



"""simple docstring"""
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,
)


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

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

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any=8						):
 UpperCAmelCase	:					Any	=	height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCAmelCase	:					Optional[Any]	=	width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      List[Any]:





    '''simple docstring'''




    super().__init__()

    self.register_modules(
        unet=_SCREAMING_SNAKE_CASE      ,     scheduler=_SCREAMING_SNAKE_CASE      ,     movq=_SCREAMING_SNAKE_CASE      ,     )
    UpperCAmelCase	:					Any	=	2 ** (len(self.movq.config.block_out_channels						) - 1)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    if latents is None:
     UpperCAmelCase	:					Optional[Any]	=	randn_tensor(_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     device=_SCREAMING_SNAKE_CASE      ,     dtype=_SCREAMING_SNAKE_CASE						)
    else:
     if latents.shape != shape:
      raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
     UpperCAmelCase	:					Optional[int]	=	latents.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	latents * scheduler.init_noise_sigma
    return latents

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=0						)  ->      Optional[int]:





    '''simple docstring'''




    if is_accelerate_available():
     from accelerate import cpu_offload
    else:
     raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

    UpperCAmelCase	:					Optional[int]	=	torch.device(F"cuda:{gpu_id}"						)

    UpperCAmelCase	:					Dict	=	[
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
      cpu_offload(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=0						)  ->      str:





    '''simple docstring'''




    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."""						)

    UpperCAmelCase	:					int	=	torch.device(F"cuda:{gpu_id}"						)

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

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

    # We'll offload the last model manually.
    UpperCAmelCase	:					List[str]	=	hook

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    if not hasattr(self.unet      ,     """_hf_hook"""						):
     return self.device
    for module in self.unet.modules():
     if (
         hasattr(_SCREAMING_SNAKE_CASE      ,     """_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(_SCREAMING_SNAKE_CASE						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 100      ,     _SCREAMING_SNAKE_CASE = 4.0      ,     _SCREAMING_SNAKE_CASE = 1      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "pil"      ,     _SCREAMING_SNAKE_CASE = True      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self._execution_device

    UpperCAmelCase	:					Optional[Any]	=	guidance_scale > 1.0

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

    if do_classifier_free_guidance:
     UpperCAmelCase	:					Union[str, Any]	=	image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE      ,     dim=0						)
     UpperCAmelCase	:					Dict	=	negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE      ,     dim=0						)

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

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

    UpperCAmelCase	:					Optional[int]	=	self.unet.config.in_channels

    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	downscale_height_and_width(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     self.movq_scale_factor						)

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

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

     UpperCAmelCase	:					Any	=	{"""image_embeds""": image_embeds}
     UpperCAmelCase	:					Dict	=	self.unet(
         sample=_SCREAMING_SNAKE_CASE      ,     timestep=_SCREAMING_SNAKE_CASE      ,     encoder_hidden_states=_SCREAMING_SNAKE_CASE      ,     added_cond_kwargs=_SCREAMING_SNAKE_CASE      ,     return_dict=_SCREAMING_SNAKE_CASE      ,     )[0]

     if do_classifier_free_guidance:
      UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	noise_pred.split(latents.shape[1]      ,     dim=1						)
      UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	noise_pred.chunk(2						)
      UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	variance_pred.chunk(2						)
      UpperCAmelCase	:					Optional[int]	=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
      UpperCAmelCase	:					Optional[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"]
     ):
      UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	noise_pred.split(latents.shape[1]      ,     dim=1						)

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase	:					Optional[int]	=	self.scheduler.step(
         _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     )[0]
    # post-processing
    UpperCAmelCase	:					str	=	self.movq.decode(_SCREAMING_SNAKE_CASE      ,     force_not_quantize=_SCREAMING_SNAKE_CASE						)["""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"]:
     UpperCAmelCase	:					int	=	image * 0.5 + 0.5
     UpperCAmelCase	:					Optional[int]	=	image.clamp(0      ,     1						)
     UpperCAmelCase	:					int	=	image.cpu().permute(0      ,     2      ,     3      ,     1						).float().numpy()

    if output_type == "pil":
     UpperCAmelCase	:					Union[str, Any]	=	self.numpy_to_pil(_SCREAMING_SNAKE_CASE						)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE						)

'''simple docstring'''




class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				Any						,					A						:				str						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Any							      =    graph

             self._normalize_graph(A						,					A       )
             _UpperCAmelCase     :  List[str]							      =    len(A       )
             _UpperCAmelCase     :  Tuple							      =    None




      def      _A	(       self						:				Any						,					A						:				List[Any]						,					A						:				str       ):
             if sources is int:
                    _UpperCAmelCase     :  List[Any]							      =    [sources]
             if sinks is int:
                    _UpperCAmelCase     :  List[Any]							      =    [sinks]

             if len(A       ) == 0 or len(A       ) == 0:
                    return

             _UpperCAmelCase     :  str							      =    sources[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    sinks[0]

             # make fake vertex if there are more
             # than one source or sink
             if len(A       ) > 1 or len(A       ) > 1:
                    _UpperCAmelCase     :  Dict							      =    0
                    for i in sources:
                           max_input_flow += sum(self.graph[i]       )

                    _UpperCAmelCase     :  str							      =    len(self.graph       ) + 1
                    for room in self.graph:
                           room.insert(0						,					0       )
                    self.graph.insert(0						,					[0] * size       )
                    for i in sources:
                           _UpperCAmelCase     :  Optional[Any]							      =    max_input_flow
                    _UpperCAmelCase     :  List[str]							      =    0

