
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCAmelCase :			Union[str, Any]              =	8


def 				A	( lowercase   ,	lowercase=BITS			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					x.device

		UpperCamelCase          =					(x * 255).int().clamp(0   ,	255			)

		UpperCamelCase          =					2 ** torch.arange(bits - 1   ,	-1   ,	-1   ,	device=lowercase			)
		UpperCamelCase          =					rearrange(lowercase   ,	'd -> d 1 1'			)
		UpperCamelCase          =					rearrange(lowercase   ,	'b c h w -> b c 1 h w'			)

		UpperCamelCase          =					((x & mask) != 0).float()
		UpperCamelCase          =					rearrange(lowercase   ,	'b c d h w -> b (c d) h w'			)
		UpperCamelCase          =					bits * 2 - 1
		return bits


def 				A	( lowercase   ,	lowercase=BITS			)       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					x.device

		UpperCamelCase          =					(x > 0).int()
		UpperCamelCase          =					2 ** torch.arange(bits - 1   ,	-1   ,	-1   ,	device=lowercase   ,	dtype=torch.intaa			)

		UpperCamelCase          =					rearrange(lowercase   ,	'd -> d 1 1'			)
		UpperCamelCase          =					rearrange(lowercase   ,	'b (c d) h w -> b c d h w'   ,	d=8			)
		UpperCamelCase          =					reduce(x * mask   ,	'b c d h w -> b c h w'   ,	'sum'			)
		return (dec / 255).clamp(0.0   ,	1.0			)


def 				A	( self   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase = 0.0   ,	lowercase = True   ,	lowercase=None   ,	lowercase = True   ,	)       ->  Union[DDIMSchedulerOutput, Tuple]:
		'''simple docstring'''

		if self.num_inference_steps is None:
				raise ValueError(
				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'			)

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		UpperCamelCase          =					timestep - self.config.num_train_timesteps // self.num_inference_steps

		# 2. compute alphas, betas
		UpperCamelCase          =					self.alphas_cumprod[timestep]
		UpperCamelCase          =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

		UpperCamelCase          =					1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		UpperCamelCase          =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

		# 4. Clip "predicted x_0"
		UpperCamelCase          =					self.bit_scale
		if self.config.clip_sample:
				UpperCamelCase          =					torch.clamp(lowercase   ,	-scale   ,	lowercase			)

		# 5. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		UpperCamelCase          =					self._get_variance(lowercase   ,	lowercase			)
		UpperCamelCase          =					eta * variance ** 0.5

		if use_clipped_model_output:
				# the model_output is always re-derived from the clipped x_0 in Glide
				UpperCamelCase          =					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

		# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		UpperCamelCase          =					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

		# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		UpperCamelCase          =					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if eta > 0:
				# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
				UpperCamelCase          =					model_output.device if torch.is_tensor(lowercase			) else 'cpu'
				UpperCamelCase          =					torch.randn(model_output.shape   ,	dtype=model_output.dtype   ,	generator=lowercase			).to(lowercase			)
				UpperCamelCase          =					self._get_variance(lowercase   ,	lowercase			) ** 0.5 * eta * noise

				UpperCamelCase          =					prev_sample + variance

		if not return_dict:
				return (prev_sample,)

		return DDIMSchedulerOutput(prev_sample=lowercase   ,	pred_original_sample=lowercase			)


def 				A	( self   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase="epsilon"   ,	lowercase=None   ,	lowercase = True   ,	)       ->  Union[DDPMSchedulerOutput, Tuple]:
		'''simple docstring'''

		UpperCamelCase          =					timestep

		if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
				UpperCamelCase     ,  UpperCamelCase          =					torch.split(lowercase   ,	sample.shape[1]   ,	dim=1			)
		else:
				UpperCamelCase          =					None

		# 1. compute alphas, betas
		UpperCamelCase          =					self.alphas_cumprod[t]
		UpperCamelCase          =					self.alphas_cumprod[t - 1] if t > 0 else self.one
		UpperCamelCase          =					1 - alpha_prod_t
		UpperCamelCase          =					1 - alpha_prod_t_prev

		# 2. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
		if prediction_type == "epsilon":
				UpperCamelCase          =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif prediction_type == "sample":
				UpperCamelCase          =					model_output
		else:
				raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''			)

		# 3. Clip "predicted x_0"
		UpperCamelCase          =					self.bit_scale
		if self.config.clip_sample:
				UpperCamelCase          =					torch.clamp(lowercase   ,	-scale   ,	lowercase			)

		# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		UpperCamelCase          =					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
		UpperCamelCase          =					self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

