


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					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   'vision-encoder-decoder'
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   True

					def __init__(     self       :   Any							,					**UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )
										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}'''       )

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

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

					@classmethod
					def   UpperCAmelCase__       (     cls       :   Union[str, Any]							,					UpperCamelCase__       :   PretrainedConfig							,					UpperCamelCase__       :   PretrainedConfig							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""       )
										lowercase_  							=			True
										lowercase_  							=			True

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







					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			copy.deepcopy(self.__dict__       )
										lowercase_  							=			self.encoder.to_dict()
										lowercase_  							=			self.decoder.to_dict()
										lowercase_  							=			self.__class__.model_type
										return output







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[str]						=   version.parse('1.11'   )

					@property
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






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

					@property
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										return 1e-4







					@property
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






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







class        UpperCamelCase__   (	__magic_name__   ):

					@property
					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






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

										return common_inputs







					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   "PreTrainedTokenizerBase"							,					UpperCamelCase__       :   int = -1							,					UpperCamelCase__       :   int = -1							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   Optional["TensorType"] = None							,					):






										'''simple docstring'''






										import torch

										lowercase_  							=			OrderedDict()

										lowercase_  							=			super().generate_dummy_inputs(
										    UpperCamelCase__							,					batch_size=UpperCamelCase__							,					seq_length=UpperCamelCase__							,					is_pair=UpperCamelCase__							,					framework=UpperCamelCase__       )

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

										return common_inputs







class        UpperCamelCase__   (	__magic_name__   ):

					@property
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										pass

					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   PretrainedConfig       ):






										'''simple docstring'''






										return VisionEncoderDecoderEncoderOnnxConfig(UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   PretrainedConfig							,					UpperCamelCase__       :   PretrainedConfig							,					UpperCamelCase__       :   str = "default"       ):






										'''simple docstring'''






										lowercase_  							=			encoder_config.hidden_size
										return VisionEncoderDecoderDecoderOnnxConfig(UpperCamelCase__							,					UpperCamelCase__       )






import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
							a					  =     {
							    'linear': PIL.Image.Resampling.BILINEAR,
							    'bilinear': PIL.Image.Resampling.BILINEAR,
							    'bicubic': PIL.Image.Resampling.BICUBIC,
							    'lanczos': PIL.Image.Resampling.LANCZOS,
							    'nearest': PIL.Image.Resampling.NEAREST,
							}
else:
							a					  =     {
							    'linear': PIL.Image.LINEAR,
							    'bilinear': PIL.Image.BILINEAR,
							    'bicubic': PIL.Image.BICUBIC,
							    'lanczos': PIL.Image.LANCZOS,
							    'nearest': PIL.Image.NEAREST,
							}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			(images / 2 + 0.5).clamp(0						,							1  )
					lowercase_  							=			images.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()
					lowercase_  							=			numpy_to_pil(UpperCAmelCase__  )
					return images



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if images.ndim == 3:
										lowercase_  							=			images[None, ...]
					lowercase_  							=			(images * 2_5_5).round().astype("""uint8"""  )
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										lowercase_  							=			[Image.fromarray(image.squeeze()						,							mode="""L"""  ) for image in images]
					else:
										lowercase_  							=			[Image.fromarray(UpperCAmelCase__  ) for image in images]

					return pil_images


