




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      KandinskyInpaintPipeline
				__UpperCAmelCase :     int       =      ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
				__UpperCAmelCase :     Union[str, Any]       =      [
				    "prompt",
				    "negative_prompt",
				    "image_embeds",
				    "negative_image_embeds",
				    "image",
				    "mask_image",
				]
				__UpperCAmelCase :     Optional[Any]       =      [
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "negative_prompt",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				__UpperCAmelCase :     Optional[int]       =      False



				@property
				def       snake_case    (     self					:	Tuple						):
						return 3_2



				@property
				def       snake_case    (     self					:	Tuple						):
						return 3_2



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return self.time_input_dim



				@property
				def       snake_case    (     self					:	Any						):
						return self.time_input_dim * 4



				@property
				def       snake_case    (     self					:	List[Any]						):
						return 1_0_0



				@property
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Optional[Any]												=				XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
						return tokenizer



				@property
				def       snake_case    (     self					:	int						):
						torch.manual_seed(0						)
						__lowercase				:		Union[str, Any]												=				MCLIPConfig(
						    numDims=self.cross_attention_dim    ,   transformerDimensions=self.text_embedder_hidden_size    ,   hidden_size=self.text_embedder_hidden_size    ,   intermediate_size=3_7    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   vocab_size=1_0_0_5    ,   )

						__lowercase				:		Optional[Any]												=				MultilingualCLIP(lowercase__						)
						__lowercase				:		Dict												=				text_encoder.eval()

						return text_encoder



				@property
				def       snake_case    (     self					:	int						):
						torch.manual_seed(0						)

						__lowercase				:		str												=				{
						    "in_channels": 9,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "text_image",
						    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
						    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
						    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "layers_per_block": 1,
						    "encoder_hid_dim": self.text_embedder_hidden_size,
						    "encoder_hid_dim_type": "text_image_proj",
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": None,
						}

						__lowercase				:		Any												=				UNetaDConditionModel(**lowercase__						)
						return model



				@property
				def       snake_case    (     self					:	Union[str, Any]						):
						return {
						    "block_out_channels": [3_2, 6_4],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



				@property
				def       snake_case    (     self					:	List[Any]						):
						torch.manual_seed(0						)
						__lowercase				:		Tuple												=				VQModel(**self.dummy_movq_kwargs						)
						return model



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Optional[Any]												=				self.dummy_text_encoder
						__lowercase				:		Dict												=				self.dummy_tokenizer
						__lowercase				:		List[str]												=				self.dummy_unet
						__lowercase				:		Tuple												=				self.dummy_movq

						__lowercase				:		Optional[int]												=				DDIMScheduler(
						    num_train_timesteps=1_0_0_0    ,   beta_schedule="linear"    ,   beta_start=0.0_0_0_8_5    ,   beta_end=0.0_1_2    ,   clip_sample=lowercase__    ,   set_alpha_to_one=lowercase__    ,   steps_offset=1    ,   prediction_type="epsilon"    ,   thresholding=lowercase__    ,   )

						__lowercase				:		List[str]												=				{
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	Tuple    ,   lowercase__					:	Union[str, Any]=0						):
						__lowercase				:		List[str]												=				floats_tensor((1, self.cross_attention_dim)    ,   rng=random.Random(lowercase__						)						).to(lowercase__						)
						__lowercase				:		Tuple												=				floats_tensor((1, self.cross_attention_dim)    ,   rng=random.Random(seed + 1						)						).to(lowercase__						)
						# create init_image
						__lowercase				:		Union[str, Any]												=				floats_tensor((1, 3, 6_4, 6_4)    ,   rng=random.Random(lowercase__						)						).to(lowercase__						)
						__lowercase				:		Optional[int]												=				image.cpu().permute(0    ,   2    ,   3    ,   1						)[0]
						__lowercase				:		Optional[Any]												=				Image.fromarray(np.uinta(lowercase__						)						).convert("RGB"						).resize((2_5_6, 2_5_6)						)
						# create mask
						__lowercase				:		List[Any]												=				np.ones((6_4, 6_4)    ,   dtype=np.floataa						)
						__lowercase				:		Optional[int]												=				0

						if str(lowercase__						).startswith("mps"						):
								__lowercase				:		str												=				torch.manual_seed(lowercase__						)
						else:
								__lowercase				:		str												=				torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
						__lowercase				:		Union[str, Any]												=				{
						    "prompt": "horse",
						    "image": init_image,
						    "mask_image": mask,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 6_4,
						    "width": 6_4,
						    "num_inference_steps": 2,
						    "guidance_scale": 4.0,
						    "output_type": "np",
						}
						return inputs



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		Any												=				"cpu"

