


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class 					__SCREAMING_SNAKE_CASE (      _a	,    _a	,    _a	,    unittest.TestCase ):
      A					:				Tuple     				=  StableDiffusionControlNetImgaImgPipeline
      A					:				Dict     				=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      A					:				Any     				=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      A					:				Union[str, Any]     				=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
      A					:				List[Any]     				=  IMAGE_TO_IMAGE_IMAGE_PARAMS


      def 							__lowerCamelCase      (				self		):
            torch.manual_seed(0		)
            lowercase      : Optional[int]        =		UNetaDConditionModel(
                block_out_channels=(32, 64)				,    layers_per_block=2				,    sample_size=32				,    in_channels=4				,    out_channels=4				,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,    cross_attention_dim=32				,    )
            torch.manual_seed(0		)
            lowercase      : List[str]        =		ControlNetModel(
                block_out_channels=(32, 64)				,    layers_per_block=2				,    in_channels=4				,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,    cross_attention_dim=32				,    conditioning_embedding_out_channels=(16, 32)				,    )
            torch.manual_seed(0		)
            lowercase      : List[str]        =		DDIMScheduler(
                beta_start=0.00085				,    beta_end=0.012				,    beta_schedule='''scaled_linear'''				,    clip_sample=lowerCAmelCase_				,    set_alpha_to_one=lowerCAmelCase_				,    )
            torch.manual_seed(0		)
            lowercase      : Union[str, Any]        =		AutoencoderKL(
                block_out_channels=[32, 64]				,    in_channels=3				,    out_channels=3				,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,    latent_channels=4				,    )
            torch.manual_seed(0		)
            lowercase      : Optional[int]        =		CLIPTextConfig(
                bos_token_id=0				,    eos_token_id=2				,    hidden_size=32				,    intermediate_size=37				,    layer_norm_eps=1E-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1000				,    )
            lowercase      : int        =		CLIPTextModel(lowerCAmelCase_		)
            lowercase      : str        =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

            lowercase      : Optional[Any]        =		{
                '''unet''': unet,
                '''controlnet''': controlnet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''safety_checker''': None,
                '''feature_extractor''': None,
            }
            return components


      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__=0		):


            if str(lowerCAmelCase_		).startswith('''mps'''		):
                  lowercase      : Optional[int]        =		torch.manual_seed(lowerCAmelCase_		)
            else:
                  lowercase      : List[str]        =		torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)

            lowercase      : Tuple        =		2
            lowercase      : List[str]        =		randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,    generator=lowerCAmelCase_				,    device=torch.device(lowerCAmelCase_		)				,    )
            lowercase      : Tuple        =		floats_tensor(control_image.shape				,    rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
            lowercase      : Dict        =		image.cpu().permute(0				,    2				,    3				,    1		)[0]
            lowercase      : Union[str, Any]        =		Image.fromarray(np.uinta(lowerCAmelCase_		)		).convert('''RGB'''		).resize((64, 64)		)
            lowercase      : Any        =		{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 6.0,
                '''output_type''': '''numpy''',
                '''image''': image,
                '''control_image''': control_image,
            }

            return inputs


      def 							__lowerCamelCase      (				self		):
            return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)


      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()				,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,    )
      def 							__lowerCamelCase      (				self		):
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)






      def 							__lowerCamelCase      (				self		):
            self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







class 					__SCREAMING_SNAKE_CASE (      _a	,    _a	,    unittest.TestCase ):
      A					:				List[str]     				=  StableDiffusionControlNetImgaImgPipeline
      A					:				Optional[int]     				=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      A					:				Tuple     				=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      A					:				int     				=  frozenset([] )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


      def 							__lowerCamelCase      (				self		):
            torch.manual_seed(0		)
            lowercase      : Dict        =		UNetaDConditionModel(
                block_out_channels=(32, 64)				,    layers_per_block=2				,    sample_size=32				,    in_channels=4				,    out_channels=4				,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,    cross_attention_dim=32				,    )
            torch.manual_seed(0		)

            def init_weights(SCREAMING_SNAKE_CASE__		):
                  if isinstance(lowerCAmelCase_				,    torch.nn.Convad		):
                        torch.nn.init.normal(m.weight		)
                        m.bias.data.fill_(1.0		)

            lowercase      : Union[str, Any]        =		ControlNetModel(
                block_out_channels=(32, 64)				,    layers_per_block=2				,    in_channels=4				,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,    cross_attention_dim=32				,    conditioning_embedding_out_channels=(16, 32)				,    )
            controlneta.controlnet_down_blocks.apply(lowerCAmelCase_		)

            torch.manual_seed(0		)
            lowercase      : Union[str, Any]        =		ControlNetModel(
                block_out_channels=(32, 64)				,    layers_per_block=2				,    in_channels=4				,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,    cross_attention_dim=32				,    conditioning_embedding_out_channels=(16, 32)				,    )
            controlneta.controlnet_down_blocks.apply(lowerCAmelCase_		)

