



'''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       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Optional[int]  							=   StableUnCLIPImgaImgPipeline
							lowerCAmelCase			:       Any  							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
							lowerCAmelCase			:       List[str]  							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowerCAmelCase			:       List[Any]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       int  							=   frozenset([]  )







							def 				__lowercase     (				self   :			List[str]						):
									_a	: List[Any]								=				32
									_a	: Tuple								=				embedder_hidden_size

									# image encoding components

									_a	: Any								=				CLIPImageProcessor(crop_size=32      ,size=32						)

									torch.manual_seed(0						)
									_a	: Tuple								=				CLIPVisionModelWithProjection(
									    CLIPVisionConfig(
									        hidden_size=_UpperCAmelCase      ,projection_dim=_UpperCAmelCase      ,num_hidden_layers=5      ,num_attention_heads=4      ,image_size=32      ,intermediate_size=37      ,patch_size=1      ,)						)

									# regular denoising components

									torch.manual_seed(0						)
									_a	: List[Any]								=				StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase						)
									_a	: List[Any]								=				DDPMScheduler(beta_schedule='squaredcos_cap_v2'						)

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

									torch.manual_seed(0						)
									_a	: List[str]								=				CLIPTextModel(
									    CLIPTextConfig(
									        bos_token_id=0      ,eos_token_id=2      ,hidden_size=_UpperCAmelCase      ,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=1000      ,)						)

									torch.manual_seed(0						)
									_a	: List[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=_UpperCAmelCase      ,layers_per_block=1      ,upcast_attention=_UpperCAmelCase      ,use_linear_projection=_UpperCAmelCase      ,)

									torch.manual_seed(0						)
									_a	: List[Any]								=				DDIMScheduler(
									    beta_schedule='scaled_linear'      ,beta_start=0.0_00_85      ,beta_end=0.0_12      ,prediction_type='v_prediction'      ,set_alpha_to_one=_UpperCAmelCase      ,steps_offset=1      ,)

									torch.manual_seed(0						)
									_a	: Any								=				AutoencoderKL()

									_a	: 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 				__lowercase     (				self   :			Dict      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			str=0      ,_UpperCAmelCase   :			Any=True						):
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: Optional[int]								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Union[str, Any]								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)

									_a	: Union[str, Any]								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)

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

									return {
									    "prompt": "An anime racoon running a marathon",
									    "image": input_image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "np",
									}







							@skip_mps
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[Any]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Any								=				self.get_dummy_components()
									_a	: Union[str, Any]								=				StableUnCLIPImgaImgPipeline(**_UpperCAmelCase						)
									_a	: Union[str, Any]								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: List[Any]								=				self.get_dummy_inputs(_UpperCAmelCase						)
									inputs.update({'image_embeds': None}						)
									_a	: Union[str, Any]								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: List[Any]								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_a	: List[Any]								=				np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Any								=				torch_device in ['cpu', 'mps']

									self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Union[str, Any]								=				torch_device in ['cpu', 'mps']

									self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase						)







							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()      ,reason='XFormers attention is only available with CUDA and `xformers` installed'      ,)
							def 				__lowercase     (				self   :			Optional[Any]						):
									self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCAmelCase						)

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







							def 				__lowercase     (				self   :			Union[str, Any]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			Tuple						):
									_a	: int								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

									_a	: str								=				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'						)

									_a	: Optional[int]								=				StableUnCLIPImgaImgPipeline.from_pretrained(
									    'fusing/stable-unclip-2-1-l-img2img'      ,torch_dtype=torch.floataa						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									# 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()

									_a	: Union[str, Any]								=				torch.Generator(device='cpu'						).manual_seed(0						)
									_a	: Dict								=				pipe(_UpperCAmelCase      ,'anime turle'      ,generator=_UpperCAmelCase      ,output_type='np'						)

									_a	: Optional[Any]								=				output.images[0]

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

									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

									_a	: 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'						)

									_a	: Optional[Any]								=				StableUnCLIPImgaImgPipeline.from_pretrained(
									    'fusing/stable-unclip-2-1-h-img2img'      ,torch_dtype=torch.floataa						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									# 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()

									_a	: str								=				torch.Generator(device='cpu'						).manual_seed(0						)
									_a	: Union[str, Any]								=				pipe(_UpperCAmelCase      ,'anime turle'      ,generator=_UpperCAmelCase      ,output_type='np'						)

									_a	: str								=				output.images[0]

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

									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: List[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()

									_a	: Dict								=				StableUnCLIPImgaImgPipeline.from_pretrained(
									    'fusing/stable-unclip-2-1-h-img2img'      ,torch_dtype=torch.floataa						)
									_a	: Optional[Any]								=				pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									_a	: Tuple								=				pipe(
									    _UpperCAmelCase      ,'anime turtle'      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: Union[str, Any]								=				torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9









'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCAmelCase		     =  TypeVar('''T''')

class       __magic_name__		(       Generic[T]  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			bool = True						):
									_a	: dict[T, list[T]]								=				{}  # dictionary of lists
									_a	: Tuple								=				directed







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			T      ,_UpperCAmelCase   :			T						):

									if not self.directed:  # For undirected graphs
											# if both source vertex and destination vertex are both present in the
											# adjacency list, add destination vertex to source vertex list of adjacent
											# vertices and add source vertex to destination vertex list of adjacent
											# vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination vertex
											# to source vertex list of adjacent vertices, then create a new vertex with
											# destination vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: Optional[Any]								=				[source_vertex]
											# if only destination vertex is present in adjacency list, add source vertex
											# to destination vertex list of adjacent vertices, then create a new vertex
											# with source vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif destination_vertex in self.adj_list:
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
													_a	: Union[str, Any]								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and assign a list
											# containing the destination vertex as it's first adjacent vertex also
											# create a new vertex with destination vertex as key and assign a list
											# containing the source vertex as it's first adjacent vertex.
											else:
													_a	: Union[str, Any]								=				[destination_vertex]
													_a	: Optional[int]								=				[source_vertex]
									else:  # For directed graphs
											# if both source vertex and destination vertex are present in adjacency
											# list, add destination vertex to source vertex list of adjacent vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination
											# vertex to source vertex list of adjacent vertices and create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: int								=				[]
											# if only destination vertex is present in adjacency list, create a new
											# vertex with source vertex as key and assign a list containing destination
											# vertex as first adjacent vertex
											elif destination_vertex in self.adj_list:
													_a	: Tuple								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and a list containing
											# destination vertex as it's first adjacent vertex. Then create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											else:
													_a	: Tuple								=				[destination_vertex]
													_a	: str								=				[]

									return self







							def __repr__(				self   :			int						):
									return pformat(self.adj_list						)




