

'''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


lowercase__			    =			logging.get_logger(__name__)




class        A_ (				__snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:	List[Any]  		=  ["""pixel_values"""]


     def __init__(		self :     Optional[int]     ,			lowercase_ :     List[str] = True     ,			lowercase_ :     Union[str, Any] = None     ,			lowercase_ :     str = PILImageResampling.BILINEAR     ,			lowercase_ :     Tuple = True     ,			lowercase_ :     Tuple = None     ,			lowercase_ :     List[Any] = True     ,			lowercase_ :     int = 1 / 255     ,			lowercase_ :     Optional[Any] = True     ,			lowercase_ :     Tuple = None     ,			lowercase_ :     Optional[int] = None     ,			**lowercase_ :     Tuple     ,			)						->	Any:
      super().__init__(**a_       )
      UpperCAmelCase       :	Any											= size if size is not None else {'''shortest_edge''': 256}
      UpperCAmelCase       :	Tuple											= get_size_dict(a_     ,			default_to_square=a_       )
      UpperCAmelCase       :	Dict											= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      UpperCAmelCase       :	Union[str, Any]											= get_size_dict(a_     ,			param_name='crop_size'       )
      UpperCAmelCase       :	Optional[int]											= do_resize
      UpperCAmelCase       :	Tuple											= size
      UpperCAmelCase       :	Optional[int]											= resample
      UpperCAmelCase       :	str											= do_center_crop
      UpperCAmelCase       :	str											= crop_size
      UpperCAmelCase       :	Tuple											= do_rescale
      UpperCAmelCase       :	Tuple											= rescale_factor
      UpperCAmelCase       :	Dict											= do_normalize
      UpperCAmelCase       :	List[Any]											= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCAmelCase       :	Union[str, Any]											= image_std if image_std is not None else IMAGENET_STANDARD_STD


     def    UpperCAmelCase_       (		self :     Union[str, Any]     ,			lowercase_ :     List[str]     ,			lowercase_ :     List[Any]     ,			lowercase_ :     Optional[int] = PILImageResampling.BICUBIC     ,			lowercase_ :     int = None     ,			**lowercase_ :     List[Any]     ,			)						->	int:
      UpperCAmelCase       :	Dict											= get_size_dict(a_     ,			default_to_square=a_       )
      if "shortest_edge" not in size:
       raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
      UpperCAmelCase       :	Tuple											= get_resize_output_image_size(a_     ,			size=size['shortest_edge']     ,			default_to_square=a_       )
      return resize(a_     ,			size=a_     ,			resample=a_     ,			data_format=a_     ,			**a_       )


     def    UpperCAmelCase_       (		self :     List[Any]     ,			lowercase_ :     str     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     Dict = None     ,			**lowercase_ :     Optional[Any]     ,			)						->	int:
      UpperCAmelCase       :	List[Any]											= get_size_dict(a_       )
      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(a_     ,			size=(size['height'], size['width'])     ,			data_format=a_     ,			**a_       )


     def    UpperCAmelCase_       (		self :     str     ,			lowercase_ :     List[str]     ,			lowercase_ :     str     ,			lowercase_ :     int = None     ,			**lowercase_ :     Optional[int]       )						->	Tuple:
      return rescale(a_     ,			scale=a_     ,			data_format=a_     ,			**a_       )


     def    UpperCAmelCase_       (		self :     Optional[int]     ,			lowercase_ :     Any     ,			lowercase_ :     str     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     Optional[int] = None     ,			**lowercase_ :     int     ,			)						->	Union[str, Any]:
      return normalize(a_     ,			mean=a_     ,			std=a_     ,			data_format=a_     ,			**a_       )


     def    UpperCAmelCase_       (		self :     Dict     ,			lowercase_ :     Any     ,			lowercase_ :     Optional[int] = None     ,			lowercase_ :     Dict = None     ,			lowercase_ :     Union[str, Any] = None     ,			lowercase_ :     Union[str, Any] = None     ,			lowercase_ :     str = None     ,			lowercase_ :     Tuple = None     ,			lowercase_ :     int = None     ,			lowercase_ :     Dict = None     ,			lowercase_ :     int = None     ,			lowercase_ :     int = None     ,			lowercase_ :     Union[str, Any] = None     ,			lowercase_ :     int = ChannelDimension.FIRST     ,			**lowercase_ :     Optional[Any]     ,			)						->	Optional[Any]:
      UpperCAmelCase       :	int											= do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase       :	Optional[Any]											= size if size is not None else self.size
      UpperCAmelCase       :	Optional[Any]											= get_size_dict(a_     ,			default_to_square=a_       )
      UpperCAmelCase       :	str											= resample if resample is not None else self.resample
      UpperCAmelCase       :	Dict											= do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCAmelCase       :	Optional[int]											= crop_size if crop_size is not None else self.crop_size
      UpperCAmelCase       :	List[Any]											= get_size_dict(a_     ,			param_name='crop_size'       )
      UpperCAmelCase       :	int											= do_rescale if do_rescale is not None else self.do_rescale
      UpperCAmelCase       :	Optional[int]											= rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCAmelCase       :	List[str]											= 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       :	Tuple											= image_std if image_std is not None else self.image_std

      UpperCAmelCase       :	Dict											= 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:
       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.
      UpperCAmelCase       :	List[str]											= [to_numpy_array(a_       ) for image in images]

      if do_resize:
       UpperCAmelCase       :	List[Any]											= [self.resize(image=a_     ,			size=a_     ,			resample=a_       ) for image in images]

      if do_center_crop:
       UpperCAmelCase       :	Dict											= [self.center_crop(image=a_     ,			size=a_       ) for image in images]

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

      if do_normalize:
       UpperCAmelCase       :	Any											= [self.normalize(image=a_     ,			mean=a_     ,			std=a_       ) for image in images]

      UpperCAmelCase       :	Tuple											= [to_channel_dimension_format(a_     ,			a_       ) for image in images]

      UpperCAmelCase       :	str											= {'''pixel_values''': images}
      return BatchFeature(data=a_     ,			tensor_type=a_       )




     def    UpperCAmelCase_       (		self :     Tuple     ,			lowercase_ :     int     ,			lowercase_ :     Tuple = None       )						->	Union[str, Any]:
      UpperCAmelCase       :	Tuple											= outputs.logits

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

       if is_torch_tensor(a_       ):
        UpperCAmelCase       :	Optional[Any]											= target_sizes.numpy()

       UpperCAmelCase       :	Dict											= []

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

      return semantic_segmentation








"""simple docstring"""
import numpy as np






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				int(np.ceil((x_end - xa) / h     )     )
	__snake_case	:						Dict						=				np.zeros((n + 1,)     )
	__snake_case	:						List[Any]						=				ya
	__snake_case	:						int						=				xa

	for k in range(_snake_case     ):
		__snake_case	:						Any						=				f(_snake_case     ,   y[k]     )
		__snake_case	:						List[Any]						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						int						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						Optional[int]						=				f(x + h     ,   y[k] + h * ka     )
		__snake_case	:						Optional[int]						=				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
		x += h

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()





