

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

_lowerCAmelCase					:			Any  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'vision-encoder-decoder'
			SCREAMING_SNAKE_CASE							   =						True





			def __init__(		self					,  **__snake_case					)   -> List[str]:




						'''simple docstring'''
						super().__init__(**__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}'					)

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

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





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  __snake_case					,  **__snake_case					)   -> PretrainedConfig:




						'''simple docstring'''
						logger.info('Setting `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 		__magic_name__    (		self					)   -> 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






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						version.parse('1.11'      )





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




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





			@property
			def 		__magic_name__    (		self					)   -> float:




						'''simple docstring'''
						return 1e-4





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




						'''simple docstring'''
						return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}					)






class 	__magic_name__    (			lowerCAmelCase_      ):





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




						'''simple docstring'''
						__a        =OrderedDict()
						__a        ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
						__a        ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
						__a        ={0: 'batch', 1: 'encoder_sequence'}

						return common_inputs





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = -1					,  __snake_case = -1					,  __snake_case = False					,  __snake_case = None					,  )   -> Mapping[str, Any]:




						'''simple docstring'''
						import torch

						__a        =OrderedDict()

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

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

						return common_inputs






class 	__magic_name__    (			lowerCAmelCase_      ):





			@property
			def 		__magic_name__    (		self					)   -> None:




						'''simple docstring'''
						pass





			def 		__magic_name__    (		self					,  __snake_case					)   -> OnnxConfig:




						'''simple docstring'''
						return VisionEncoderDecoderEncoderOnnxConfig(__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case = "default"					)   -> OnnxConfig:




						'''simple docstring'''
						__a        =encoder_config.hidden_size
						return VisionEncoderDecoderDecoderOnnxConfig(__snake_case					,  __snake_case					)









_lowerCAmelCase					:			Optional[int]  				=			"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_lowerCAmelCase					:			Tuple  				=			[{"type": "code", "content": INSTALL_CONTENT}]
_lowerCAmelCase					:			Optional[int]  				=			{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






