




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       a__   ( snake_case__			,    unittest.TestCase				):
						_a							:   Optional[Any] =   KandinskyInpaintPipeline
						_a							:   Optional[Any] =   ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						_a							:   Optional[int] =   [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						_a							:   List[Any] =   [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_a							:   str =   False






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return 3_2






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return 3_2






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return self.time_input_dim






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return self.time_input_dim * 4






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return 1_0_0






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	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		,      )

											__lowerCAmelCase           =	MultilingualCLIP(_A      )
											__lowerCAmelCase           =	text_encoder.eval()

											return text_encoder






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )

											__lowerCAmelCase           =	{
											    "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,
											}

											__lowerCAmelCase           =	UNetaDConditionModel(**_A      )
											return model






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	VQModel(**self.dummy_movq_kwargs      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_text_encoder
											__lowerCAmelCase           =	self.dummy_tokenizer
											__lowerCAmelCase           =	self.dummy_unet
											__lowerCAmelCase           =	self.dummy_movq

											__lowerCAmelCase           =	DDIMScheduler(
											    num_train_timesteps=1_0_0_0		,      beta_schedule="linear"		,      beta_start=0.0_00_85		,      beta_end=0.0_12		,      clip_sample=_A		,      set_alpha_to_one=_A		,      steps_offset=1		,      prediction_type="epsilon"		,      thresholding=_A		,      )

											__lowerCAmelCase           =	{
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "movq": movq,
											}

											return components






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A=0      ):




											"""simple docstring"""



											__lowerCAmelCase           =	floats_tensor((1, self.cross_attention_dim)		,      rng=random.Random(_A      )      ).to(_A      )
											__lowerCAmelCase           =	floats_tensor((1, self.cross_attention_dim)		,      rng=random.Random(seed + 1      )      ).to(_A      )
											# create init_image
											__lowerCAmelCase           =	floats_tensor((1, 3, 6_4, 6_4)		,      rng=random.Random(_A      )      ).to(_A      )
											__lowerCAmelCase           =	image.cpu().permute(0		,      2		,      3		,      1      )[0]
											__lowerCAmelCase           =	Image.fromarray(np.uinta(_A      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )
											# create mask
											__lowerCAmelCase           =	np.ones((6_4, 6_4)		,      dtype=np.floataa      )
											__lowerCAmelCase           =	0

											if str(_A      ).startswith("mps"      ):
																__lowerCAmelCase           =	torch.manual_seed(_A      )
											else:
																__lowerCAmelCase           =	torch.Generator(device=_A      ).manual_seed(_A      )
											__lowerCAmelCase           =	{
											    "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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"cpu"

											__lowerCAmelCase           =	self.get_dummy_components()

											__lowerCAmelCase           =	self.pipeline_class(**_A      )
											__lowerCAmelCase           =	pipe.to(_A      )

											pipe.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	pipe(**self.get_dummy_inputs(_A      )      )
											__lowerCAmelCase           =	output.images

											__lowerCAmelCase           =	pipe(
											    **self.get_dummy_inputs(_A      )		,      return_dict=_A		,      )[0]

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

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

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

											__lowerCAmelCase           =	np.array(
											    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]      )

											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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

											__lowerCAmelCase           =	load_image(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
											__lowerCAmelCase           =	np.ones((7_6_8, 7_6_8)		,      dtype=np.floataa      )
											__lowerCAmelCase           =	0

											__lowerCAmelCase           =	"a hat"

											__lowerCAmelCase           =	KandinskyPriorPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1-prior"		,      torch_dtype=torch.floataa      )
											pipe_prior.to(_A      )

											__lowerCAmelCase           =	KandinskyInpaintPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1-inpaint"		,      torch_dtype=torch.floataa      )
											__lowerCAmelCase           =	pipeline.to(_A      )
											pipeline.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.Generator(device="cpu"      ).manual_seed(0      )
											__lowerCAmelCase       ,							__lowerCAmelCase           =	pipe_prior(
											    _A		,      generator=_A		,      num_inference_steps=5		,      negative_prompt=""		,      ).to_tuple()

											__lowerCAmelCase           =	pipeline(
											    _A		,      image=_A		,      mask_image=_A		,      image_embeds=_A		,      negative_image_embeds=_A		,      generator=_A		,      num_inference_steps=1_0_0		,      height=7_6_8		,      width=7_6_8		,      output_type="np"		,      )

											__lowerCAmelCase           =	output.images[0]

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

											assert_mean_pixel_difference(_A		,      _A      )










def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					while len(SCREAMING_SNAKE_CASE_					) > 1:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	min(SCREAMING_SNAKE_CASE_					), max(SCREAMING_SNAKE_CASE_					)
										start.append(SCREAMING_SNAKE_CASE_					)
										end.append(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
					UpperCamelCase__         =      input("""Enter numbers separated by a comma:\n""").strip()
					UpperCamelCase__         =      [int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")




