
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 							UpperCAmelCase				(  __a      ,      unittest.TestCase  ):




						'''simple docstring'''





						snake_case_    =					KandinskyVaaPipeline
						snake_case_    =					[
						    """image_embeds""",
						    """negative_image_embeds""",
						]
						snake_case_    =					["""image_embeds""", """negative_image_embeds"""]
						snake_case_    =					[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						snake_case_    =					False






						@property
						def     UpperCamelCase_  (	self :			List[Any]			):
										return 32






						@property
						def     UpperCamelCase_  (	self :			Dict			):
										return 32






						@property
						def     UpperCamelCase_  (	self :			Optional[Any]			):
										return self.time_input_dim






						@property
						def     UpperCamelCase_  (	self :			Optional[Any]			):
										return self.time_input_dim * 4






						@property
						def     UpperCamelCase_  (	self :			Dict			):
										return 1_00






						@property
						def     UpperCamelCase_  (	self :			Tuple			):
										torch.manual_seed(0			)

										__A             =			{
										    "in_channels": 4,
										    # Out channels is double in channels because predicts mean and variance
										    "out_channels": 8,
										    "addition_embed_type": "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": "image_proj",
										    "cross_attention_dim": self.cross_attention_dim,
										    "attention_head_dim": 4,
										    "resnet_time_scale_shift": "scale_shift",
										    "class_embed_type": None,
										}

										__A             =			UNetaDConditionModel(**a__			)
										return model






						@property
						def     UpperCamelCase_  (	self :			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     UpperCamelCase_  (	self :			str			):
										torch.manual_seed(0			)
										__A             =			VQModel(**self.dummy_movq_kwargs			)
										return model






						def     UpperCamelCase_  (	self :			Any			):
										__A             =			self.dummy_unet
										__A             =			self.dummy_movq

										__A             =			DDIMScheduler(
										    num_train_timesteps=10_00							,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             =			{
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}
										return components






						def     UpperCamelCase_  (	self :			Any							,A :			Union[str, Any]							,A :			Any=0			):
										__A             =			floats_tensor((1, self.text_embedder_hidden_size)							,rng=random.Random(a__			)			).to(a__			)
										__A             =			floats_tensor((1, self.text_embedder_hidden_size)							,rng=random.Random(seed + 1			)			).to(
										    a__			)
										if str(a__			).startswith("mps"			):
														__A             =			torch.manual_seed(a__			)
										else:
														__A             =			torch.Generator(device=a__			).manual_seed(a__			)
										__A             =			{
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs







						def     UpperCamelCase_  (	self :			List[str]			):
										__A             =			"cpu"

										__A             =			self.get_dummy_components()

										__A             =			self.pipeline_class(**a__			)
										__A             =			pipe.to(a__			)

										pipe.set_progress_bar_config(disable=a__			)

										__A             =			pipe(**self.get_dummy_inputs(a__			)			)
										__A             =			output.images

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

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

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

										__A             =			np.array(
										    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]			)

										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()}'''





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




						'''simple docstring'''





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







						def     UpperCamelCase_  (	self :			Dict			):
										__A             =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"			)

										__A             =			KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"							,torch_dtype=torch.floataa			)
										pipe_prior.to(a__			)

										__A             =			KandinskyVaaPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder"							,torch_dtype=torch.floataa			)
										__A             =			pipeline.to(a__			)
										pipeline.set_progress_bar_config(disable=a__			)

										__A             =			"red cat, 4k photo"

										__A             =			torch.Generator(device="cuda"			).manual_seed(0			)
										__A      ,							__A             =			pipe_prior(
										    a__							,generator=a__							,num_inference_steps=5							,negative_prompt=""							,).to_tuple()

										__A             =			torch.Generator(device="cuda"			).manual_seed(0			)
										__A             =			pipeline(
										    image_embeds=a__							,negative_image_embeds=a__							,generator=a__							,num_inference_steps=1_00							,output_type="np"							,)

										__A             =			output.images[0]

										assert image.shape == (5_12, 5_12, 3)

										assert_mean_pixel_difference(a__							,a__			)


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
