




'''simple docstring'''

import copy

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


a    :							Optional[Any]										=    logging.get_logger(__name__)






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


				def __init__(						self       :   Dict			,							**lowercase_       :   Tuple   ):
									super().__init__(**lowercase_   )
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									snake_case_		     =							kwargs.pop('''encoder'''   )
									snake_case_		     =							encoder_config.pop('''model_type'''   )
									snake_case_		     =							kwargs.pop('''decoder'''   )
									snake_case_		     =							decoder_config.pop('''model_type'''   )

									from ..auto.configuration_auto import AutoConfig

									snake_case_		     =							AutoConfig.for_model(lowercase_			,							**lowercase_   )
									snake_case_		     =							AutoConfig.for_model(lowercase_			,							**lowercase_   )
									snake_case_		     =							True


				@classmethod
				def     A_					(						cls       :   Union[str, Any]			,							lowercase_       :   List[str]			,							lowercase_       :   List[Any]			,							**lowercase_       :   Any   ):
									logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''   )
									snake_case_		     =							True
									snake_case_		     =							True

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


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							copy.deepcopy(self.__dict__   )
									snake_case_		     =							self.encoder.to_dict()
									snake_case_		     =							self.decoder.to_dict()
									snake_case_		     =							self.__class__.model_type
									return output



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