            torch.manual_seed(0		)
            lowercase      : str        =		DDIMScheduler(
                beta_start=0.00085				,    beta_end=0.012				,    beta_schedule='''scaled_linear'''				,    clip_sample=lowerCAmelCase_				,    set_alpha_to_one=lowerCAmelCase_				,    )
            torch.manual_seed(0		)
            lowercase      : Optional[Any]        =		AutoencoderKL(
                block_out_channels=[32, 64]				,    in_channels=3				,    out_channels=3				,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,    latent_channels=4				,    )
            torch.manual_seed(0		)
            lowercase      : List[str]        =		CLIPTextConfig(
                bos_token_id=0				,    eos_token_id=2				,    hidden_size=32				,    intermediate_size=37				,    layer_norm_eps=1E-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1000				,    )
            lowercase      : Optional[int]        =		CLIPTextModel(lowerCAmelCase_		)
            lowercase      : Optional[int]        =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

            lowercase      : Dict        =		MultiControlNetModel([controlneta, controlneta]		)

            lowercase      : Tuple        =		{
                '''unet''': unet,
                '''controlnet''': controlnet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''safety_checker''': None,
                '''feature_extractor''': None,
            }
            return components


      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__=0		):


            if str(lowerCAmelCase_		).startswith('''mps'''		):
                  lowercase      : List[Any]        =		torch.manual_seed(lowerCAmelCase_		)
            else:
                  lowercase      : List[str]        =		torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)

            lowercase      : int        =		2

            lowercase      : int        =		[
                randn_tensor(
                    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,    generator=lowerCAmelCase_				,    device=torch.device(lowerCAmelCase_		)				,    ),
                randn_tensor(
                    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,    generator=lowerCAmelCase_				,    device=torch.device(lowerCAmelCase_		)				,    ),
            ]

            lowercase      : Optional[int]        =		floats_tensor(control_image[0].shape				,    rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
            lowercase      : int        =		image.cpu().permute(0				,    2				,    3				,    1		)[0]
            lowercase      : List[str]        =		Image.fromarray(np.uinta(lowerCAmelCase_		)		).convert('''RGB'''		).resize((64, 64)		)
            lowercase      : int        =		{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 6.0,
                '''output_type''': '''numpy''',
                '''image''': image,
                '''control_image''': control_image,
            }

            return inputs


      def 							__lowerCamelCase      (				self		):
            lowercase      : Dict        =		self.get_dummy_components()
            lowercase      : int        =		self.pipeline_class(**lowerCAmelCase_		)
            pipe.to(lowerCAmelCase_		)

            lowercase      : Union[str, Any]        =		10.0
            lowercase      : str        =		4

            lowercase      : str        =		self.get_dummy_inputs(lowerCAmelCase_		)
            lowercase      : Tuple        =		steps
            lowercase      : Tuple        =		scale
            lowercase      : int        =		pipe(**lowerCAmelCase_		)[0]

            lowercase      : List[Any]        =		self.get_dummy_inputs(lowerCAmelCase_		)
            lowercase      : Tuple        =		steps
            lowercase      : Optional[Any]        =		scale
            lowercase      : Dict        =		pipe(**lowerCAmelCase_				,    control_guidance_start=0.1				,    control_guidance_end=0.2		)[0]

            lowercase      : Optional[Any]        =		self.get_dummy_inputs(lowerCAmelCase_		)
            lowercase      : Optional[int]        =		steps
            lowercase      : Optional[int]        =		scale
            lowercase      : int        =		pipe(**lowerCAmelCase_				,    control_guidance_start=[0.1, 0.3]				,    control_guidance_end=[0.2, 0.7]		)[0]

            lowercase      : str        =		self.get_dummy_inputs(lowerCAmelCase_		)
            lowercase      : Optional[Any]        =		steps
            lowercase      : List[str]        =		scale
            lowercase      : int        =		pipe(**lowerCAmelCase_				,    control_guidance_start=0.4				,    control_guidance_end=[0.5, 0.8]		)[0]

            # make sure that all outputs are different
            assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
            assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
            assert np.sum(np.abs(output_a - output_a		)		) > 1E-3


      def 							__lowerCamelCase      (				self		):
            return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)


      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()				,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,    )
      def 							__lowerCamelCase      (				self		):
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)


      def 							__lowerCamelCase      (				self		):
            self._test_inference_batch_single_identical(expected_max_diff=2E-3		)






      def 							__lowerCamelCase      (				self		):
            lowercase      : int        =		self.get_dummy_components()
            lowercase      : int        =		self.pipeline_class(**lowerCAmelCase_		)
            pipe.to(lowerCAmelCase_		)
            pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
            with tempfile.TemporaryDirectory() as tmpdir:
                  try:
                        # save_pretrained is not implemented for Multi-ControlNet
                        pipe.save_pretrained(lowerCAmelCase_		)
                  except NotImplementedError:
                        pass







