

'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
    import torch


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     List[str]       =					["""pixel_values"""]
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Dict = True     ,SCREAMING_SNAKE_CASE__			:      Dict = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any] = PILImageResampling.BILINEAR     ,SCREAMING_SNAKE_CASE__			:      Optional[Any] = True     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      Any = True     ,SCREAMING_SNAKE_CASE__			:      str = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      List[Any] = True     ,SCREAMING_SNAKE_CASE__			:      str = None     ,SCREAMING_SNAKE_CASE__			:      int = None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     super().__init__(**snake_case_							)
     SCREAMING_SNAKE_CASE:List[str]						      =					size if size is not None else {"shortest_edge": 256}
     SCREAMING_SNAKE_CASE:Optional[int]						      =					get_size_dict(snake_case_     ,default_to_square=snake_case_							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:Dict						      =					get_size_dict(snake_case_     ,param_name="crop_size"							)
     SCREAMING_SNAKE_CASE:Any						      =					do_resize
     SCREAMING_SNAKE_CASE:Tuple						      =					size
     SCREAMING_SNAKE_CASE:int						      =					resample
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_center_crop
     SCREAMING_SNAKE_CASE:int						      =					crop_size
     SCREAMING_SNAKE_CASE:List[Any]						      =					do_rescale
     SCREAMING_SNAKE_CASE:int						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Tuple						      =					do_normalize
     SCREAMING_SNAKE_CASE:List[str]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any] = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Dict = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(snake_case_     ,default_to_square=snake_case_							)
     if "shortest_edge" not in size:
       raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					get_resize_output_image_size(snake_case_     ,size=size["shortest_edge"]     ,default_to_square=snake_case_							)
     return resize(snake_case_     ,size=snake_case_     ,resample=snake_case_     ,data_format=snake_case_     ,**snake_case_							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      List[str] = None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					get_size_dict(snake_case_							)
     if "height" not in size or "width" not in size:
       raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''							)
     return center_crop(snake_case_     ,size=(size["height"], size["width"])     ,data_format=snake_case_     ,**snake_case_							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int = None     ,**SCREAMING_SNAKE_CASE__			:      int							):
     return rescale(snake_case_     ,scale=snake_case_     ,data_format=snake_case_     ,**snake_case_							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[str] = None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     return normalize(snake_case_     ,mean=snake_case_     ,std=snake_case_     ,data_format=snake_case_     ,**snake_case_							)
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any] = None     ,SCREAMING_SNAKE_CASE__			:      List[str] = None     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any] = None     ,SCREAMING_SNAKE_CASE__			:      List[str] = None     ,SCREAMING_SNAKE_CASE__			:      Dict = None     ,SCREAMING_SNAKE_CASE__			:      List[Any] = None     ,SCREAMING_SNAKE_CASE__			:      Tuple = None     ,SCREAMING_SNAKE_CASE__			:      Dict = None     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      Dict = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     SCREAMING_SNAKE_CASE:List[str]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:str						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(snake_case_     ,default_to_square=snake_case_							)
     SCREAMING_SNAKE_CASE:List[str]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:Tuple						      =					do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE:Tuple						      =					crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE:Optional[int]						      =					get_size_dict(snake_case_     ,param_name="crop_size"							)
     SCREAMING_SNAKE_CASE:Any						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:int						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:Any						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:int						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:str						      =					make_list_of_images(snake_case_							)

     if not valid_images(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_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:Union[str, Any]						      =					[to_numpy_array(snake_case_							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:str						      =					[self.resize(image=snake_case_     ,size=snake_case_     ,resample=snake_case_							) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.center_crop(image=snake_case_     ,size=snake_case_							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:List[Any]						      =					[self.rescale(image=snake_case_     ,scale=snake_case_							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.normalize(image=snake_case_     ,mean=snake_case_     ,std=snake_case_							) for image in images]

     SCREAMING_SNAKE_CASE:Optional[int]						      =					[to_channel_dimension_format(snake_case_     ,snake_case_							) for image in images]

     SCREAMING_SNAKE_CASE:Optional[int]						      =					{"pixel_values": images}
     return BatchFeature(data=snake_case_     ,tensor_type=snake_case_							)







   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str = None							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(snake_case_							) != len(snake_case_							):
         raise ValueError(
             "Make sure that you pass in as many target sizes as the batch dimension of the logits"							)

       if is_torch_tensor(snake_case_							):
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					target_sizes.numpy()

       SCREAMING_SNAKE_CASE:Tuple						      =					[]

       for idx in range(len(snake_case_							)							):
         SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0							)     ,size=target_sizes[idx]     ,mode="bilinear"     ,align_corners=snake_case_							)
         SCREAMING_SNAKE_CASE:int						      =					resized_logits[0].argmax(dim=0							)
         semantic_segmentation.append(snake_case_							)
     else:
       SCREAMING_SNAKE_CASE:Tuple						      =					logits.argmax(dim=1							)
       SCREAMING_SNAKE_CASE:str						      =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

     return semantic_segmentation





'''simple docstring'''

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    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					(images / 2 + 0.5).clamp(0      ,       1 )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					images.cpu().permute(0      ,       2      ,       3      ,       1 ).float().numpy()
  SCREAMING_SNAKE_CASE:str						      =					numpy_to_pil(snake_case )
  return images





def    A_  ( snake_case ):
  if images.ndim == 3:
    SCREAMING_SNAKE_CASE:List[str]						      =					images[None, ...]
  SCREAMING_SNAKE_CASE:List[Any]						      =					(images * 255).round().astype("uint8" )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE:Any						      =					[Image.fromarray(image.squeeze()      ,       mode="L" ) for image in images]
  else:
    SCREAMING_SNAKE_CASE:int						      =					[Image.fromarray(snake_case ) for image in images]

  return pil_images