                    _UpperCAmelCase     :  str							      =    len(self.graph       ) + 1
                    for room in self.graph:
                           room.append(0       )
                    self.graph.append([0] * size       )
                    for i in sinks:
                           _UpperCAmelCase     :  Dict							      =    max_input_flow
                    _UpperCAmelCase     :  List[Any]							      =    size - 1




      def      _A	(       self						:				Union[str, Any]       ):
             if self.maximum_flow_algorithm is None:
                    raise Exception("You need to set maximum flow algorithm before."       )
             if self.source_index is None or self.sink_index is None:
                    return 0

             self.maximum_flow_algorithm.execute()
             return self.maximum_flow_algorithm.getMaximumFlow()





      def      _A	(       self						:				Tuple						,					A						:				Dict       ):
             _UpperCAmelCase     :  str							      =    algorithm(self       )

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Any						,					A						:				str       ):
             _UpperCAmelCase     :  Optional[int]							      =    flow_network
             _UpperCAmelCase     :  Any							      =    flow_network.verticesCount
             _UpperCAmelCase     :  List[str]							      =    flow_network.sourceIndex
             _UpperCAmelCase     :  Union[str, Any]							      =    flow_network.sinkIndex
             # it's just a reference, so you shouldn't change
             # it in your algorithms, use deep copy before doing that
             _UpperCAmelCase     :  Any							      =    flow_network.graph
             _UpperCAmelCase     :  Union[str, Any]							      =    False




      def      _A	(       self						:				List[str]       ):
             if not self.executed:
                    self._algorithm()
                    _UpperCAmelCase     :  int							      =    True





      def      _A	(       self						:				List[Any]       ):
             pass

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]       ):
             super().__init__(A       )
             # use this to save your result
             _UpperCAmelCase     :  Any							      =    -1





      def      _A	(       self						:				Union[str, Any]       ):
             if not self.executed:
                    raise Exception("You should execute algorithm before using its result!"       )

             return self.maximum_flow

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				int       ):
             super().__init__(A       )

             _UpperCAmelCase     :  List[str]							      =    [[0] * self.verticies_count for i in range(self.verticies_count       )]

             _UpperCAmelCase     :  Union[str, Any]							      =    [0] * self.verticies_count
             _UpperCAmelCase     :  int							      =    [0] * self.verticies_count




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.verticies_count

             # push some substance to graph
             for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]       ):
                    self.preflow[self.source_index][nextvertex_index] += bandwidth
                    self.preflow[nextvertex_index][self.source_index] -= bandwidth
                    self.excesses[nextvertex_index] += bandwidth

             # Relabel-to-front selection rule
             _UpperCAmelCase     :  Optional[int]							      =    [
                 i
                 for i in range(self.verticies_count       )
                 if i != self.source_index and i != self.sink_index
             ]

             # move through list
             _UpperCAmelCase     :  Any							      =    0
             while i < len(A       ):
                    _UpperCAmelCase     :  int							      =    vertices_list[i]
                    _UpperCAmelCase     :  int							      =    self.heights[vertex_index]
                    self.process_vertex(A       )
                    if self.heights[vertex_index] > previous_height:
                           # if it was relabeled, swap elements
                           # and start from 0 index
                           vertices_list.insert(0						,					vertices_list.pop(A       )       )
                           _UpperCAmelCase     :  Union[str, Any]							      =    0
                    else:
                           i += 1

             _UpperCAmelCase     :  List[Any]							      =    sum(self.preflow[self.source_index]       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				str       ):
             while self.excesses[vertex_index] > 0:
                    for neighbour_index in range(self.verticies_count       ):
                           # if it's neighbour and current vertex is higher
                           if (
                               self.graph[vertex_index][neighbour_index]
                               - self.preflow[vertex_index][neighbour_index]
                               > 0
                               and self.heights[vertex_index] > self.heights[neighbour_index]
                           ):
                                  self.push(A						,					A       )

                    self.relabel(A       )




      def      _A	(       self						:				int						,					A						:				Dict						,					A						:				List[str]       ):
             _UpperCAmelCase     :  int							      =    min(
                 self.excesses[from_index]						,					self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,					)
             self.preflow[from_index][to_index] += preflow_delta
             self.preflow[to_index][from_index] -= preflow_delta
             self.excesses[from_index] -= preflow_delta
             self.excesses[to_index] += preflow_delta





      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    None
             for to_index in range(self.verticies_count       ):
                    if (
                        self.graph[vertex_index][to_index]
                        - self.preflow[vertex_index][to_index]
                        > 0
                    ) and (min_height is None or self.heights[to_index] < min_height):
                           _UpperCAmelCase     :  Tuple							      =    self.heights[to_index]

             if min_height is not None:
                    _UpperCAmelCase     :  Optional[Any]							      =    min_height + 1


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  [0]
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [3]
  # graph = [
  #     [0, 0, 4, 6, 0, 0],
  #     [0, 0, 5, 2, 0, 0],
  #     [0, 0, 0, 0, 4, 4],
  #     [0, 0, 0, 0, 6, 6],
  #     [0, 0, 0, 0, 0, 0],
  #     [0, 0, 0, 0, 0, 0],
  # ]
  __SCREAMING_SNAKE_CASE       :     List[Any]     =  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

  # prepare our network
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  FlowNetwork(graph, entrances, exits)
  # set algorithm
  flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
  # and calculate
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  flow_network.find_maximum_flow()

  print(F'maximum flow is {maximum_flow}')

