

'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 							_snake_case (       _a	,  _a	,  _a	,  unittest.TestCase						):
			_A       :     Any       =					StableUnCLIPImgaImgPipeline
			_A       :     List[str]       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS
			_A       :     Optional[Any]       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			_A       :     Dict       =					frozenset(
			    []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_A       :     Optional[Any]       =					frozenset([]						)
			def     __UpperCamelCase						( self			:      Union[str, Any]							):
					SCREAMING_SNAKE_CASE:str						      =					32
					SCREAMING_SNAKE_CASE:Optional[int]						      =					embedder_hidden_size

					# image encoding components

					SCREAMING_SNAKE_CASE:List[str]						      =					CLIPImageProcessor(crop_size=32     ,size=32							)

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:str						      =					CLIPVisionModelWithProjection(
					    CLIPVisionConfig(
					        hidden_size=SCREAMING_SNAKE_CASE__     ,projection_dim=SCREAMING_SNAKE_CASE__     ,num_hidden_layers=5     ,num_attention_heads=4     ,image_size=32     ,intermediate_size=37     ,patch_size=1     ,)							)

					# regular denoising components

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:Tuple						      =					StableUnCLIPImageNormalizer(embedding_dim=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					DDPMScheduler(beta_schedule="squaredcos_cap_v2"							)

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:List[Any]						      =					CLIPTextModel(
					    CLIPTextConfig(
					        bos_token_id=0     ,eos_token_id=2     ,hidden_size=SCREAMING_SNAKE_CASE__     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1e-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1_000     ,)							)

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					UNetaDConditionModel(
					    sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")     ,up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")     ,block_out_channels=(32, 64)     ,attention_head_dim=(2, 4)     ,class_embed_type="projection"     ,projection_class_embeddings_input_dim=embedder_projection_dim * 2     ,cross_attention_dim=SCREAMING_SNAKE_CASE__     ,layers_per_block=1     ,upcast_attention=SCREAMING_SNAKE_CASE__     ,use_linear_projection=SCREAMING_SNAKE_CASE__     ,)

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:Tuple						      =					DDIMScheduler(
					    beta_schedule="scaled_linear"     ,beta_start=0.00_085     ,beta_end=0.012     ,prediction_type="v_prediction"     ,set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,steps_offset=1     ,)

					torch.manual_seed(0							)
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					AutoencoderKL()

					SCREAMING_SNAKE_CASE:Optional[int]						      =					{
					    # image encoding components
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder.eval(),
					    # image noising components
					    "image_normalizer": image_normalizer.eval(),
					    "image_noising_scheduler": image_noising_scheduler,
					    # regular denoising components
					    "tokenizer": tokenizer,
					    "text_encoder": text_encoder.eval(),
					    "unet": unet.eval(),
					    "scheduler": scheduler,
					    "vae": vae.eval(),
					}

					return components
			def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Any=0     ,SCREAMING_SNAKE_CASE__			:      Any=True							):
					if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
							SCREAMING_SNAKE_CASE:Dict						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
					else:
							SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:List[Any]						      =					floats_tensor((1, 3, 32, 32)     ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)

					if pil_image:
							SCREAMING_SNAKE_CASE:Dict						      =					input_image * 0.5 + 0.5
							SCREAMING_SNAKE_CASE:str						      =					input_image.clamp(0     ,1							)
							SCREAMING_SNAKE_CASE:Tuple						      =					input_image.cpu().permute(0     ,2     ,3     ,1							).float().numpy()
							SCREAMING_SNAKE_CASE:int						      =					DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE__							)[0]

					return {
					    "prompt": "An anime racoon running a marathon",
					    "image": input_image,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "np",
					}
			@skip_mps
			def     __UpperCamelCase						( self			:      Tuple							):
					SCREAMING_SNAKE_CASE:Dict						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE:List[str]						      =					self.get_dummy_components()
					SCREAMING_SNAKE_CASE:Dict						      =					StableUnCLIPImgaImgPipeline(**SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:str						      =					sd_pipe.to(SCREAMING_SNAKE_CASE__							)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:Any						      =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
					inputs.update({"image_embeds": None}							)
					SCREAMING_SNAKE_CASE:List[str]						      =					sd_pipe(**SCREAMING_SNAKE_CASE__							).images
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE:str						      =					np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]							)

