
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =["pixel_values"]



       def __init__(							self ,     _A = True ,     _A = None ,     _A = PIL.Image.NEAREST ,     _A = False ,     _A = None ,     _A = 1 / 255 ,     _A = False ,     _A = True ,     _A = True ,     _A = None ,     _A = None ,     _A = True ,     **_A ,     )				->				None:
          super().__init__(**_A	)
          SCREAMING_SNAKE_CASE_	=      size if size is not None else {'''height''': 346, '''width''': 346}
          SCREAMING_SNAKE_CASE_	=      get_size_dict(_A	)
          SCREAMING_SNAKE_CASE_	=      crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
          SCREAMING_SNAKE_CASE_	=      get_size_dict(_A ,     param_name='''crop_size'''	)

          SCREAMING_SNAKE_CASE_	=      do_resize
          SCREAMING_SNAKE_CASE_	=      size
          SCREAMING_SNAKE_CASE_	=      resample
          SCREAMING_SNAKE_CASE_	=      do_center_crop
          SCREAMING_SNAKE_CASE_	=      crop_size
          SCREAMING_SNAKE_CASE_	=      do_rescale
          SCREAMING_SNAKE_CASE_	=      rescale_factor
          SCREAMING_SNAKE_CASE_	=      rescale_offset
          SCREAMING_SNAKE_CASE_	=      do_normalize
          SCREAMING_SNAKE_CASE_	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          SCREAMING_SNAKE_CASE_	=      image_std if image_std is not None else IMAGENET_STANDARD_STD
          SCREAMING_SNAKE_CASE_	=      include_top



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A = PIL.Image.NEAREST ,     _A = None ,     **_A ,     )				->				np.ndarray:
          SCREAMING_SNAKE_CASE_	=      get_size_dict(_A	)
          if "height" not in size or "width" not in size:
             raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
          return resize(
              _A ,     size=(size['''height'''], size['''width''']) ,     resample=_A ,     data_format=_A ,     **_A	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A = None ,     **_A ,     )				->				np.ndarray:
          SCREAMING_SNAKE_CASE_	=      get_size_dict(_A	)
          if "height" not in size or "width" not in size:
             raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
          return center_crop(_A ,     size=(size['''height'''], size['''width''']) ,     data_format=_A ,     **_A	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A = True ,     _A = None ,     **_A ,     )				->				int:
          if offset:
             SCREAMING_SNAKE_CASE_	=      (image - 127.5) * scale
             if data_format is not None:
                SCREAMING_SNAKE_CASE_	=      to_channel_dimension_format(_A ,     _A	)
             SCREAMING_SNAKE_CASE_	=      rescaled_image.astype(np.floataa	)
          else:
             SCREAMING_SNAKE_CASE_	=      rescale(_A ,     scale=_A ,     data_format=_A ,     **_A	)
          return rescaled_image



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A = None ,     **_A ,     )				->				np.ndarray:
          return normalize(_A ,     mean=_A ,     std=_A ,     data_format=_A ,     **_A	)




       def     _UpperCamelCase	(							self ,     _A ,     _A = None ,     _A = None ,     _A=None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = None ,     _A = ChannelDimension.FIRST ,     **_A ,     )				->				PIL.Image.Image:
          SCREAMING_SNAKE_CASE_	=      do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_	=      resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_	=      do_center_crop if do_center_crop is not None else self.do_center_crop
          SCREAMING_SNAKE_CASE_	=      do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_	=      rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_	=      rescale_offset if rescale_offset is not None else self.rescale_offset
          SCREAMING_SNAKE_CASE_	=      do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_	=      image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_	=      image_std if image_std is not None else self.image_std
          SCREAMING_SNAKE_CASE_	=      include_top if include_top is not None else self.include_top

          SCREAMING_SNAKE_CASE_	=      size if size is not None else self.size
          SCREAMING_SNAKE_CASE_	=      get_size_dict(_A	)
          SCREAMING_SNAKE_CASE_	=      crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_	=      get_size_dict(_A ,     param_name='''crop_size'''	)

          SCREAMING_SNAKE_CASE_	=      make_list_of_images(_A	)

          if not valid_images(_A	):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

          if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

          if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

          if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

          if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_	=      [to_numpy_array(_A	) for image in images]

          if do_resize:
             SCREAMING_SNAKE_CASE_	=      [self.resize(image=_A ,     size=_A ,     resample=_A	) for image in images]

          if do_center_crop:
             SCREAMING_SNAKE_CASE_	=      [self.center_crop(image=_A ,     size=_A	) for image in images]

          if do_rescale:
             SCREAMING_SNAKE_CASE_	=      [self.rescale(image=_A ,     scale=_A ,     offset=_A	) for image in images]

          if do_normalize:
             SCREAMING_SNAKE_CASE_	=      [self.normalize(image=_A ,     mean=_A ,     std=_A	) for image in images]

          if include_top:
             SCREAMING_SNAKE_CASE_	=      [self.normalize(image=_A ,     mean=[0, 0, 0] ,     std=_A	) for image in images]

          SCREAMING_SNAKE_CASE_	=      [to_channel_dimension_format(_A ,     _A	) for image in images]

          SCREAMING_SNAKE_CASE_	=      {'''pixel_values''': images}
          return BatchFeature(data=_A ,     tensor_type=_A	)




import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


