
"""simple docstring"""






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class      _SCREAMING_SNAKE_CASE   (     UpperCAmelCase__				):
			UpperCAmelCase_    :Union[str, Any]												=		CONFIG_NAME






			@register_to_config
			def __init__(   self			,  __A = True			,  __A = 8			,  __A = "lanczos"			,  __A = True			,  __A = False			,  )     ->	Union[str, Any]:
						super().__init__()






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	str:

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

						return pil_images






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Any:

						if not isinstance(snake_case_			,  snake_case_			):
									lowerCAmelCase_       :int     	=    [images]
						lowerCAmelCase_       :int     	=    [np.array(snake_case_			).astype(np.floataa			) / 255.0 for image in images]
						lowerCAmelCase_       :Tuple     	=    np.stack(snake_case_			,  axis=0			)

						return images






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	int:

						if images.ndim == 3:
									lowerCAmelCase_       :Any     	=    images[..., None]

						lowerCAmelCase_       :Dict     	=    torch.from_numpy(images.transpose(0			,  3			,  1			,  2			)			)
						return images






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Optional[int]:
						lowerCAmelCase_       :Optional[Any]     	=    images.cpu().permute(0			,  2			,  3			,  1			).float().numpy()
						return images






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Any:
						return 2.0 * images - 1.0






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Any:
						return (images / 2 + 0.5).clamp(0			,  1			)






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Union[str, Any]:
						lowerCAmelCase_       :List[Any]     	=    image.convert("""RGB"""			)
						return image






			def 					__lowerCAmelCase					(   self			,  __A			,  __A = None			,  __A = None			,  )     ->	Optional[int]:

						if height is None:
									lowerCAmelCase_       :int     	=    image.height
						if width is None:
									lowerCAmelCase_       :List[Any]     	=    image.width

						lowerCAmelCase_       :Dict     	=    (
						    x - x % self.config.vae_scale_factor for x in (width, height)
						)  # resize to integer multiple of vae_scale_factor
						lowerCAmelCase_       :Any     	=    image.resize((width, height)			,  resample=PIL_INTERPOLATION[self.config.resample]			)
						return image






			def 					__lowerCAmelCase					(   self			,  __A			,  __A = None			,  __A = None			,  )     ->	str:
						lowerCAmelCase_       :Dict     	=    (PIL.Image.Image, np.ndarray, torch.Tensor)
						if isinstance(snake_case_			,  snake_case_			):
									lowerCAmelCase_       :Dict     	=    [image]
						elif not (isinstance(snake_case_			,  snake_case_			) and all(isinstance(snake_case_			,  snake_case_			) for i in image			)):
									raise ValueError(
									    f"""Input is in incorrect format: {[type(snake_case_			) for i in image]}. Currently, we only support {", ".join(snake_case_			)}"""			)

						if isinstance(image[0]			,  PIL.Image.Image			):
									if self.config.do_convert_rgb:
												lowerCAmelCase_       :Dict     	=    [self.convert_to_rgb(snake_case_			) for i in image]
									if self.config.do_resize:
												lowerCAmelCase_       :Any     	=    [self.resize(snake_case_			,  snake_case_			,  snake_case_			) for i in image]
									lowerCAmelCase_       :Optional[int]     	=    self.pil_to_numpy(snake_case_			)  # to np
									lowerCAmelCase_       :Dict     	=    self.numpy_to_pt(snake_case_			)  # to pt

						elif isinstance(image[0]			,  np.ndarray			):
									lowerCAmelCase_       :Optional[Any]     	=    np.concatenate(snake_case_			,  axis=0			) if image[0].ndim == 4 else np.stack(snake_case_			,  axis=0			)
									lowerCAmelCase_       :Optional[Any]     	=    self.numpy_to_pt(snake_case_			)
									lowerCAmelCase_       :Tuple     	=    image.shape
									if self.config.do_resize and (
									    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
									):
												raise ValueError(
												    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
												    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

						elif isinstance(image[0]			,  torch.Tensor			):
									lowerCAmelCase_       :int     	=    torch.cat(snake_case_			,  axis=0			) if image[0].ndim == 4 else torch.stack(snake_case_			,  axis=0			)
									lowerCAmelCase_       :str     	=    image.shape

									# don't need any preprocess if the image is latents
									if channel == 4:
												return image

									if self.config.do_resize and (
									    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
									):
												raise ValueError(
												    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
												    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

        # expected range [0,1], normalize to [-1,1]
						lowerCAmelCase_       :Optional[Any]     	=    self.config.do_normalize
						if image.min() < 0:
									warnings.warn(
									    """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
									    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""			,  snake_case_			,  )
									lowerCAmelCase_       :Optional[int]     	=    False

						if do_normalize:
									lowerCAmelCase_       :Any     	=    self.normalize(snake_case_			)

						return image






			def 					__lowerCAmelCase					(   self			,  __A			,  __A = "pil"			,  __A = None			,  )     ->	Optional[Any]:

