






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 							a_ (						a_		):


							'''simple docstring'''






							__a:	int     =    CONFIG_NAME

							@register_to_config
							def __init__(		self			,			lowercase_ = True			,			lowercase_ = 8			,			lowercase_ = "lanczos"			,			lowercase_ = True			,			lowercase_ = False			,			)					->					Dict:
														'''simple docstring'''

														super().__init__()

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					PIL.Image.Image:
														'''simple docstring'''

														if images.ndim == 3:
																					lowerCAmelCase_										=     images[None, ...]
														lowerCAmelCase_										=     (images * 2_5_5).round().astype('uint8'   )
														if images.shape[-1] == 1:
																					# special case for grayscale (single channel) images
																					lowerCAmelCase_										=     [Image.fromarray(image.squeeze()			,			mode='L'   ) for image in images]
														else:
																					lowerCAmelCase_										=     [Image.fromarray(lowercase_   ) for image in images]

														return pil_images

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					np.ndarray:
														'''simple docstring'''

														if not isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     [images]
														lowerCAmelCase_										=     [np.array(lowercase_   ).astype(np.floataa   ) / 2_55.0 for image in images]
														lowerCAmelCase_										=     np.stack(lowercase_			,			axis=0   )

														return images

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					torch.FloatTensor:
														'''simple docstring'''

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

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

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     images.cpu().permute(0			,			2			,			3			,			1   ).float().numpy()
														return images

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					Dict:
														'''simple docstring'''

														return 2.0 * images - 1.0

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					Optional[int]:
														'''simple docstring'''

														return (images / 2 + 0.5).clamp(0			,			1   )

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     image.convert('RGB'   )
														return image

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														if height is None:
																					lowerCAmelCase_										=     image.height
														if width is None:
																					lowerCAmelCase_										=     image.width

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			)					->					torch.Tensor:
														'''simple docstring'''

														lowerCAmelCase_										=     (PIL.Image.Image, np.ndarray, torch.Tensor)
														if isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     [image]
														elif not (isinstance(lowercase_			,			lowercase_   ) and all(isinstance(lowercase_			,			lowercase_   ) for i in image   )):
																					raise ValueError(
																					    f'''Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {", ".join(lowercase_   )}'''   )

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

														elif isinstance(image[0]			,			np.ndarray   ):
																					lowerCAmelCase_										=     np.concatenate(lowercase_			,			axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_			,			axis=0   )
																					lowerCAmelCase_										=     self.numpy_to_pt(lowercase_   )
																					lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_										=     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_										=     torch.cat(lowercase_			,			axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_			,			axis=0   )
																					lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_										=     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_										=     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()}]'''			,			lowercase_			,			)
																					lowerCAmelCase_										=     False

														if do_normalize:
																					lowerCAmelCase_										=     self.normalize(lowercase_   )

														return image






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = "pil"			,			lowercase_ = None			,			)					->					Tuple:
														'''simple docstring'''

														if not isinstance(lowercase_			,			torch.Tensor   ):
																					raise ValueError(
																					    f'''Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor'''   )
														if output_type not in ["latent", "pt", "np", "pil"]:
																					lowerCAmelCase_										=     (
																					    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'			,			lowercase_			,			standard_warn=lowercase_   )
																					lowerCAmelCase_										=     'np'

														if output_type == "latent":
																					return image

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

														lowerCAmelCase_										=     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_										=     self.pt_to_numpy(lowercase_   )

														if output_type == "np":
																					return image

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




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	List[str]     =    CONFIG_NAME

							@register_to_config
							def __init__(		self			,			lowercase_ = True			,			lowercase_ = 8			,			lowercase_ = "lanczos"			,			lowercase_ = True			,			)					->					Optional[int]:
														'''simple docstring'''

														super().__init__()

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					List[str]:
														'''simple docstring'''

														if images.ndim == 3:
																					lowerCAmelCase_										=     images[None, ...]
														lowerCAmelCase_										=     (images * 2_5_5).round().astype('uint8'   )
														if images.shape[-1] == 1:
																					# special case for grayscale (single channel) images
																					lowerCAmelCase_										=     [Image.fromarray(image.squeeze()			,			mode='L'   ) for image in images]
														else:
																					lowerCAmelCase_										=     [Image.fromarray(image[:, :, :3]   ) for image in images]

														return pil_images

							@staticmethod
							def      _lowercase       (		lowercase_   )					->					List[str]:
														'''simple docstring'''

														return image[:, :, 1] * 2**8 + image[:, :, 2]

							def      _lowercase       (		self			,			lowercase_   )					->					Tuple:
														'''simple docstring'''

														if images.ndim == 3:
																					lowerCAmelCase_										=     images[None, ...]
														lowerCAmelCase_										=     images[:, :, :, 3:]
														if images.shape[-1] == 6:
																					lowerCAmelCase_										=     (images_depth * 2_5_5).round().astype('uint8'   )
																					lowerCAmelCase_										=     [
																					    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )			,			mode='I;16'   ) for image_depth in images_depth
																					]
														elif images.shape[-1] == 4:
																					lowerCAmelCase_										=     (images_depth * 6_55_35.0).astype(np.uintaa   )
																					lowerCAmelCase_										=     [Image.fromarray(lowercase_			,			mode='I;16'   ) for image_depth in images_depth]
														else:
																					raise Exception('Not supported'   )

														return pil_images






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = "pil"			,			lowercase_ = None			,			)					->					Tuple:
														'''simple docstring'''

														if not isinstance(lowercase_			,			torch.Tensor   ):
																					raise ValueError(
																					    f'''Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor'''   )
														if output_type not in ["latent", "pt", "np", "pil"]:
																					lowerCAmelCase_										=     (
																					    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'			,			lowercase_			,			standard_warn=lowercase_   )
																					lowerCAmelCase_										=     'np'

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

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

														lowerCAmelCase_										=     self.pt_to_numpy(lowercase_   )

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

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











import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



