


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_ (       _a			,unittest.TestCase):
						lowerCamelCase_         =     KandinskyInpaintPipeline
						lowerCamelCase_         =     ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						lowerCamelCase_         =     [
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						lowerCamelCase_         =     [
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCamelCase_         =     False




						@property
						def  _snake_case					(	self						:   Any						)      ->int:






									"""simple docstring"""






									return 32




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






									"""simple docstring"""






									return 32




						@property
						def  _snake_case					(	self						:   int						)      ->Optional[Any]:






									"""simple docstring"""






									return self.time_input_dim




						@property
						def  _snake_case					(	self						:   Tuple						)      ->List[str]:






									"""simple docstring"""






									return self.time_input_dim * 4




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->int:






									"""simple docstring"""






									return 100




						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Dict						  =    XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
									return tokenizer




						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->List[str]:






									"""simple docstring"""






									torch.manual_seed(0						)
									a__ :List[Any]						  =    MCLIPConfig(
									    numDims=self.cross_attention_dim   ,     transformerDimensions=self.text_embedder_hidden_size   ,     hidden_size=self.text_embedder_hidden_size   ,     intermediate_size=37   ,     num_attention_heads=4   ,     num_hidden_layers=5   ,     vocab_size=1005   ,     )

									a__ :Optional[Any]						  =    MultilingualCLIP(__A						)
									a__ :Optional[int]						  =    text_encoder.eval()

									return text_encoder




						@property
						def  _snake_case					(	self						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									torch.manual_seed(0						)

									a__ :Optional[int]						  =    {
									    "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,
									}

									a__ :Dict						  =    UNetaDConditionModel(**__A						)
									return model




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Dict:






									"""simple docstring"""






									return {
									    "block_out_channels": [32, 64],
									    "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": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}




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






									"""simple docstring"""






									torch.manual_seed(0						)
									a__ :Tuple						  =    VQModel(**self.dummy_movq_kwargs						)
									return model




						def  _snake_case					(	self						:   List[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[Any]						  =    self.dummy_text_encoder
									a__ :Any						  =    self.dummy_tokenizer
									a__ :str						  =    self.dummy_unet
									a__ :Optional[Any]						  =    self.dummy_movq

									a__ :List[str]						  =    DDIMScheduler(
									    num_train_timesteps=1000   ,     beta_schedule="linear"   ,     beta_start=0.00_085   ,     beta_end=0.012   ,     clip_sample=__A   ,     set_alpha_to_one=__A   ,     steps_offset=1   ,     prediction_type="epsilon"   ,     thresholding=__A   ,     )

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

									return components




						def  _snake_case					(	self						:   List[Any]   ,     __A						:   Tuple   ,     __A						:   Any=0						)      ->Optional[int]:






									"""simple docstring"""






									a__ :List[Any]						  =    floats_tensor((1, self.cross_attention_dim)   ,     rng=random.Random(__A						)						).to(__A						)
									a__ :Any						  =    floats_tensor((1, self.cross_attention_dim)   ,     rng=random.Random(seed + 1						)						).to(__A						)
									# create init_image
									a__ :Dict						  =    floats_tensor((1, 3, 64, 64)   ,     rng=random.Random(__A						)						).to(__A						)
									a__ :Optional[Any]						  =    image.cpu().permute(0   ,     2   ,     3   ,     1						)[0]
									a__ :Tuple						  =    Image.fromarray(np.uinta(__A						)						).convert("RGB"						).resize((256, 256)						)
									# create mask
									a__ :List[str]						  =    np.ones((64, 64)   ,     dtype=np.floataa						)
									a__ :Optional[int]						  =    0

									if str(__A						).startswith("mps"						):
												a__ :Union[str, Any]						  =    torch.manual_seed(__A						)
									else:
												a__ :Any						  =    torch.Generator(device=__A						).manual_seed(__A						)
									a__ :Optional[int]						  =    {
									    "prompt": "horse",
									    "image": init_image,
									    "mask_image": mask,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 64,
									    "width": 64,
									    "num_inference_steps": 2,
									    "guidance_scale": 4.0,
									    "output_type": "np",
									}
									return inputs




						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									a__ :Dict						  =    "cpu"

									a__ :List[Any]						  =    self.get_dummy_components()

									a__ :Any						  =    self.pipeline_class(**__A						)
									a__ :Optional[Any]						  =    pipe.to(__A						)

									pipe.set_progress_bar_config(disable=__A						)

									a__ :Any						  =    pipe(**self.get_dummy_inputs(__A						)						)
									a__ :Optional[int]						  =    output.images

									a__ :Optional[Any]						  =    pipe(
									    **self.get_dummy_inputs(__A						)   ,     return_dict=__A   ,     )[0]

									a__ :str						  =    image[0, -3:, -3:, -1]
									a__ :Any						  =    image_from_tuple[0, -3:, -3:, -1]

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

									assert image.shape == (1, 64, 64, 3)

									a__ :List[str]						  =    np.array(
									    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]						)

									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[str]						)      ->List[Any]:






									"""simple docstring"""






									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)






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




						def  _snake_case					(	self						:   Optional[int]						)      ->Union[str, Any]:






									"""simple docstring"""






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






						def  _snake_case					(	self						:   List[str]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :int						  =    load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"						)

									a__ :Tuple						  =    load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
									a__ :Union[str, Any]						  =    np.ones((768, 768)   ,     dtype=np.floataa						)
									a__ :Union[str, Any]						  =    0

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

									a__ :List[str]						  =    KandinskyPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-prior"   ,     torch_dtype=torch.floataa						)
									pipe_prior.to(__A						)

									a__ :Any						  =    KandinskyInpaintPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-inpaint"   ,     torch_dtype=torch.floataa						)
									a__ :str						  =    pipeline.to(__A						)
									pipeline.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    torch.Generator(device="cpu"						).manual_seed(0						)
									a__  ,  a__ :int						  =    pipe_prior(
									    __A   ,     generator=__A   ,     num_inference_steps=5   ,     negative_prompt=""   ,     ).to_tuple()

									a__ :Tuple						  =    pipeline(
									    __A   ,     image=__A   ,     mask_image=__A   ,     image_embeds=__A   ,     negative_image_embeds=__A   ,     generator=__A   ,     num_inference_steps=100   ,     height=768   ,     width=768   ,     output_type="np"   ,     )

									a__ :int						  =    output.images[0]

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

									assert_mean_pixel_difference(__A   ,     __A						)






class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    {}




						def  _snake_case					(	self						:   Tuple						)      ->None:






									"""simple docstring"""






									print(self.vertex						)
									for i in self.vertex:
												print(__A   ,     " -> "   ,     " -> ".join([str(__A						) for j in self.vertex[i]]						)						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   int						)      ->None:






									"""simple docstring"""






									if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__A						)
									else:
												# else make a new vertex
												a__ :Any						  =    [to_vertex]




						def  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__ :List[Any]						  =    [False] * len(self.vertex						)

									# call the recursive helper function
									for i in range(len(self.vertex						)						):
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   list						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    True

									print(__A   ,     end=" "						)

									# Recur for all the vertices that are adjacent to this node
									for i in self.vertex:
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)


if __name__ == "__main__":
		snake_case__			 =						Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('''DFS:''')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3