						__lowercase				:		Optional[Any]												=				self.get_dummy_components()

						__lowercase				:		List[Any]												=				self.pipeline_class(**lowercase__						)
						__lowercase				:		Union[str, Any]												=				pipe.to(lowercase__						)

						pipe.set_progress_bar_config(disable=lowercase__						)

						__lowercase				:		List[str]												=				pipe(**self.get_dummy_inputs(lowercase__						)						)
						__lowercase				:		int												=				output.images

						__lowercase				:		Dict												=				pipe(
						    **self.get_dummy_inputs(lowercase__						)    ,   return_dict=lowercase__    ,   )[0]

						__lowercase				:		str												=				image[0, -3:, -3:, -1]
						__lowercase				:		List[str]												=				image_from_tuple[0, -3:, -3:, -1]

						print(f'image.shape {image.shape}'						)

						assert image.shape == (1, 6_4, 6_4, 3)

						__lowercase				:		Dict												=				np.array(
						    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]						)

						assert (
						    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
						), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
						), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



				def       snake_case    (     self					:	List[Any]						):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3						)





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


				"""simple docstring"""





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



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		str												=				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"						)

						__lowercase				:		List[Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
						__lowercase				:		Optional[Any]												=				np.ones((7_6_8, 7_6_8)    ,   dtype=np.floataa						)
						__lowercase				:		Tuple												=				0

						__lowercase				:		Union[str, Any]												=				"a hat"

						__lowercase				:		Optional[Any]												=				KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"    ,   torch_dtype=torch.floataa						)
						pipe_prior.to(lowercase__						)

						__lowercase				:		Tuple												=				KandinskyInpaintPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-inpaint"    ,   torch_dtype=torch.floataa						)
						__lowercase				:		Optional[Any]												=				pipeline.to(lowercase__						)
						pipeline.set_progress_bar_config(disable=lowercase__						)

						__lowercase				:		Any												=				torch.Generator(device="cpu"						).manual_seed(0						)
						__lowercase				:		str												=				pipe_prior(
						    lowercase__    ,   generator=lowercase__    ,   num_inference_steps=5    ,   negative_prompt=""    ,   ).to_tuple()

						__lowercase				:		int												=				pipeline(
						    lowercase__    ,   image=lowercase__    ,   mask_image=lowercase__    ,   image_embeds=lowercase__    ,   negative_image_embeds=lowercase__    ,   generator=lowercase__    ,   num_inference_steps=1_0_0    ,   height=7_6_8    ,   width=7_6_8    ,   output_type="np"    ,   )

						__lowercase				:		str												=				output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(lowercase__    ,   lowercase__						)







"""simple docstring"""







from collections import deque

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase				:		Any												=				len(_lowerCamelCase				)
		__lowercase				:		str												=				deque()
		__lowercase				:		Tuple												=				[False for _ in range(_lowerCamelCase				)]
		__lowercase				:		Optional[int]												=				[-1 for _ in range(_lowerCamelCase				)]
		__lowercase				:		Dict												=				index_of[:]

		def strong_connect(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		List[str]												=				index  # the number when this node is seen
				__lowercase				:		List[str]												=				index  # lowest rank node reachable from here
				index += 1
				stack.append(_lowerCamelCase				)
				__lowercase				:		List[Any]												=				True

				for w in g[v]:
						if index_of[w] == -1:
								__lowercase				:		Optional[int]												=				strong_connect(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
								__lowercase				:		List[Any]												=				(
								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
								)
						elif on_stack[w]:
								__lowercase				:		List[Any]												=				(
								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
								)

				if lowlink_of[v] == index_of[v]:
						__lowercase				:		Any												=				[]
						__lowercase				:		List[Any]												=				stack.pop()
						__lowercase				:		Any												=				False
						component.append(_lowerCamelCase				)
						while w != v:
								__lowercase				:		List[str]												=				stack.pop()
								__lowercase				:		Dict												=				False
								component.append(_lowerCamelCase				)
						components.append(_lowerCamelCase				)
				return index

		__lowercase				:		Tuple												=				[]
		for v in range(_lowerCamelCase				):
				if index_of[v] == -1:
						strong_connect(_lowerCamelCase,  0,  _lowerCamelCase				)

		return components

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		int												=				[[] for _ in range(_lowerCamelCase				)]
		for u, v in edges:
				g[u].append(_lowerCamelCase				)
		return g


if __name__ == "__main__":
						# Test
						__A   :  int					=      7
						__A   :  int					=      [0, 0, 1, 2, 3, 3, 4, 4, 6]
						__A   :  List[Any]					=      [1, 3, 2, 0, 1, 4, 5, 6, 5]
						__A   :  List[str]					=      [(u, v) for u, v in zip(source, target)]
						__A   :  List[str]					=      create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
