import copy

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


snake_case__						      =			logging.get_logger(__name__)



class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     A_					    =	"speech-encoder-decoder"
     A_					    =	True



     def __init__(       self					,					**A_    )			->					Tuple:






           """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}'    )

           _lowerCamelCase           =					kwargs.pop('''encoder'''    )
           _lowerCamelCase           =					encoder_config.pop('''model_type'''    )
           _lowerCamelCase           =					kwargs.pop('''decoder'''    )
           _lowerCamelCase           =					decoder_config.pop('''model_type'''    )

           _lowerCamelCase           =					AutoConfig.for_model(__UpperCAmelCase					,					**__UpperCAmelCase    )
           _lowerCamelCase           =					AutoConfig.for_model(__UpperCAmelCase					,					**__UpperCAmelCase    )
           _lowerCamelCase           =					True



     @classmethod
     def 		UpperCamelCase_    (       cls					,					A_					,					A_					,					**A_    )			->					PretrainedConfig:






           """simple docstring"""
           logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''    )
           _lowerCamelCase           =					True
           _lowerCamelCase           =					True

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





     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					copy.deepcopy(self.__dict__    )
           _lowerCamelCase           =					self.encoder.to_dict()
           _lowerCamelCase           =					self.decoder.to_dict()
           _lowerCamelCase           =					self.__class__.model_type
           return output



"""simple docstring"""






from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					int:
     return True






  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'						)
     a   :	Any						=    'Hugging Face大法好，谁用谁知道。'
     a   :	Tuple						=    'Hugging Face大法好,谁用谁知道。<unk>'
     a   :	Optional[int]						=    '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[Any]						=    tokens + [tokenizer.unk_token]

     a   :	str						=    [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					__UpperCAmelCase						)

     a   :	List[Any]						=    tokenizer.decode(__UpperCAmelCase						)
     self.assertEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)





