



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__  (	UpperCamelCase					,							unittest.TestCase						):
					__A     :    List[str]           =						KandinskyInpaintPipeline
					__A     :    Tuple           =						['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
					__A     :    Any           =						[
					    'prompt',
					    'negative_prompt',
					    'image_embeds',
					    'negative_image_embeds',
					    'image',
					    'mask_image',
					]
					__A     :    str           =						[
					    'generator',
					    'height',
					    'width',
					    'latents',
					    'guidance_scale',
					    'negative_prompt',
					    'num_inference_steps',
					    'return_dict',
					    'guidance_scale',
					    'num_images_per_prompt',
					    'output_type',
					    'return_dict',
					]
					__A     :    Dict           =						False







					@property
					def  _lowercase			(       self      :     Optional[Any]):
											return 32







					@property
					def  _lowercase			(       self      :     Optional[Any]):
											return 32







					@property
					def  _lowercase			(       self      :     List[Any]):
											return self.time_input_dim







					@property
					def  _lowercase			(       self      :     Dict):
											return self.time_input_dim * 4







					@property
					def  _lowercase			(       self      :     str):
											return 100







					@property
					def  _lowercase			(       self      :     Optional[int]):
											A__	:							Union[str, Any]						    = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
											return tokenizer







					@property
					def  _lowercase			(       self      :     Tuple):
											torch.manual_seed(0)
											A__	:							int						    = 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[int]						    = MultilingualCLIP(_A)
											A__	:							Any						    = text_encoder.eval()

											return text_encoder







					@property
					def  _lowercase			(       self      :     str):
											torch.manual_seed(0)

											A__	:							Dict						    = {
											    "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__	:							Union[str, Any]						    = UNetaDConditionModel(**_A)
											return model







					@property
					def  _lowercase			(       self      :     Union[str, Any]):
											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  _lowercase			(       self      :     str):
											torch.manual_seed(0)
											A__	:							Dict						    = VQModel(**self.dummy_movq_kwargs)
											return model







					def  _lowercase			(       self      :     List[Any]):
											A__	:							List[Any]						    = self.dummy_text_encoder
											A__	:							Tuple						    = self.dummy_tokenizer
											A__	:							Tuple						    = self.dummy_unet
											A__	:							Dict						    = self.dummy_movq

											A__	:							Tuple						    = DDIMScheduler(
											    num_train_timesteps=1000			,		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			,		)

											A__	:							int						    = {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "movq": movq,
											}

											return components







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

											if str(_A).startswith("mps"):
																	A__	:							List[Any]						    = torch.manual_seed(_A)
											else:
																	A__	:							Union[str, Any]						    = torch.Generator(device=_A).manual_seed(_A)
											A__	:							Any						    = {
											    "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  _lowercase			(       self      :     Dict):
											A__	:							Tuple						    = "cpu"

											A__	:							str						    = self.get_dummy_components()

											A__	:							Dict						    = self.pipeline_class(**_A)
											A__	:							int						    = pipe.to(_A)

											pipe.set_progress_bar_config(disable=_A)

											A__	:							Dict						    = pipe(**self.get_dummy_inputs(_A))
											A__	:							List[str]						    = output.images

											A__	:							List[Any]						    = pipe(
											    **self.get_dummy_inputs(_A)			,		return_dict=_A			,		)[0]

											A__	:							Union[str, Any]						    = image[0, -3:, -3:, -1]
											A__	:							Tuple						    = image_from_tuple[0, -3:, -3:, -1]

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

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

											A__	:							List[Any]						    = 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  _lowercase			(       self      :     Optional[int]):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3)




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







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







					def  _lowercase			(       self      :     Dict):
											A__	:							List[Any]						    = load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy")

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

											A__	:							List[Any]						    = "a hat"

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

											A__	:							Optional[Any]						    = KandinskyInpaintPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1-inpaint"			,		torch_dtype=torch.floataa)
											A__	:							Union[str, Any]						    = pipeline.to(_A)
											pipeline.set_progress_bar_config(disable=_A)

											A__	:							Optional[Any]						    = torch.Generator(device="cpu").manual_seed(0)
											A__    ,       A__	:							str						    = pipe_prior(
											    _A			,		generator=_A			,		num_inference_steps=5			,		negative_prompt=""			,		).to_tuple()

											A__	:							Any						    = 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__	:							Optional[int]						    = output.images[0]

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

											assert_mean_pixel_difference(_A			,		_A)




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()