

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  __snake_case					(      SCREAMING_SNAKE_CASE_						):
  __lowerCAmelCase       :		Optional[Any]  	=					CONFIG_NAME

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

  @staticmethod
  def   lowerCAmelCase__       (   _A):






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

      return pil_images

  @staticmethod
  def   lowerCAmelCase__       (   _A):






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

      return images

  @staticmethod
  def   lowerCAmelCase__       (   _A):






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

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

  @staticmethod
  def   lowerCAmelCase__       (   _A):
      SCREAMING_SNAKE_CASE_													=  images.cpu().permute(0  ,						2  ,						3  ,						1).float().numpy()
      return images

  @staticmethod
  def   lowerCAmelCase__       (   _A):
      return 2.0 * images - 1.0

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

  @staticmethod
  def   lowerCAmelCase__       (   _A):
      SCREAMING_SNAKE_CASE_													=  image.convert('RGB')
      return image

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						):






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

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

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  (PIL.Image.Image, np.ndarray, torch.Tensor)
      if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_):
          SCREAMING_SNAKE_CASE_													=  [image]
      elif not (isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_) and all(isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_) for i in image)):
          raise ValueError(
              f"""Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_) for i in image]}. Currently, we only support {', '.join(SCREAMING_SNAKE_CASE_)}""")

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

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

      if do_normalize:
          SCREAMING_SNAKE_CASE_													=  self.normalize(SCREAMING_SNAKE_CASE_)

      return image


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = "pil"  ,						_A = None  ,						):






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

      if output_type == "latent":
          return image

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

      SCREAMING_SNAKE_CASE_													=  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

      SCREAMING_SNAKE_CASE_													=  self.pt_to_numpy(SCREAMING_SNAKE_CASE_)

      if output_type == "np":
          return image

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






class  __snake_case					(      SCREAMING_SNAKE_CASE_						):
  __lowerCAmelCase       :		Dict  	=					CONFIG_NAME

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

  @staticmethod
  def   lowerCAmelCase__       (   _A):






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

      return pil_images

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

  def   lowerCAmelCase__       (   self  ,						_A):






      if images.ndim == 3:
          SCREAMING_SNAKE_CASE_													=  images[None, ...]
      SCREAMING_SNAKE_CASE_													=  images[:, :, :, 3:]
      if images.shape[-1] == 6:
          SCREAMING_SNAKE_CASE_													=  (images_depth * 255).round().astype('uint8')
          SCREAMING_SNAKE_CASE_													=  [
              Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE_)  ,						mode='I;16') for image_depth in images_depth
          ]
      elif images.shape[-1] == 4:
          SCREAMING_SNAKE_CASE_													=  (images_depth * 65535.0).astype(np.uintaa)
          SCREAMING_SNAKE_CASE_													=  [Image.fromarray(SCREAMING_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  ,						):






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

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

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

      SCREAMING_SNAKE_CASE_													=  self.pt_to_numpy(SCREAMING_SNAKE_CASE_)

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

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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Queue()
    SCREAMING_SNAKE_CASE_													=  [False] * len(_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  [-1] * len(_SCREAMING_SNAKE_CASE    )

    def bfs():
        while not queue.empty():
            SCREAMING_SNAKE_CASE_													=  queue.get()
            SCREAMING_SNAKE_CASE_													=  True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    SCREAMING_SNAKE_CASE_													=  1 - color[u]
                    queue.put(_SCREAMING_SNAKE_CASE    )

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
        if not visited[i]:
            queue.put(_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
       # Adjacency List of graph
       print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))


