



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__       =	8





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       str=BITS   )    ->			str:







			'''simple docstring'''
			_UpperCAmelCase			=       x.device

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

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

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





def 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[Any]=BITS   )    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       x.device

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

			_UpperCAmelCase			=       rearrange(_UpperCAmelCase							,       'd -> d 1 1'   )
			_UpperCAmelCase			=       rearrange(_UpperCAmelCase							,       '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    :       int							,       _UpperCAmelCase    :       torch.FloatTensor							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       torch.FloatTensor							,       _UpperCAmelCase    :       float = 0.0							,       _UpperCAmelCase    :       bool = True							,       _UpperCAmelCase    :       Any=None							,       _UpperCAmelCase    :       bool = 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(_UpperCAmelCase							,       -scale							,       _UpperCAmelCase   )

			# 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(_UpperCAmelCase							,       _UpperCAmelCase   )
			_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(_UpperCAmelCase   ) else 'cpu'
						_UpperCAmelCase			=       torch.randn(model_output.shape							,       dtype=model_output.dtype							,       generator=_UpperCAmelCase   ).to(_UpperCAmelCase   )
						_UpperCAmelCase			=       self._get_variance(_UpperCAmelCase							,       _UpperCAmelCase   ) ** 0.5 * eta * noise

						_UpperCAmelCase			=       prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=_UpperCAmelCase							,       pred_original_sample=_UpperCAmelCase   )





def 							A     (					self    :       int							,       _UpperCAmelCase    :       torch.FloatTensor							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       torch.FloatTensor							,       _UpperCAmelCase    :       Union[str, Any]="epsilon"							,       _UpperCAmelCase    :       Tuple=None							,       _UpperCAmelCase    :       bool = 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(_UpperCAmelCase							,       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(_UpperCAmelCase							,       -scale							,       _UpperCAmelCase   )

			# 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=_UpperCAmelCase   ).to(model_output.device   )
						_UpperCAmelCase			=       (self._get_variance(_UpperCAmelCase							,       predicted_variance=_UpperCAmelCase   ) ** 0.5) * noise

			_UpperCAmelCase			=       pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=_UpperCAmelCase							,       pred_original_sample=_UpperCAmelCase   )







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  Optional[int]			,		A       :  UNetaDConditionModel			,		A       :  Union[DDIMScheduler, DDPMScheduler]			,		A       :  Optional[float] = 1.0			,		)       ->   Optional[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       :  List[str]			,		A       :  Optional[int] = 2_56			,		A       :  Optional[int] = 2_56			,		A       :  Optional[int] = 50			,		A       :  Optional[torch.Generator] = None			,		A       :  Optional[int] = 1			,		A       :  Optional[str] = "pil"			,		A       :  bool = True			,		**A       :  Union[str, Any]			,		)       ->   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 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       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					KandinskyVaaPipeline
			UpperCamelCase        =					[
			    '''image_embeds''',
			    '''negative_image_embeds''',
			]
			UpperCamelCase        =					['''image_embeds''', '''negative_image_embeds''']
			UpperCamelCase        =					[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



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



						"""simple docstring"""






						return 32



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



						"""simple docstring"""






						return 32



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



						"""simple docstring"""






						return self.time_input_dim



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



						"""simple docstring"""






						return self.time_input_dim * 4



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



						"""simple docstring"""






						return 1_00



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    '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,
						}

						_UpperCAmelCase			=       UNetaDConditionModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""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    _lowerCamelCase	(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






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



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_unet
						_UpperCAmelCase			=       self.dummy_movq

						_UpperCAmelCase			=       DDIMScheduler(
						    num_train_timesteps=10_00			,		beta_schedule='linear'			,		beta_start=0.0_0_0_8_5			,		beta_end=0.0_1_2			,		clip_sample=A			,		set_alpha_to_one=A			,		steps_offset=1			,		prediction_type='epsilon'			,		thresholding=A			,		)

						_UpperCAmelCase			=       {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}
						return components



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  str			,		A       :  Tuple=0)       ->   Union[str, 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)
						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase			=       {
						    '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    _lowerCamelCase	(						self       :  str)       ->   str:



						"""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(
						    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6])

						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       __lowerCAmelCase   (      unittest.TestCase						):



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



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy')

						_UpperCAmelCase			=       KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'			,		torch_dtype=torch.floataa)
						pipe_prior.to(A)

						_UpperCAmelCase			=       KandinskyVaaPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder'			,		torch_dtype=torch.floataa)
						_UpperCAmelCase			=       pipeline.to(A)
						pipeline.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       'red cat, 4k photo'

						_UpperCAmelCase			=       torch.Generator(device='cuda').manual_seed(0)
						_UpperCAmelCase	,					_UpperCAmelCase			=       pipe_prior(
						    A			,		generator=A			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

						_UpperCAmelCase			=       torch.Generator(device='cuda').manual_seed(0)
						_UpperCAmelCase			=       pipeline(
						    image_embeds=A			,		negative_image_embeds=A			,		generator=A			,		num_inference_steps=1_00			,		output_type='np'			,		)

						_UpperCAmelCase			=       output.images[0]

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

						assert_mean_pixel_difference(A			,		A)
