

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


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






class  a__     ( _a						):
   snake_case_				=   "speech-encoder-decoder"
   snake_case_				=   True


   def __init__(  self,					**_UpperCAmelCase	):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)
        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}'''	)

        lowercase__								=				kwargs.pop("encoder"	)
        lowercase__								=				encoder_config.pop("model_type"	)
        lowercase__								=				kwargs.pop("decoder"	)
        lowercase__								=				decoder_config.pop("model_type"	)

        lowercase__								=				AutoConfig.for_model(_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				AutoConfig.for_model(_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				True


   @classmethod
   def 						snake_case__						(  cls,					_UpperCAmelCase,					_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"	)
        lowercase__								=				True
        lowercase__								=				True

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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.encoder.to_dict()
        lowercase__								=				self.decoder.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