		# 5. Compute predicted previous sample µ_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		UpperCamelCase          =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		UpperCamelCase          =					0
		if t > 0:
				UpperCamelCase          =					torch.randn(
				    model_output.size()   ,	dtype=model_output.dtype   ,	layout=model_output.layout   ,	generator=lowercase			).to(model_output.device			)
				UpperCamelCase          =					(self._get_variance(lowercase   ,	predicted_variance=lowercase			) ** 0.5) * noise

		UpperCamelCase          =					pred_prev_sample + variance

		if not return_dict:
				return (pred_prev_sample,)

		return DDPMSchedulerOutput(prev_sample=lowercase   ,	pred_original_sample=lowercase			)


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_ = 1.0    ,				)  -> Union[str, Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					bit_scale
				UpperCamelCase          =					(
				    ddim_bit_scheduler_step if isinstance(A_    ,				A_	) else ddpm_bit_scheduler_step
				)

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



		@torch.no_grad()
		def __call__(			self    ,				A_ = 256    ,				A_ = 256    ,				A_ = 50    ,				A_ = None    ,				A_ = 1    ,				A_ = "pil"    ,				A_ = True    ,				**A_    ,				)  -> Union[Tuple, ImagePipelineOutput]:
				"""simple docstring"""
				UpperCamelCase          =					torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)    ,				generator=A_    ,				)
				UpperCamelCase          =					decimal_to_bits(A_	) * self.bit_scale
				UpperCamelCase          =					latents.to(self.device	)

				self.scheduler.set_timesteps(A_	)

				for t in self.progress_bar(self.scheduler.timesteps	):
						# predict the noise residual
						UpperCamelCase          =					self.unet(A_    ,				A_	).sample

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase          =					self.scheduler.step(A_    ,				A_    ,				A_	).prev_sample

				UpperCamelCase          =					bits_to_decimal(A_	)

				if output_type == "pil":
						UpperCamelCase          =					self.numpy_to_pil(A_	)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_	)

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    KandinskyVaaControlnetImgaImgPipeline
		__lowercase	:	Optional[Any]     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Any     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Union[str, Any]     					=    [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__lowercase	:	Optional[int]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				torch.manual_seed(0	)

				UpperCamelCase          =					{
				    'in_channels': 8,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'image_hint',
				    '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,
				}

				UpperCamelCase          =					UNetaDConditionModel(**A_	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					VQModel(**self.dummy_movq_kwargs	)
				return model



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_unet
				UpperCamelCase          =					self.dummy_movq

				UpperCamelCase          =					{
				    'num_train_timesteps': 1_000,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				UpperCamelCase          =					DDIMScheduler(**A_	)

				UpperCamelCase          =					{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1	)	).to(
				    A_	)
				# create init_image
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	)[0]
				UpperCamelCase          =					Image.fromarray(np.uinta(A_	)	).convert('RGB'	).resize((256, 256)	)
				# create hint
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)

				if str(A_	).startswith('mps'	):
						UpperCamelCase          =					torch.manual_seed(A_	)
				else:
						UpperCamelCase          =					torch.Generator(device=A_	).manual_seed(A_	)
				UpperCamelCase          =					{
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					'cpu'

				UpperCamelCase          =					self.get_dummy_components()

				UpperCamelCase          =					self.pipeline_class(**A_	)
				UpperCamelCase          =					pipe.to(A_	)

				pipe.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					pipe(**self.get_dummy_inputs(A_	)	)
				UpperCamelCase          =					output.images

				UpperCamelCase          =					pipe(
				    **self.get_dummy_inputs(A_	)    ,				return_dict=A_    ,				)[0]

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

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

				UpperCamelCase          =					np.array(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]	)
				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     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
				UpperCamelCase          =					init_image.resize((512, 512)	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/hint_image_cat.png'	)
				UpperCamelCase          =					torch.from_numpy(np.array(A_	)	).float() / 255.0
				UpperCamelCase          =					hint.permute(2    ,				0    ,				1	).unsqueeze(0	)

				UpperCamelCase          =					'A robot, 4k photo'

				UpperCamelCase          =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'    ,				torch_dtype=torch.floataa	)
				pipe_prior.to(A_	)

				UpperCamelCase          =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,				torch_dtype=torch.floataa	)
				UpperCamelCase          =					pipeline.to(A_	)

				pipeline.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					torch.Generator(device='cpu'	).manual_seed(0	)

				UpperCamelCase     ,  UpperCamelCase          =					pipe_prior(
				    A_    ,				image=A_    ,				strength=0.85    ,				generator=A_    ,				negative_prompt=''    ,				).to_tuple()

				UpperCamelCase          =					pipeline(
				    image=A_    ,				image_embeds=A_    ,				negative_image_embeds=A_    ,				hint=A_    ,				generator=A_    ,				num_inference_steps=100    ,				height=512    ,				width=512    ,				strength=0.5    ,				output_type='np'    ,				)

				UpperCamelCase          =					output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(A_    ,				A_	)
