from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass







@dataclass
class __snake_case    :
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42







class __snake_case    :




   def __init__(   self      :    int	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				[[] for _ in range(_lowercase				)]
     SCREAMING_SNAKE_CASE__      							=				size




   def __getitem__(   self      :    int	,     _lowercase      :    int				):



     """simple docstring"""






     return iter(self._graph[vertex]				)




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     return self._size




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    int	,     _lowercase      :    int	,     _lowercase      :    int				):



     """simple docstring"""






     if weight not in (0, 1):
       raise ValueError("""Edge weight must be either 0 or 1."""				)

     if to_vertex < 0 or to_vertex >= self.size:
       raise ValueError("""Vertex indexes must be in [0; size)."""				)

     self._graph[from_vertex].append(Edge(_lowercase	,     _lowercase				)				)




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    int	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				deque([start_vertex]				)
     SCREAMING_SNAKE_CASE__      							=				[None] * self.size
     SCREAMING_SNAKE_CASE__      							=				0

     while queue:
       SCREAMING_SNAKE_CASE__      							=				queue.popleft()
       SCREAMING_SNAKE_CASE__      							=				distances[current_vertex]
       if current_distance is None:
         continue

       for edge in self[current_vertex]:
         SCREAMING_SNAKE_CASE__      							=				current_distance + edge.weight
         SCREAMING_SNAKE_CASE__      							=				distances[edge.destination_vertex]
         if (
             isinstance(_lowercase	,     _lowercase				)
             and new_distance >= dest_vertex_distance
         ):
           continue
         SCREAMING_SNAKE_CASE__      							=				new_distance
         if edge.weight == 0:
           queue.appendleft(edge.destination_vertex				)
         else:
           queue.append(edge.destination_vertex				)

     if distances[finish_vertex] is None:
       raise ValueError("""No path from start_vertex to finish_vertex."""				)

     return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    str				):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   @property
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				1
     SCREAMING_SNAKE_CASE__      							=				3
     SCREAMING_SNAKE_CASE__      							=				(32, 32)

     SCREAMING_SNAKE_CASE__      							=				floats_tensor((batch_size, num_channels) + sizes	,     rng=random.Random(0				)				).to(_lowercase				)
     return image




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				UNetaDConditionModel(
         block_out_channels=(32, 32, 64)	,     layers_per_block=2	,     sample_size=32	,     in_channels=7	,     out_channels=4	,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")	,     up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")	,     cross_attention_dim=32	,     attention_head_dim=8	,     use_linear_projection=_lowercase	,     only_cross_attention=(True, True, False)	,     num_class_embeds=1_00	,     )
     return model




   @property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				AutoencoderKL(
         block_out_channels=[32, 32, 64]	,     in_channels=3	,     out_channels=3	,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,     latent_channels=4	,     )
     return model




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				CLIPTextConfig(
         bos_token_id=0	,     eos_token_id=2	,     hidden_size=32	,     intermediate_size=37	,     layer_norm_eps=1E-05	,     num_attention_heads=4	,     num_hidden_layers=5	,     pad_token_id=1	,     vocab_size=10_00	,     hidden_act="""gelu"""	,     projection_dim=5_12	,     )
     return CLIPTextModel(_lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler()
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_vae
     SCREAMING_SNAKE_CASE__      							=				self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__      							=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

     SCREAMING_SNAKE_CASE__      							=				self.dummy_image.cpu().permute(0	,     2	,     3	,     1				)[0]
     SCREAMING_SNAKE_CASE__      							=				Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((64, 64)				)

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     )

     SCREAMING_SNAKE_CASE__      							=				output.images

     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     return_dict=_lowercase	,     )[0]

     SCREAMING_SNAKE_CASE__      							=				image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__      							=				image_from_tuple[0, -3:, -3:, -1]

     SCREAMING_SNAKE_CASE__      							=				low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE__      							=				np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]				)

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler()
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_vae
     SCREAMING_SNAKE_CASE__      							=				self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__      							=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

     SCREAMING_SNAKE_CASE__      							=				self.dummy_image.cpu().permute(0	,     2	,     3	,     1				)[0]
     SCREAMING_SNAKE_CASE__      							=				Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((64, 64)				)

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         2 * [prompt]	,     image=2 * [low_res_image]	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images
     assert image.shape[0] == 2

     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     num_images_per_prompt=2	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images
     assert image.shape[0] == 2




   @unittest.skipIf(torch_device != """cuda"""	,     """This test requires a GPU"""				)
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler()
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_vae
     SCREAMING_SNAKE_CASE__      							=				self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__      							=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

     SCREAMING_SNAKE_CASE__      							=				self.dummy_image.cpu().permute(0	,     2	,     3	,     1				)[0]
     SCREAMING_SNAKE_CASE__      							=				Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((64, 64)				)

     # put models in fp16, except vae as it overflows in fp16
     SCREAMING_SNAKE_CASE__      							=				unet.half()
     SCREAMING_SNAKE_CASE__      							=				text_encoder.half()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     num_inference_steps=2	,     output_type="""np"""	,     ).images

     SCREAMING_SNAKE_CASE__      							=				low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""				)
     SCREAMING_SNAKE_CASE__      							=				load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
         """/upsampled_cat.npy"""				)

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline.from_pretrained(_lowercase				)
     pipe.to(_lowercase				)
     pipe.set_progress_bar_config(disable=_lowercase				)
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__      							=				"""a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pipe(
         prompt=_lowercase	,     image=_lowercase	,     generator=_lowercase	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image				).max() < 1E-3




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""				)
     SCREAMING_SNAKE_CASE__      							=				load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
         """/upsampled_cat_fp16.npy"""				)

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline.from_pretrained(
         _lowercase	,     torch_dtype=torch.floataa	,     )
     pipe.to(_lowercase				)
     pipe.set_progress_bar_config(disable=_lowercase				)
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__      							=				"""a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pipe(
         prompt=_lowercase	,     image=_lowercase	,     generator=_lowercase	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image				).max() < 5E-1




   def      __a						(   self      :    Any				):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__      							=				load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""				)

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline.from_pretrained(
         _lowercase	,     torch_dtype=torch.floataa	,     )
     pipe.to(_lowercase				)
     pipe.set_progress_bar_config(disable=_lowercase				)
     pipe.enable_attention_slicing(1				)
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__      							=				"""a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pipe(
         prompt=_lowercase	,     image=_lowercase	,     generator=_lowercase	,     num_inference_steps=5	,     output_type="""np"""	,     )

     SCREAMING_SNAKE_CASE__      							=				torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9