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


      def 							__lowerCamelCase      (				self		):
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






      def 							__lowerCamelCase      (				self		):
            lowercase      : Dict        =		ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

            lowercase      : Optional[Any]        =		StableDiffusionControlNetImgaImgPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''				,    safety_checker=lowerCAmelCase_				,    controlnet=lowerCAmelCase_		)
            pipe.enable_model_cpu_offload()
            pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

            lowercase      : int        =		torch.Generator(device='''cpu'''		).manual_seed(0		)
            lowercase      : str        =		'''evil space-punk bird'''
            lowercase      : int        =		load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''		).resize((512, 512)		)
            lowercase      : Tuple        =		load_image(
                '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''		).resize((512, 512)		)

            lowercase      : List[str]        =		pipe(
                lowerCAmelCase_				,    lowerCAmelCase_				,    control_image=lowerCAmelCase_				,    generator=lowerCAmelCase_				,    output_type='''np'''				,    num_inference_steps=50				,    strength=0.6				,    )

            lowercase      : Union[str, Any]        =		output.images[0]

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

            lowercase      : str        =		load_numpy(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''		)

            assert np.abs(expected_image - image		).max() < 9E-2







'''simple docstring'''


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


_snake_case : Optional[Any]  				=						8
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Dict=BITS ):





							'''simple docstring'''


							_a      	=							x.device

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

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

							_a      	=							((x & mask) != 0).float()
							_a      	=							rearrange(UpperCamelCase					,		'''b c d h w -> b (c d) h w''' )
							_a      	=							bits * 2 - 1
							return bits
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Any=BITS ):





							'''simple docstring'''


							_a      	=							x.device

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

							_a      	=							rearrange(UpperCamelCase					,		'''d -> d 1 1''' )
							_a      	=							rearrange(UpperCamelCase					,		'''b (c d) h w -> b c d h w'''					,		d=8 )
							_a      	=							reduce(x * mask					,		'''b c d h w -> b c h w'''					,		'''sum''' )
							return (dec / 255).clamp(0.0					,		1.0 )
def 						snake_case_       (self			:    Union[str, Any]					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    float = 0.0					,		UpperCamelCase			:    bool = True					,		UpperCamelCase			:    Any=None					,		UpperCamelCase			:    bool = True					,		):





							'''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)
							_a      	=							timestep - self.config.num_train_timesteps // self.num_inference_steps

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

							_a      	=							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
							_a      	=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							_a      	=							self.bit_scale
							if self.config.clip_sample:
														_a      	=							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)
							_a      	=							self._get_variance(UpperCamelCase					,		UpperCamelCase )
							_a      	=							eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														_a      	=							(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
							_a      	=							(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
							_a      	=							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
														_a      	=							model_output.device if torch.is_tensor(UpperCamelCase ) else '''cpu'''
														_a      	=							torch.randn(model_output.shape					,		dtype=model_output.dtype					,		generator=UpperCamelCase ).to(UpperCamelCase )
														_a      	=							self._get_variance(UpperCamelCase					,		UpperCamelCase ) ** 0.5 * eta * noise

														_a      	=							prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCamelCase					,		pred_original_sample=UpperCamelCase )
def 						snake_case_       (self			:    Any					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    str="epsilon"					,		UpperCamelCase			:    Dict=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							_a      	=							timestep

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

							# 1. compute alphas, betas
							_a      	=							self.alphas_cumprod[t]
							_a      	=							self.alphas_cumprod[t - 1] if t > 0 else self.one
							_a      	=							1 - alpha_prod_t
							_a      	=							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":
														_a      	=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														_a      	=							model_output
							else:
														raise ValueError(f'Unsupported prediction_type {prediction_type}.' )

							# 3. Clip "predicted x_0"
							_a      	=							self.bit_scale
							if self.config.clip_sample:
														_a      	=							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
							_a      	=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							_a      	=							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
							_a      	=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

							_a      	=							pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCamelCase					,		pred_original_sample=UpperCamelCase )

class      A       (						_a       ):







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    UNetaDConditionModel				,     lowerCAmelCase_			:    Union[DDIMScheduler, DDPMScheduler]				,     lowerCAmelCase_			:    Optional[float] = 1.0				,     )     ->				int:


														"""simple docstring"""


														super().__init__()
														_a      	=							bit_scale
														_a      	=							(
														    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ) else ddpm_bit_scheduler_step
														)

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







							@torch.no_grad()
							def __call__(    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 50				,     lowerCAmelCase_			:    Optional[torch.Generator] = None				,     lowerCAmelCase_			:    Optional[int] = 1				,     lowerCAmelCase_			:    Optional[str] = "pil"				,     lowerCAmelCase_			:    bool = True				,     **lowerCAmelCase_			:    Any				,     )     ->				Union[Tuple, ImagePipelineOutput]:


														"""simple docstring"""


														_a      	=							torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)				,     generator=lowerCAmelCase_				,     )
														_a      	=							decimal_to_bits(lowerCAmelCase_   ) * self.bit_scale
														_a      	=							latents.to(self.device   )

														self.scheduler.set_timesteps(lowerCAmelCase_   )

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

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

														_a      	=							bits_to_decimal(lowerCAmelCase_   )

														if output_type == "pil":
																					_a      	=							self.numpy_to_pil(lowerCAmelCase_   )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCAmelCase_   )

