




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      :						Tuple							=  logging.get_logger(__name__)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''vision-encoder-decoder'''
    UpperCAmelCase__				    = True


    def __init__(     self :      Tuple   ,					**UpperCAmelCase__ :      List[str])		->Optional[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}""")

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

           A__ 					=       AutoConfig.for_model(UpperCAmelCase__   ,					**UpperCAmelCase__)
           A__ 					=       AutoConfig.for_model(UpperCAmelCase__   ,					**UpperCAmelCase__)
           A__ 					=       True


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Tuple   ,					UpperCAmelCase__ :      PretrainedConfig   ,					UpperCAmelCase__ :      PretrainedConfig   ,					**UpperCAmelCase__ :      List[Any])		->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()   ,					**UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->List[str]:





           '''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 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = version.parse('''1.11'''	)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->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 :      List[Any])		->float:





           '''simple docstring'''

           return 1e-4





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





           '''simple docstring'''

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->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 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      "PreTrainedTokenizerBase"   ,					UpperCAmelCase__ :      int = -1   ,					UpperCAmelCase__ :      int = -1   ,					UpperCAmelCase__ :      bool = False   ,					UpperCAmelCase__ :      Optional["TensorType"] = None   ,					)		->Mapping[str, Any]:





           '''simple docstring'''

           import torch

           A__ 					=       OrderedDict()

           A__ 					=       super().generate_dummy_inputs(
               UpperCAmelCase__   ,					batch_size=UpperCAmelCase__   ,					seq_length=UpperCAmelCase__   ,					is_pair=UpperCAmelCase__   ,					framework=UpperCAmelCase__)

           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(UpperCAmelCase__)

           return common_inputs


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->None:





           '''simple docstring'''

           pass


    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      PretrainedConfig)		->OnnxConfig:





           '''simple docstring'''

           return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      PretrainedConfig   ,					UpperCAmelCase__ :      PretrainedConfig   ,					UpperCAmelCase__ :      str = "default")		->OnnxConfig:





           '''simple docstring'''

           A__ 					=       encoder_config.hidden_size
           return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase__   ,					UpperCAmelCase__)






from PIL import Image




def    a__    (		snake_case__					:							Image						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     (259 * (level + 255)) / (255 * (259 - level))

  def contrast(snake_case__					:							int     ) -> int:
    return int(128 + factor * (c - 128)     )

  return img.point(snake_case__     )


if __name__ == "__main__":
 # Load image
 with Image.open('image_data/lena.jpg') as img:
  # Change contrast to 170
  SCREAMING_SNAKE_CASE__					:     int       =     change_contrast(img, 170)
  cont_img.save('image_data/lena_high_contrast.png', format='png')