					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3
			def     __UpperCamelCase						( self			:      str							):
					SCREAMING_SNAKE_CASE:int						      =					torch_device in ["cpu", "mps"]

					self._test_attention_slicing_forward_pass(test_max_difference=SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      List[str]							):
					SCREAMING_SNAKE_CASE:int						      =					torch_device in ["cpu", "mps"]

					self._test_inference_batch_single_identical(test_max_difference=SCREAMING_SNAKE_CASE__							)







			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()     ,reason="XFormers attention is only available with CUDA and `xformers` installed"     ,)
			def     __UpperCamelCase						( self			:      List[str]							):
					self._test_xformers_attention_forwardGenerator_pass(test_max_difference=SCREAMING_SNAKE_CASE__							)





@slow
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
			def     __UpperCamelCase						( self			:      List[str]							):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
			def     __UpperCamelCase						( self			:      Union[str, Any]							):
					SCREAMING_SNAKE_CASE:Any						      =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

					SCREAMING_SNAKE_CASE:Optional[Any]						      =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"							)

					SCREAMING_SNAKE_CASE:List[Any]						      =					StableUnCLIPImgaImgPipeline.from_pretrained(
					    "fusing/stable-unclip-2-1-l-img2img"     ,torch_dtype=torch.floataa							)
					pipe.to(SCREAMING_SNAKE_CASE__							)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
					# stable unclip will oom when integration tests are run on a V100,
					# so turn on memory savings
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE:Dict						      =					torch.Generator(device="cpu"							).manual_seed(0							)
					SCREAMING_SNAKE_CASE:str						      =					pipe(SCREAMING_SNAKE_CASE__     ,"anime turle"     ,generator=SCREAMING_SNAKE_CASE__     ,output_type="np"							)

					SCREAMING_SNAKE_CASE:List[Any]						      =					output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      List[Any]							):
					SCREAMING_SNAKE_CASE:List[str]						      =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

					SCREAMING_SNAKE_CASE:Any						      =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"							)

					SCREAMING_SNAKE_CASE:int						      =					StableUnCLIPImgaImgPipeline.from_pretrained(
					    "fusing/stable-unclip-2-1-h-img2img"     ,torch_dtype=torch.floataa							)
					pipe.to(SCREAMING_SNAKE_CASE__							)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
					# stable unclip will oom when integration tests are run on a V100,
					# so turn on memory savings
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.Generator(device="cpu"							).manual_seed(0							)
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					pipe(SCREAMING_SNAKE_CASE__     ,"anime turle"     ,generator=SCREAMING_SNAKE_CASE__     ,output_type="np"							)

					SCREAMING_SNAKE_CASE:int						      =					output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)







			def     __UpperCamelCase						( self			:      List[str]							):
					SCREAMING_SNAKE_CASE:Any						      =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

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

					SCREAMING_SNAKE_CASE:Dict						      =					StableUnCLIPImgaImgPipeline.from_pretrained(
					    "fusing/stable-unclip-2-1-h-img2img"     ,torch_dtype=torch.floataa							)
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					pipe.to(SCREAMING_SNAKE_CASE__							)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE:int						      =					pipe(
					    SCREAMING_SNAKE_CASE__     ,"anime turtle"     ,num_inference_steps=2     ,output_type="np"     ,)

					SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.cuda.max_memory_allocated()
					# make sure that less than 7 GB is allocated
					assert mem_bytes < 7 * 10**9









'''simple docstring'''

import random





def    A_  ( snake_case      ,       snake_case      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:dict						      =					{i: [] for i in range(snake_case )}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(snake_case )
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(snake_case ):
    for j in range(i + 1      ,       snake_case ):
      if random.random() < probability:
        graph[i].append(snake_case )
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(snake_case )
  return graph





def    A_  ( snake_case ):
  return {
      i: [j for j in range(snake_case ) if i != j] for i in range(snake_case )
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()






