


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

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


if TYPE_CHECKING:
	from ... import PreTrainedTokenizerBase, TensorType

A							:					Tuple     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):

					'''simple docstring'''





					snake_case_		:				str										=	"""vision-encoder-decoder"""
					snake_case_		:				Union[str, Any]										=	True


					def __init__(						self	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	Dict:




											'''simple docstring'''

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

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

											__snake_case				=		AutoConfig.for_model(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		AutoConfig.for_model(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		True


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    **SCREAMING_SNAKE_CASE	:		int						)			->	PretrainedConfig:




											'''simple docstring'''

											logger.info("Setting `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()     ,    **SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__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






class     UpperCamelCase(  _a   ):

					'''simple docstring'''





					snake_case_		:				str										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	float:




											'''simple docstring'''

											return 1e-4






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}						)






class     UpperCamelCase(  _a   ):

					'''simple docstring'''





					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											__snake_case				=		OrderedDict()
											__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
											__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
											__snake_case				=		{0: "batch", 1: "encoder_sequence"}

											return common_inputs






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		"PreTrainedTokenizerBase"     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional["TensorType"] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											import torch

											__snake_case				=		OrderedDict()

											__snake_case				=		super().generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)

											__snake_case		,					__snake_case				=		dummy_input["input_ids"].shape
											__snake_case				=		(batch, encoder_sequence, self._config.encoder_hidden_size)
											__snake_case				=		dummy_input.pop("input_ids"						)
											__snake_case				=		dummy_input.pop("attention_mask"						)
											__snake_case				=		torch.zeros(SCREAMING_SNAKE_CASE						)

											return common_inputs






class     UpperCamelCase(  _a   ):

					'''simple docstring'''





					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	None:




											'''simple docstring'''

											pass


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig						)			->	OnnxConfig:




											'''simple docstring'''

											return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    SCREAMING_SNAKE_CASE	:		str = "default"						)			->	OnnxConfig:




											'''simple docstring'''

											__snake_case				=		encoder_config.hidden_size
											return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)









import os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





