
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_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					str										=	CONFIG_NAME





	@register_to_config
	def __init__(	self		,   snake_case = True		,   snake_case = 8		,   snake_case = "lanczos"		,   snake_case = True		,   snake_case = False		,   ):
		super().__init__()





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		if images.ndim == 3:
			lowercase					  =   images[None, ...]
		lowercase					  =   (images * 255).round().astype('uint8'	)
		if images.shape[-1] == 1:
			# special case for grayscale (single channel) images
			lowercase					  =   [Image.fromarray(image.squeeze()		,   mode='L'	) for image in images]
		else:
			lowercase					  =   [Image.fromarray(snake_case	) for image in images]

		return pil_images





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		if not isinstance(snake_case		,   snake_case	):
			lowercase					  =   [images]
		lowercase					  =   [np.array(snake_case	).astype(np.floataa	) / 255.0 for image in images]
		lowercase					  =   np.stack(snake_case		,   axis=0	)

		return images





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		if images.ndim == 3:
			lowercase					  =   images[..., None]

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





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		lowercase					  =   images.cpu().permute(0		,   2		,   3		,   1	).float().numpy()
		return images





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		return 2.0 * images - 1.0





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		return (images / 2 + 0.5).clamp(0		,   1	)





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		lowercase					  =   image.convert('RGB'	)
		return image





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = None		,   ):
		if height is None:
			lowercase					  =   image.height
		if width is None:
			lowercase					  =   image.width

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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = None		,   ):
		lowercase					  =   (PIL.Image.Image, np.ndarray, torch.Tensor)
		if isinstance(snake_case		,   snake_case	):
			lowercase					  =   [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:
				lowercase					  =   [self.convert_to_rgb(snake_case	) for i in image]
			if self.config.do_resize:
				lowercase					  =   [self.resize(snake_case		,   snake_case		,   snake_case	) for i in image]
			lowercase					  =   self.pil_to_numpy(snake_case	)  # to np
			lowercase					  =   self.numpy_to_pt(snake_case	)  # to pt

		elif isinstance(image[0]		,   np.ndarray	):
			lowercase					  =   np.concatenate(snake_case		,   axis=0	) if image[0].ndim == 4 else np.stack(snake_case		,   axis=0	)
			lowercase					  =   self.numpy_to_pt(snake_case	)
			lowercase  , lowercase  , lowercase  , lowercase					  =   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	):
			lowercase					  =   torch.cat(snake_case		,   axis=0	) if image[0].ndim == 4 else torch.stack(snake_case		,   axis=0	)
			lowercase  , lowercase  , lowercase  , lowercase					  =   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]
		lowercase					  =   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		,   )
			lowercase					  =   False

		if do_normalize:
			lowercase					  =   self.normalize(snake_case	)

		return image





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = "pil"		,   snake_case = None		,   ):
		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"]:
			lowercase					  =   (
			    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	)
			lowercase					  =   'np'

		if output_type == "latent":
			return image

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

		lowercase					  =   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

		lowercase					  =   self.pt_to_numpy(snake_case	)

		if output_type == "np":
			return image

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







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Dict										=	CONFIG_NAME





	@register_to_config
	def __init__(	self		,   snake_case = True		,   snake_case = 8		,   snake_case = "lanczos"		,   snake_case = True		,   ):
		super().__init__()





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		if images.ndim == 3:
			lowercase					  =   images[None, ...]
		lowercase					  =   (images * 255).round().astype('uint8'	)
		if images.shape[-1] == 1:
			# special case for grayscale (single channel) images
			lowercase					  =   [Image.fromarray(image.squeeze()		,   mode='L'	) for image in images]
		else:
			lowercase					  =   [Image.fromarray(image[:, :, :3]	) for image in images]

		return pil_images





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	snake_case	):
		return image[:, :, 1] * 2**8 + image[:, :, 2]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		if images.ndim == 3:
			lowercase					  =   images[None, ...]
		lowercase					  =   images[:, :, :, 3:]
		if images.shape[-1] == 6:
			lowercase					  =   (images_depth * 255).round().astype('uint8'	)
			lowercase					  =   [
			    Image.fromarray(self.rgblike_to_depthmap(snake_case	)		,   mode='I;16'	) for image_depth in images_depth
			]
		elif images.shape[-1] == 4:
			lowercase					  =   (images_depth * 65_535.0).astype(np.uintaa	)
			lowercase					  =   [Image.fromarray(snake_case		,   mode='I;16'	) for image_depth in images_depth]
		else:
			raise Exception('Not supported'	)

		return pil_images





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = "pil"		,   snake_case = None		,   ):
		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"]:
			lowercase					  =   (
			    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	)
			lowercase					  =   'np'

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

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

		lowercase					  =   self.pt_to_numpy(snake_case	)

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



from __future__ import annotations

from queue import Queue







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   {}





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i in self.vertices:
			print(snake_case		,   ' : '		,   ' -> '.join([str(snake_case	) for j in self.vertices[i]]	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if from_vertex in self.vertices:
			self.vertices[from_vertex].append(snake_case	)
		else:
			lowercase					  =   [to_vertex]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   set()

		# create a first in first out queue to store all the vertices for BFS
		lowercase					  =   Queue()

		# mark the source node as visited and enqueue it
		visited.add(snake_case	)
		queue.put(snake_case	)

		while not queue.empty():
			lowercase					  =   queue.get()

			# loop through all adjacent vertex and enqueue it if not yet visited
			for adjacent_vertex in self.vertices[vertex]:
				if adjacent_vertex not in visited:
					queue.put(snake_case	)
					visited.add(snake_case	)
		return visited


if __name__ == "__main__":
	from doctest import testmod

	testmod(verbose=True)

	UpperCAmelCase     				=			Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	# 0  :  1 -> 2
	# 1  :  2
	# 2  :  0 -> 3
	# 3  :  3

	assert sorted(g.bfs(2)) == [0, 1, 2, 3]