						if not isinstance(snake_case_			,  torch.Tensor			):
									raise ValueError(
									    f"""Input for postprocessing is in incorrect format: {type(snake_case_			)}. We only support pytorch tensor"""			)
						if output_type not in ["latent", "pt", "np", "pil"]:
									lowerCAmelCase_       :Optional[Any]     	=    (
									    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
									    '`pil`, `np`, `pt`, `latent`'
									)
									deprecate("""Unsupported output_type"""			,  """1.0.0"""			,  snake_case_			,  standard_warn=snake_case_			)
									lowerCAmelCase_       :Dict     	=    'np'

						if output_type == "latent":
									return image

						if do_denormalize is None:
									lowerCAmelCase_       :str     	=    [self.config.do_normalize] * image.shape[0]

						lowerCAmelCase_       :List[Any]     	=    torch.stack(
						    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

						if output_type == "pt":
									return image

						lowerCAmelCase_       :Optional[Any]     	=    self.pt_to_numpy(snake_case_			)

						if output_type == "np":
									return image

						if output_type == "pil":
									return self.numpy_to_pil(snake_case_			)




class      _SCREAMING_SNAKE_CASE   (     UpperCAmelCase__				):
			UpperCAmelCase_    :str												=		CONFIG_NAME






			@register_to_config
			def __init__(   self			,  __A = True			,  __A = 8			,  __A = "lanczos"			,  __A = True			,  )     ->	Dict:
						super().__init__()






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Dict:

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

						return pil_images






			@staticmethod
			def 					__lowerCAmelCase					(   __A			)     ->	Any:
						return image[:, :, 1] * 2**8 + image[:, :, 2]






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	str:

						if images.ndim == 3:
									lowerCAmelCase_       :Union[str, Any]     	=    images[None, ...]
						lowerCAmelCase_       :Tuple     	=    images[:, :, :, 3:]
						if images.shape[-1] == 6:
									lowerCAmelCase_       :Dict     	=    (images_depth * 255).round().astype("""uint8"""			)
									lowerCAmelCase_       :List[Any]     	=    [
									    Image.fromarray(self.rgblike_to_depthmap(snake_case_			)			,  mode="""I;16"""			) for image_depth in images_depth
									]
						elif images.shape[-1] == 4:
									lowerCAmelCase_       :Optional[int]     	=    (images_depth * 6_5535.0).astype(np.uintaa			)
									lowerCAmelCase_       :Union[str, Any]     	=    [Image.fromarray(snake_case_			,  mode="""I;16"""			) for image_depth in images_depth]
						else:
									raise Exception("""Not supported"""			)

						return pil_images






			def 					__lowerCAmelCase					(   self			,  __A			,  __A = "pil"			,  __A = None			,  )     ->	int:

						if not isinstance(snake_case_			,  torch.Tensor			):
									raise ValueError(
									    f"""Input for postprocessing is in incorrect format: {type(snake_case_			)}. We only support pytorch tensor"""			)
						if output_type not in ["latent", "pt", "np", "pil"]:
									lowerCAmelCase_       :Optional[int]     	=    (
									    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
									    '`pil`, `np`, `pt`, `latent`'
									)
									deprecate("""Unsupported output_type"""			,  """1.0.0"""			,  snake_case_			,  standard_warn=snake_case_			)
									lowerCAmelCase_       :Any     	=    'np'

						if do_denormalize is None:
									lowerCAmelCase_       :int     	=    [self.config.do_normalize] * image.shape[0]

						lowerCAmelCase_       :List[str]     	=    torch.stack(
						    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

						lowerCAmelCase_       :Optional[Any]     	=    self.pt_to_numpy(snake_case_			)

						if output_type == "np":
									if image.shape[-1] == 6:
												lowerCAmelCase_       :Dict     	=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]			,  axis=0			)
									else:
												lowerCAmelCase_       :str     	=    image[:, :, :, 3:]
									return image[:, :, :, :3], image_depth

						if output_type == "pil":
									return self.numpy_to_pil(snake_case_			), self.numpy_to_depth(snake_case_			)
						else:
									raise Exception(f"""This type {output_type} is not supported"""			)













"""simple docstring"""




def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    (boundary[1] - boundary[0]) / steps
  A_    :			Optional[int]									=    boundary[0]
  A_    :			Optional[int]									=    boundary[1]
  A_    :			Any									=    make_points(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Dict									=    0.0
  y += (h / 3.0) * f(_UpperCAmelCase   )
  A_    :			Optional[int]									=    2
  for i in x_i:
    y += (h / 3) * (4 - 2 * (cnt % 2)) * f(_UpperCAmelCase   )
    cnt += 1
  y += (h / 3.0) * f(_UpperCAmelCase   )
  return y





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    a + h
  while x < (b - h):
    yield x
    A_    :			Dict									=    x + h





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):  # enter your function here






  """simple docstring"""





  A_    :			List[Any]									=    (x - 0) * (x - 0)
  return y





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			int									=    0.0  # Lower bound of integration
  A_    :			int									=    1.0  # Upper bound of integration
  A_    :			Tuple									=    10.0  # define number of steps or resolution
  A_    :			List[str]									=    [a, b]  # define boundary of integration
  A_    :			Optional[Any]									=    method_a(_UpperCAmelCase					,   _UpperCAmelCase   )
  print(f"""y = {y}"""   )


if __name__ == "__main__":
   main()