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

UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		="""vision-encoder-decoder"""
     _SCREAMING_SNAKE_CASE     :     List[str]		=True



     def __init__(					self				,      **lowerCAmelCase__       ):
            super().__init__(**lowerCAmelCase__       )
            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(lowerCAmelCase__				,      **lowerCAmelCase__       )
            _A=	AutoConfig.for_model(lowerCAmelCase__				,      **lowerCAmelCase__       )
            _A=	True



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            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()				,      **lowerCAmelCase__       )





     def 			a__  (					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						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=version.parse("""1.11"""			)



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



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





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





class      lowerCAmelCase						(					_a			):



     @property
     def 			a__  (					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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = -1				,      lowerCAmelCase__ = -1				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = None				,      ):
            import torch

            _A=	OrderedDict()

            _A=	super().generate_dummy_inputs(
                lowerCAmelCase__				,      batch_size=lowerCAmelCase__				,      seq_length=lowerCAmelCase__				,      is_pair=lowerCAmelCase__				,      framework=lowerCAmelCase__       )

            _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(lowerCAmelCase__       )

            return common_inputs





class      lowerCAmelCase						(					_a			):



     @property
     def 			a__  (					self       ):
            pass



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





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

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            warnings.warn(
                'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                ' Please use PerceiverImageProcessor instead.'				,      lowerCAmelCase__				,      )
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )