


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,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (      _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       StableDiffusionDiffEditPipeline
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
 SCREAMING_SNAKE_CASE__      =       frozenset(
     []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 SCREAMING_SNAKE_CASE__      =       frozenset([]							)




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						: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					,  attention_head_dim=(2, 4)					,  use_linear_projection=_lowerCamelCase					,  )
       a						:int 							=       DDIMScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=_lowerCamelCase					,  set_alpha_to_one=_lowerCamelCase					,  )
       a						:Tuple 							=       DDIMInverseScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=_lowerCamelCase					,  set_alpha_to_zero=_lowerCamelCase					,  )
       torch.manual_seed(0    )
       a						:Dict 							=       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					,  sample_size=128					,  )
       torch.manual_seed(0    )
       a						: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					,  hidden_act='''gelu'''					,  projection_dim=512					,  )
       a						:Optional[int] 							=       CLIPTextModel(_lowerCamelCase    )
       a						:str 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

       a						:Optional[Any] 							=       {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''inverse_scheduler''': inverse_scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }

       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:Optional[Any] 							=       floats_tensor((1, 16, 16)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:Tuple 							=       floats_tensor((1, 2, 4, 16, 16)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:int 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:int 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Dict 							=       {
           '''prompt''': '''a dog and a newt''',
           '''mask_image''': mask,
           '''image_latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''inpaint_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:str 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:str 							=       image.cpu().permute(0					,  2					,  3					,  1    )[0]
       a						:Optional[Any] 							=       Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Dict 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Union[str, Any] 							=       {
           '''image''': image,
           '''source_prompt''': '''a cat and a frog''',
           '''target_prompt''': '''a dog and a newt''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''num_maps_per_mask''': 2,
           '''mask_encode_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:List[str] 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:int 							=       image.cpu().permute(0					,  2					,  3					,  1    )[0]
       a						:Optional[Any] 							=       Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Dict 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:List[Any] 							=       {
           '''image''': image,
           '''prompt''': '''a cat and a frog''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''inpaint_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''decode_latents''': True,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if not hasattr(self.pipeline_class					,  '''_optional_components'''    ):
             return

       a						:Any 							=       self.get_dummy_components()
       a						:str 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       # set all optional components to None and update pipeline config accordingly
       for optional_component in pipe._optional_components:
             setattr(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

       a						:Union[str, Any] 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:List[Any] 							=       pipe(**_lowerCamelCase    )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_lowerCamelCase    )
             a						:int 							=       self.pipeline_class.from_pretrained(_lowerCamelCase    )
             pipe_loaded.to(_lowerCamelCase    )
             pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase    )

       for optional_component in pipe._optional_components:
             self.assertTrue(
                 getattr(_lowerCamelCase					,  _lowerCamelCase    ) is None					,  F'''`{optional_component}` did not stay set to None after loading.'''					,  )

       a						:Dict 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:Tuple 							=       pipe_loaded(**_lowerCamelCase    )[0]

       a						:List[str] 							=       np.abs(output - output_loaded    ).max()
       self.assertLess(_lowerCamelCase					,  1e-4    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''

       a						:Optional[int] 							=       self.get_dummy_components()
       a						:List[str] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Any 							=       self.get_dummy_mask_inputs(_lowerCamelCase    )
       a						:str 							=       pipe.generate_mask(**_lowerCamelCase    )
       a						:List[str] 							=       mask[0, -3:, -3:]

       self.assertEqual(mask.shape					,  (1, 16, 16)    )
       a						:List[Any] 							=       np.array([0] * 9    )
       a						:str 							=       np.abs(mask_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )
       self.assertEqual(mask[0, -3, -4]					,  0    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''

       a						:List[str] 							=       self.get_dummy_components()
       a						:List[Any] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:str 							=       self.get_dummy_inversion_inputs(_lowerCamelCase    )
       a						:Any 							=       pipe.invert(**_lowerCamelCase    ).images
       a						:Union[str, Any] 							=       image[0, -1, -3:, -3:]

       self.assertEqual(image.shape					,  (2, 32, 32, 3)    )
       a						:str 							=       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]					,  )
       a						:str 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().test_inference_batch_single_identical(expected_max_diff=5e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       '''cpu'''

       a						:str 							=       self.get_dummy_components()

       a						:Union[str, Any] 							=       {'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
       a						:Dict 							=       DPMSolverMultistepScheduler(**_lowerCamelCase    )
       a						:List[str] 							=       DPMSolverMultistepInverseScheduler(**_lowerCamelCase    )

       a						:int 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:str 							=       self.get_dummy_inversion_inputs(_lowerCamelCase    )
       a						:Tuple 							=       pipe.invert(**_lowerCamelCase    ).images
       a						:Any 							=       image[0, -1, -3:, -3:]

       self.assertEqual(image.shape					,  (2, 32, 32, 3)    )
       a						:int 							=       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]					,  )
       a						:Tuple 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )







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




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




 @classmethod
 def        SCREAMING_SNAKE_CASE__				(		cls    ):
       a						:Tuple 							=       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''    )

       a						:Union[str, Any] 							=       raw_image.convert('''RGB'''    ).resize((768, 768)    )

       a						:List[Any] 							=       raw_image




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       torch.manual_seed(0    )

       a						:int 							=       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1'''					,  safety_checker=_lowerCamelCase					,  torch_dtype=torch.floataa    )
       a						:Optional[int] 							=       DDIMScheduler.from_config(pipe.scheduler.config    )
       a						:Optional[int] 							=       DDIMInverseScheduler.from_config(pipe.scheduler.config    )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Optional[Any] 							=       '''a bowl of fruit'''
       a						:Any 							=       '''a bowl of pears'''

       a						:Any 							=       pipe.generate_mask(
           image=self.raw_image					,  source_prompt=_lowerCamelCase					,  target_prompt=_lowerCamelCase					,  generator=_lowerCamelCase					,  )

       a						:Dict 							=       pipe.invert(
           prompt=_lowerCamelCase					,  image=self.raw_image					,  inpaint_strength=0.7					,  generator=_lowerCamelCase    ).latents

       a						:List[str] 							=       pipe(
           prompt=_lowerCamelCase					,  mask_image=_lowerCamelCase					,  image_latents=_lowerCamelCase					,  generator=_lowerCamelCase					,  negative_prompt=_lowerCamelCase					,  inpaint_strength=0.7					,  output_type='''numpy'''					,  ).images[0]

       a						:List[str] 							=       (
           np.array(
               load_image(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
           / 255
       )
       assert np.abs((expected_image - image).max()    ) < 5e-1




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       torch.manual_seed(0    )

       a						:List[Any] 							=       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1'''					,  safety_checker=_lowerCamelCase					,  torch_dtype=torch.floataa    )
       a						:Any 							=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
       a						:Union[str, Any] 							=       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Dict 							=       '''a bowl of fruit'''
       a						:Optional[Any] 							=       '''a bowl of pears'''

       a						:Tuple 							=       pipe.generate_mask(
           image=self.raw_image					,  source_prompt=_lowerCamelCase					,  target_prompt=_lowerCamelCase					,  generator=_lowerCamelCase					,  )

       a						:Dict 							=       pipe.invert(
           prompt=_lowerCamelCase					,  image=self.raw_image					,  inpaint_strength=0.7					,  generator=_lowerCamelCase					,  num_inference_steps=25					,  ).latents

       a						:str 							=       pipe(
           prompt=_lowerCamelCase					,  mask_image=_lowerCamelCase					,  image_latents=_lowerCamelCase					,  generator=_lowerCamelCase					,  negative_prompt=_lowerCamelCase					,  inpaint_strength=0.7					,  num_inference_steps=25					,  output_type='''numpy'''					,  ).images[0]

       a						:List[Any] 							=       (
           np.array(
               load_image(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
           / 255
       )
       assert np.abs((expected_image - image).max()    ) < 5e-1











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_ : List[str]    		=       8


def    A						(__A   :    Union[str, Any]   ,				__A   :    List[Any]=BITS       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

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

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

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


def    A						(__A   :    Dict   ,				__A   :    Tuple=BITS       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

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

     UpperCAmelCase_          =							rearrange(__A   ,				'''d -> d 1 1'''       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''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   :    List[Any]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    float = 0.0   ,				__A   :    bool = True   ,				__A   :    Tuple=None   ,				__A   :    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(__A   ,				-scale   ,				__A       )

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

          UpperCAmelCase_          =							prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )


def    A						(self   :    Optional[int]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    int="epsilon"   ,				__A   :    Optional[Any]=None   ,				__A   :    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(__A   ,				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(__A   ,				-scale   ,				__A       )

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

     UpperCAmelCase_          =							pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )







class __snake_case			(			a		):







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						Union[DDIMScheduler, DDPMScheduler]					,						_snake_case		:						Optional[float] = 1.0					,						):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							bit_scale
        UpperCAmelCase_          =							(
            ddim_bit_scheduler_step if isinstance(_snake_case					,						_snake_case) else ddpm_bit_scheduler_step
        )

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







   @torch.no_grad()
   def __call__(      self		:						Union[str, Any]					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 50					,						_snake_case		:						Optional[torch.Generator] = None					,						_snake_case		:						Optional[int] = 1					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						**_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


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

        self.scheduler.set_timesteps(_snake_case)

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

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

        UpperCAmelCase_          =							bits_to_decimal(_snake_case)

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)






