





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCAmelCase				:       Any	   =				logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase				:       Tuple	   =				"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=8 )			->     Dict:
    lowerCamelCase__    :      Tuple					 =		height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    lowerCamelCase__    :      Dict					 =		width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor


class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      UNetaDConditionModel			,     UpperCAmelCase  :      DDPMScheduler			,     UpperCAmelCase  :      VQModel			,     )			->	str:
     super().__init__()

     self.register_modules(
         unet=UpperCAmelCase			,     scheduler=UpperCAmelCase			,     movq=UpperCAmelCase			,     )
     lowerCamelCase__    :      Optional[Any]					 =		2 ** (len(self.movq.config.block_out_channels	) - 1)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Tuple	)			->	List[str]:
     if latents is None:
         lowerCamelCase__    :      Dict					 =		randn_tensor(UpperCAmelCase			,     generator=UpperCAmelCase			,     device=UpperCAmelCase			,     dtype=UpperCAmelCase	)
     else:
         if latents.shape != shape:
             raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
         lowerCamelCase__    :      str					 =		latents.to(UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		latents * scheduler.init_noise_sigma
     return latents
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Tuple=0	)			->	List[Any]:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError('Please install accelerate via `pip install accelerate`'	)

     lowerCamelCase__    :      Any					 =		torch.device(F"""cuda:{gpu_id}"""	)

     lowerCamelCase__    :      List[Any]					 =		[
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(UpperCAmelCase			,     UpperCAmelCase	)
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      int=0	)			->	List[Any]:
     if is_accelerate_available() and is_accelerate_version('>='			,     '0.17.0.dev0'	):
         from accelerate import cpu_offload_with_hook
     else:
         raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'	)

     lowerCamelCase__    :      Union[str, Any]					 =		torch.device(F"""cuda:{gpu_id}"""	)

     if self.device.type != "cpu":
         self.to('cpu'			,     silence_dtype_warnings=UpperCAmelCase	)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

     lowerCamelCase__    :      Union[str, Any]					 =		None
     for cpu_offloaded_model in [self.unet, self.movq]:
         lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		cpu_offload_with_hook(UpperCAmelCase			,     UpperCAmelCase			,     prev_module_hook=UpperCAmelCase	)

     # We'll offload the last model manually.
     lowerCamelCase__    :      Optional[int]					 =		hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def      A_	(      self  :      List[str]	)			->	Any:
     if not hasattr(self.unet			,     '_hf_hook'	):
         return self.device
     for module in self.unet.modules():
         if (
             hasattr(UpperCAmelCase			,     '_hf_hook'	)
             and hasattr(module._hf_hook			,     'execution_device'	)
             and module._hf_hook.execution_device is not None
         ):
             return torch.device(module._hf_hook.execution_device	)
     return self.device







 @torch.no_grad()
 @replace_example_docstring(UpperCAmelCase	)
 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[torch.FloatTensor, List[torch.FloatTensor]]			,     UpperCAmelCase  :      Union[torch.FloatTensor, List[torch.FloatTensor]]			,     UpperCAmelCase  :      int = 512			,     UpperCAmelCase  :      int = 512			,     UpperCAmelCase  :      int = 100			,     UpperCAmelCase  :      float = 4.0			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      Optional[Union[torch.Generator, List[torch.Generator]]] = None			,     UpperCAmelCase  :      Optional[torch.FloatTensor] = None			,     UpperCAmelCase  :      Optional[str] = "pil"			,     UpperCAmelCase  :      bool = True			,     )			->	Optional[int]:
     lowerCamelCase__    :      Any					 =		self._execution_device

     lowerCamelCase__    :      Dict					 =		guidance_scale > 1.0

     if isinstance(UpperCAmelCase			,     UpperCAmelCase	):
         lowerCamelCase__    :      Optional[int]					 =		torch.cat(UpperCAmelCase			,     dim=0	)
     lowerCamelCase__    :      int					 =		image_embeds.shape[0] * num_images_per_prompt
     if isinstance(UpperCAmelCase			,     UpperCAmelCase	):
         lowerCamelCase__    :      int					 =		torch.cat(UpperCAmelCase			,     dim=0	)

     if do_classifier_free_guidance:
         lowerCamelCase__    :      int					 =		image_embeds.repeat_interleave(UpperCAmelCase			,     dim=0	)
         lowerCamelCase__    :      Tuple					 =		negative_image_embeds.repeat_interleave(UpperCAmelCase			,     dim=0	)

     lowerCamelCase__    :      Optional[int]					 =		torch.cat([negative_image_embeds, image_embeds]			,     dim=0	).to(dtype=self.unet.dtype			,     device=UpperCAmelCase	)

     self.scheduler.set_timesteps(UpperCAmelCase			,     device=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		self.scheduler.timesteps

     lowerCamelCase__    :      Dict					 =		self.unet.config.in_channels

     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		downscale_height_and_width(UpperCAmelCase			,     UpperCAmelCase			,     self.movq_scale_factor	)

     # create initial latent
     lowerCamelCase__    :      Union[str, Any]					 =		self.prepare_latents(
         (batch_size, num_channels_latents, height, width)			,     image_embeds.dtype			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     self.scheduler			,     )

     for i, t in enumerate(self.progress_bar(UpperCAmelCase	)	):
         # expand the latents if we are doing classifier free guidance
         lowerCamelCase__    :      List[str]					 =		torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

         lowerCamelCase__    :      str					 =		{'image_embeds': image_embeds}
         lowerCamelCase__    :      int					 =		self.unet(
             sample=UpperCAmelCase			,     timestep=UpperCAmelCase			,     encoder_hidden_states=UpperCAmelCase			,     added_cond_kwargs=UpperCAmelCase			,     return_dict=UpperCAmelCase			,     )[0]

         if do_classifier_free_guidance:
             lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		noise_pred.split(latents.shape[1]			,     dim=1	)
             lowerCamelCase__ ,							lowerCamelCase__    :      List[Any]					 =		noise_pred.chunk(2	)
             lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		variance_pred.chunk(2	)
             lowerCamelCase__    :      str					 =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowerCamelCase__    :      List[Any]					 =		torch.cat([noise_pred, variance_pred_text]			,     dim=1	)

         if not (
             hasattr(self.scheduler.config			,     'variance_type'	)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
             lowerCamelCase__ ,							lowerCamelCase__    :      List[Any]					 =		noise_pred.split(latents.shape[1]			,     dim=1	)

         # compute the previous noisy sample x_t -> x_t-1
         lowerCamelCase__    :      Optional[int]					 =		self.scheduler.step(
             UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     generator=UpperCAmelCase			,     )[0]
     # post-processing
     lowerCamelCase__    :      Dict					 =		self.movq.decode(UpperCAmelCase			,     force_not_quantize=UpperCAmelCase	)['sample']

     if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""	)

     if output_type in ["np", "pil"]:
         lowerCamelCase__    :      Optional[int]					 =		image * 0.5 + 0.5
         lowerCamelCase__    :      Optional[int]					 =		image.clamp(0			,     1	)
         lowerCamelCase__    :      List[str]					 =		image.cpu().permute(0			,     2			,     3			,     1	).float().numpy()

     if output_type == "pil":
         lowerCamelCase__    :      Dict					 =		self.numpy_to_pil(UpperCAmelCase	)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=UpperCAmelCase	)












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





