code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  __A :		Union[str, Any]      							=    logging.get_logger(__name__)
  
  
  if is_vision_available():
    import PIL
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[str]      =				['pixel_values']
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = 1 / 255    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         super().__init__(**SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Dict          =							size if size is not None else {"""height""": 2560, """width""": 1920}
         if isinstance(SCREAMING_SNAKE_CASE_    ,      (tuple, list) ):
             # The previous feature extractor size parameter was in (width, height) format
             UpperCamelCase				:   List[Any]          =							size[::-1]
         UpperCamelCase				:   int          =							get_size_dict(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Any          =							do_resize
         UpperCamelCase				:   Any          =							size
         UpperCamelCase				:   Optional[Any]          =							resample
         UpperCamelCase				:   List[str]          =							do_thumbnail
         UpperCamelCase				:   Union[str, Any]          =							do_align_long_axis
         UpperCamelCase				:   Tuple          =							do_pad
         UpperCamelCase				:   List[str]          =							do_rescale
         UpperCamelCase				:   Union[str, Any]          =							rescale_factor
         UpperCamelCase				:   Tuple          =							do_normalize
         UpperCamelCase				:   Optional[int]          =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCamelCase				:   List[Any]          =							image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							get_image_size(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							size["""height"""], size["""width"""]
  
         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
             UpperCamelCase				:   List[str]          =							np.rotaa(SCREAMING_SNAKE_CASE_    ,      3 )
  
         if data_format is not None:
             UpperCamelCase				:   List[Any]          =							to_channel_dimension_format(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         return image
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         UpperCamelCase       ,							UpperCamelCase				:   int          =							size["""height"""], size["""width"""]
         UpperCamelCase       ,							UpperCamelCase				:   str          =							get_image_size(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   str          =							output_width - input_width
         UpperCamelCase				:   List[str]          =							output_height - input_height
  
         if random_padding:
             UpperCamelCase				:   Any          =							np.random.randint(low=0    ,      high=delta_height + 1 )
             UpperCamelCase				:   Any          =							np.random.randint(low=0    ,      high=delta_width + 1 )
         else:
             UpperCamelCase				:   Union[str, Any]          =							delta_height // 2
             UpperCamelCase				:   int          =							delta_width // 2
  
         UpperCamelCase				:   Optional[Any]          =							delta_height - pad_top
         UpperCamelCase				:   int          =							delta_width - pad_left
  
         UpperCamelCase				:   Dict          =							((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      data_format=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead.""" )
         return self.pad_image(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase       ,							UpperCamelCase				:   Optional[Any]          =							get_image_size(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase       ,							UpperCamelCase				:   Any          =							size["""height"""], size["""width"""]
  
         # We always resize to the smallest of either the input or output size.
         UpperCamelCase				:   List[str]          =							min(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							min(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         if height == input_height and width == input_width:
             return image
  
         if input_height > input_width:
             UpperCamelCase				:   Any          =							int(input_width * height / input_height )
         elif input_width > input_height:
             UpperCamelCase				:   Union[str, Any]          =							int(input_height * width / input_width )
  
         return resize(
             SCREAMING_SNAKE_CASE_    ,      size=(height, width)    ,      resample=SCREAMING_SNAKE_CASE_    ,      reducing_gap=2.0    ,      data_format=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   List[str]          =							get_size_dict(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[str]          =							min(size["""height"""]    ,      size["""width"""] )
         UpperCamelCase				:   Any          =							get_resize_output_image_size(SCREAMING_SNAKE_CASE_    ,      size=SCREAMING_SNAKE_CASE_    ,      default_to_square=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							resize(SCREAMING_SNAKE_CASE_    ,      size=SCREAMING_SNAKE_CASE_    ,      resample=SCREAMING_SNAKE_CASE_    ,      data_format=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         return resized_image
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         return rescale(SCREAMING_SNAKE_CASE_    ,      scale=SCREAMING_SNAKE_CASE_    ,      data_format=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         return normalize(SCREAMING_SNAKE_CASE_    ,      mean=SCREAMING_SNAKE_CASE_    ,      std=SCREAMING_SNAKE_CASE_    ,      data_format=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   Optional[int]          =							do_resize if do_resize is not None else self.do_resize
         UpperCamelCase				:   List[str]          =							size if size is not None else self.size
         if isinstance(SCREAMING_SNAKE_CASE_    ,      (tuple, list) ):
             # Previous feature extractor had size in (width, height) format
             UpperCamelCase				:   List[str]          =							size[::-1]
         UpperCamelCase				:   Any          =							get_size_dict(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							resample if resample is not None else self.resample
         UpperCamelCase				:   List[str]          =							do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         UpperCamelCase				:   Union[str, Any]          =							do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         UpperCamelCase				:   Optional[int]          =							do_pad if do_pad is not None else self.do_pad
         UpperCamelCase				:   Any          =							do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase				:   List[str]          =							rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase				:   List[Any]          =							do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase				:   Dict          =							image_mean if image_mean is not None else self.image_mean
         UpperCamelCase				:   Dict          =							image_std if image_std is not None else self.image_std
  
         UpperCamelCase				:   List[str]          =							make_list_of_images(SCREAMING_SNAKE_CASE_ )
  
         if not valid_images(SCREAMING_SNAKE_CASE_ ):
             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:
             raise ValueError("""Size must be specified if do_resize is True.""" )
  
         if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
  
         if do_pad and size is None:
             raise ValueError("""Size must be specified if do_pad 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.
         UpperCamelCase				:   int          =							[to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
  
         if do_align_long_axis:
             UpperCamelCase				:   str          =							[self.align_long_axis(SCREAMING_SNAKE_CASE_    ,      size=SCREAMING_SNAKE_CASE_ ) for image in images]
  
         if do_resize:
             UpperCamelCase				:   int          =							[self.resize(image=SCREAMING_SNAKE_CASE_    ,      size=SCREAMING_SNAKE_CASE_    ,      resample=SCREAMING_SNAKE_CASE_ ) for image in images]
  
         if do_thumbnail:
             UpperCamelCase				:   Union[str, Any]          =							[self.thumbnail(image=SCREAMING_SNAKE_CASE_    ,      size=SCREAMING_SNAKE_CASE_ ) for image in images]
  
         if do_pad:
             UpperCamelCase				:   Union[str, Any]          =							[self.pad_image(image=SCREAMING_SNAKE_CASE_    ,      size=SCREAMING_SNAKE_CASE_    ,      random_padding=SCREAMING_SNAKE_CASE_ ) for image in images]
  
         if do_rescale:
             UpperCamelCase				:   Dict          =							[self.rescale(image=SCREAMING_SNAKE_CASE_    ,      scale=SCREAMING_SNAKE_CASE_ ) for image in images]
  
         if do_normalize:
             UpperCamelCase				:   Optional[int]          =							[self.normalize(image=SCREAMING_SNAKE_CASE_    ,      mean=SCREAMING_SNAKE_CASE_    ,      std=SCREAMING_SNAKE_CASE_ ) for image in images]
  
         UpperCamelCase				:   str          =							[to_channel_dimension_format(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) for image in images]
  
         UpperCamelCase				:   Optional[int]          =							{"""pixel_values""": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE_    ,      tensor_type=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  