





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							:			List[str]         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'vision-encoder-decoder'
     __magic_name__   =			True




     def __init__(    self				,     **snake_case_							):
         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        lowerCAmelCase__		(    cls				,     snake_case_				,     snake_case_				,     **snake_case_							):
         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        lowerCAmelCase__		(    self							):
         _A     =							copy.deepcopy(self.__dict__							)
         _A     =							self.encoder.to_dict()
         _A     =							self.decoder.to_dict()
         _A     =							self.__class__.model_type
         return output







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			version.parse('1.11'	)




     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]							)




     @property
     def        lowerCAmelCase__		(    self							):
         return 1E-4




     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}							)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         _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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):
         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      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return VisionEncoderDecoderEncoderOnnxConfig(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = "default"							):
         _A     =							encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(snake_case_				,     snake_case_							)











from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@staticmethod
						@abstractmethod
						def 			snake_case__		(		__lowercase				:	ArgumentParser		):






							"""simple docstring"""






							raise NotImplementedError()






						@abstractmethod
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							raise NotImplementedError()
