from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__magic_name__    		='\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n        ```\n'



@dataclass
class      _A (     _lowerCamelCase				):
  SCREAMING_SNAKE_CASE_						:						Union[str, Any]    =42



class      _A (     _lowerCamelCase				):






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





       '''simple docstring'''





       super().__init__()

       self.register_modules(
           prior=lowercase_ ,					image_encoder=lowercase_ ,					image_processor=lowercase_ ,					scheduler=lowercase_ ,					renderer=lowercase_ ,					)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   Any:





       '''simple docstring'''





       if latents is None:
            UpperCamelCase__					    =   randn_tensor(lowercase_ ,					generator=lowercase_ ,					device=lowercase_ ,					dtype=lowercase_						)
       else:
            if latents.shape != shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
            UpperCamelCase__					    =   latents.to(lowercase_						)

       UpperCamelCase__					    =   latents * scheduler.init_noise_sigma
       return latents






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0						)		->   Optional[Any]:





       '''simple docstring'''





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

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

       UpperCamelCase__					    =   [self.image_encoder, self.prior]
       for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                 cpu_offload(lowercase_ ,					lowercase_						)






  @property
  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       if self.device != torch.device('''meta'''						) or not hasattr(self.image_encoder ,					'''_hf_hook'''						):
            return self.device
       for module in self.image_encoder.modules():
            if (
                hasattr(lowercase_ ,					'''_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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					)		->   Optional[int]:





       '''simple docstring'''





       if isinstance(lowercase_ ,					lowercase_						) and isinstance(image[0] ,					torch.Tensor						):
            UpperCamelCase__					    =   torch.cat(lowercase_ ,					axis=0						) if image[0].ndim == 4 else torch.stack(lowercase_ ,					axis=0						)

       if not isinstance(lowercase_ ,					torch.Tensor						):
            UpperCamelCase__					    =   self.image_processor(lowercase_ ,					return_tensors='''pt'''						).pixel_values[0].unsqueeze(0						)

       UpperCamelCase__					    =   image.to(dtype=self.image_encoder.dtype ,					device=lowercase_						)

       UpperCamelCase__					    =   self.image_encoder(lowercase_						)['''last_hidden_state''']
       UpperCamelCase__					    =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       UpperCamelCase__					    =   image_embeds.repeat_interleave(lowercase_ ,					dim=0						)

       if do_classifier_free_guidance:
            UpperCamelCase__					    =   torch.zeros_like(lowercase_						)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            UpperCamelCase__					    =   torch.cat([negative_image_embeds, image_embeds]						)

       return image_embeds






  @torch.no_grad()
  @replace_example_docstring(lowercase_						)
  def __call__(self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = 1 ,					SCREAMING_SNAKE_CASE_ = 25 ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = 4.0 ,					SCREAMING_SNAKE_CASE_ = 64 ,					SCREAMING_SNAKE_CASE_ = "pil" ,					SCREAMING_SNAKE_CASE_ = True ,					)		->   str:





       '''simple docstring'''





       if isinstance(lowercase_ ,					PIL.Image.Image						):
            UpperCamelCase__					    =   1
       elif isinstance(lowercase_ ,					torch.Tensor						):
            UpperCamelCase__					    =   image.shape[0]
       elif isinstance(lowercase_ ,					lowercase_						) and isinstance(image[0] ,					(torch.Tensor, PIL.Image.Image)						):
            UpperCamelCase__					    =   len(lowercase_						)
       else:
            raise ValueError(
                F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase_						)}"						)

       UpperCamelCase__					    =   self._execution_device

       UpperCamelCase__					    =   batch_size * num_images_per_prompt

       UpperCamelCase__					    =   guidance_scale > 1.0
       UpperCamelCase__					    =   self._encode_image(lowercase_ ,					lowercase_ ,					lowercase_ ,					lowercase_						)

       # prior

       self.scheduler.set_timesteps(lowercase_ ,					device=lowercase_						)
       UpperCamelCase__					    =   self.scheduler.timesteps

       UpperCamelCase__					    =   self.prior.config.num_embeddings
       UpperCamelCase__					    =   self.prior.config.embedding_dim

       UpperCamelCase__					    =   self.prepare_latents(
           (batch_size, num_embeddings * embedding_dim) ,					image_embeds.dtype ,					lowercase_ ,					lowercase_ ,					lowercase_ ,					self.scheduler ,					)

       # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
       UpperCamelCase__					    =   latents.reshape(latents.shape[0] ,					lowercase_ ,					lowercase_						)

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

            UpperCamelCase__					    =   self.prior(
                lowercase_ ,					timestep=lowercase_ ,					proj_embedding=lowercase_ ,					).predicted_image_embedding

            # remove the variance
            UpperCamelCase__    ,							UpperCamelCase__					    =   noise_pred.split(
                scaled_model_input.shape[2] ,					dim=2						)  # batch_size, num_embeddings, embedding_dim

            if do_classifier_free_guidance is not None:
                 UpperCamelCase__    ,							UpperCamelCase__					    =   noise_pred.chunk(2						)
                 UpperCamelCase__					    =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

            UpperCamelCase__					    =   self.scheduler.step(
                lowercase_ ,					timestep=lowercase_ ,					sample=lowercase_ ,					).prev_sample

       if output_type == "latent":
            return ShapEPipelineOutput(images=lowercase_						)

       UpperCamelCase__					    =   []
       for i, latent in enumerate(lowercase_						):
            print()
            UpperCamelCase__					    =   self.renderer.decode(
                latent[None, :] ,					lowercase_ ,					size=lowercase_ ,					ray_batch_size=4096 ,					n_coarse_samples=64 ,					n_fine_samples=128 ,					)

            images.append(lowercase_						)

       UpperCamelCase__					    =   torch.stack(lowercase_						)

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

       UpperCamelCase__					    =   images.cpu().numpy()

       if output_type == "pil":
            UpperCamelCase__					    =   [self.numpy_to_pil(lowercase_						) for image in images]

       # Offload last model to CPU
       if hasattr(self ,					'''final_offload_hook'''						) and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

       if not return_dict:
            return (images,)

       return ShapEPipelineOutput(images=lowercase_						)









'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							[False] * len(__UpperCAmelCase      )
					snake_case_		     =							[]
					queue.append(__UpperCAmelCase      )
					snake_case_		     =							True

					while queue:
										snake_case_		     =							queue.pop(0      )
										for ind in range(len(graph[u]      )      ):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(__UpperCAmelCase      )
																				snake_case_		     =							True
																				snake_case_		     =							u

					return visited[t]


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							[-1] * (len(__UpperCAmelCase      ))
					snake_case_		     =							0
					while bfs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							float('''Inf'''      )
										snake_case_		     =							sink

										while s != source:
															# Find the minimum value in select path
															snake_case_		     =							min(__UpperCAmelCase, graph[parent[s]][s]      )
															snake_case_		     =							parent[s]

										max_flow += path_flow
										snake_case_		     =							sink

										while v != source:
															snake_case_		     =							parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															snake_case_		     =							parent[v]
					return max_flow


a    :							List[Any]										=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a						,a    :							int										=    0, 5

print(ford_fulkerson(graph, source, sink))

