
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A     :	Dict    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    '''encoder-decoder'''
				snake_case_						   =    True




				def __init__(     self		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								assert (
								    "encoder" in kwargs and "decoder" in kwargs
								), "Config has to be initialized with encoder and decoder config"
								__a										=    kwargs.pop('''encoder'''				)
								__a										=    encoder_config.pop('''model_type'''				)
								__a										=    kwargs.pop('''decoder'''				)
								__a										=    decoder_config.pop('''model_type'''				)

								from ..auto.configuration_auto import AutoConfig

								__a										=    AutoConfig.for_model(_snake_case		,     **_snake_case				)
								__a										=    AutoConfig.for_model(_snake_case		,     **_snake_case				)
								__a										=    True




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      PretrainedConfig:


								'''simple docstring'''




								logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
								__a										=    True
								__a										=    True

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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

'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset







@pytest.mark.parametrize('''dataset_size'''		,				[None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,				['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if input_in_memory_max_size != "default":
        monkeypatch.setattr(datasets.config		,				'''IN_MEMORY_MAX_SIZE'''		,				snake_case__						)
    A       :   Dict					=						datasets.config.IN_MEMORY_MAX_SIZE
    if input_in_memory_max_size == "default":
        assert in_memory_max_size == 0
    else:
        assert in_memory_max_size == input_in_memory_max_size
    if dataset_size and in_memory_max_size:
        A       :   Dict					=						dataset_size < in_memory_max_size
    else:
        A       :   Tuple					=						False
    A       :   int					=						is_small_dataset(snake_case__						)
    assert result == expected




