'''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


__a:			List[Any]	       =       8
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=BITS					):
 lowercase__    :   str						= x.device

 lowercase__    :   Any						= (x * 255).int().clamp(0		,					255					)

 lowercase__    :   int						= 2 ** torch.arange(bits - 1		,					-1		,					-1		,					device=UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= rearrange(UpperCAmelCase		,					'''d -> d 1 1'''					)
 lowercase__    :   Any						= rearrange(UpperCAmelCase		,					'''b c h w -> b c 1 h w'''					)

 lowercase__    :   Any						= ((x & mask) != 0).float()
 lowercase__    :   Tuple						= rearrange(UpperCAmelCase		,					'''b c d h w -> b (c d) h w'''					)
 lowercase__    :   str						= bits * 2 - 1
 return bits
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=BITS					):
 lowercase__    :   str						= x.device

 lowercase__    :   int						= (x > 0).int()
 lowercase__    :   Optional[int]						= 2 ** torch.arange(bits - 1		,					-1		,					-1		,					device=UpperCAmelCase		,					dtype=torch.intaa					)

 lowercase__    :   Optional[Any]						= rearrange(UpperCAmelCase		,					'''d -> d 1 1'''					)
 lowercase__    :   Optional[int]						= rearrange(UpperCAmelCase		,					'''b (c d) h w -> b c d h w'''		,					d=8					)
 lowercase__    :   Dict						= reduce(x * mask		,					'''b c d h w -> b c h w'''		,					'''sum'''					)
 return (dec / 255).clamp(0.0		,					1.0					)
def       __UpperCamelCase	(       self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = True		,					UpperCAmelCase=None		,					UpperCAmelCase = True		,					):
 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)
 lowercase__    :   Union[str, Any]						= timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 lowercase__    :   Any						= self.alphas_cumprod[timestep]
 lowercase__    :   int						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 lowercase__    :   Tuple						= 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
 lowercase__    :   str						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 lowercase__    :   Optional[Any]						= self.bit_scale
 if self.config.clip_sample:
  lowercase__    :   Optional[int]						= 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)
 lowercase__    :   Optional[Any]						= self._get_variance(UpperCAmelCase		,					UpperCAmelCase					)
 lowercase__    :   Tuple						= eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  lowercase__    :   str						= (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
 lowercase__    :   Optional[Any]						= (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
 lowercase__    :   Optional[int]						= 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
  lowercase__    :   Optional[Any]						= model_output.device if torch.is_tensor(UpperCAmelCase					) else '''cpu'''
  lowercase__    :   Any						= torch.randn(model_output.shape		,					dtype=model_output.dtype		,					generator=UpperCAmelCase					).to(UpperCAmelCase					)
  lowercase__    :   Any						= self._get_variance(UpperCAmelCase		,					UpperCAmelCase					) ** 0.5 * eta * noise

  lowercase__    :   int						= prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=UpperCAmelCase		,					pred_original_sample=UpperCAmelCase					)
def       __UpperCamelCase	(       self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="epsilon"		,					UpperCAmelCase=None		,					UpperCAmelCase = True		,					):
 lowercase__    :   Any						= timestep

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

 # 1. compute alphas, betas
 lowercase__    :   Optional[Any]						= self.alphas_cumprod[t]
 lowercase__    :   Optional[int]						= self.alphas_cumprod[t - 1] if t > 0 else self.one
 lowercase__    :   int						= 1 - alpha_prod_t
 lowercase__    :   Dict						= 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":
  lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  lowercase__    :   List[str]						= model_output
 else:
  raise ValueError(F"""Unsupported prediction_type {prediction_type}."""					)

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

 # 6. Add noise
 lowercase__    :   List[str]						= 0
 if t > 0:
  lowercase__    :   Optional[Any]						= torch.randn(
      model_output.size()		,					dtype=model_output.dtype		,					layout=model_output.layout		,					generator=UpperCAmelCase					).to(model_output.device					)
  lowercase__    :   Any						= (self._get_variance(UpperCAmelCase		,					predicted_variance=UpperCAmelCase					) ** 0.5) * noise

 lowercase__    :   Optional[int]						= pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=UpperCAmelCase		,					pred_original_sample=UpperCAmelCase					)





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       )  ->  str:
        super().__init__()
        lowercase__    :   List[str]						= bit_scale
        lowercase__    :   str						= (
            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     ,       __lowerCAmelCase = 256     ,       __lowerCAmelCase = 256     ,       __lowerCAmelCase = 50     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 1     ,       __lowerCAmelCase = "pil"     ,       __lowerCAmelCase = True     ,       **__lowerCAmelCase     ,       )  ->  Union[Tuple, ImagePipelineOutput]:
        lowercase__    :   str						= torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)     ,       generator=__lowerCAmelCase     ,       )
        lowercase__    :   List[Any]						= decimal_to_bits(__lowerCAmelCase			) * self.bit_scale
        lowercase__    :   List[str]						= latents.to(self.device			)

        self.scheduler.set_timesteps(__lowerCAmelCase			)

        for t in self.progress_bar(self.scheduler.timesteps			):
         # predict the noise residual
         lowercase__    :   List[str]						= self.unet(__lowerCAmelCase     ,       __lowerCAmelCase			).sample

         # compute the previous noisy sample x_t -> x_t-1
         lowercase__    :   List[Any]						= self.scheduler.step(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			).prev_sample

        lowercase__    :   int						= bits_to_decimal(__lowerCAmelCase			)

        if output_type == "pil":
         lowercase__    :   int						= self.numpy_to_pil(__lowerCAmelCase			)

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__lowerCAmelCase			)

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   DDIMPipeline
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE 		=   PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= UNetaDModel(
            block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=3     ,       out_channels=3     ,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')     ,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')     ,       )
        lowercase__    :   Any						= DDIMScheduler()
        lowercase__    :   Union[str, Any]						= {'''unet''': unet, '''scheduler''': scheduler}
        return components

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=0			)  ->  int:
        if str(__lowerCAmelCase			).startswith('''mps'''			):
         lowercase__    :   Optional[Any]						= torch.manual_seed(__lowerCAmelCase			)
        else:
         lowercase__    :   int						= torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
        lowercase__    :   List[Any]						= {
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   int						= '''cpu'''

        lowercase__    :   Optional[Any]						= self.get_dummy_components()
        lowercase__    :   str						= self.pipeline_class(**__lowerCAmelCase			)
        pipe.to(__lowerCAmelCase			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   str						= self.get_dummy_inputs(__lowerCAmelCase			)
        lowercase__    :   str						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        self.assertEqual(image.shape     ,       (1, 32, 32, 3)			)
        lowercase__    :   Optional[int]						= np.array(
            [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
        lowercase__    :   Optional[Any]						= np.abs(image_slice.flatten() - expected_slice			).max()
        self.assertLessEqual(__lowerCAmelCase     ,       1E-3			)

       def _lowerCAmelCase(						self			)  ->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().test_save_load_local(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        super().test_save_load_optional_components(expected_max_difference=3E-3			)


       def _lowerCAmelCase(						self			)  ->  int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3			)



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





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= '''google/ddpm-cifar10-32'''

        lowercase__    :   List[Any]						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   str						= DDIMScheduler()

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddim.to(__lowerCAmelCase			)
        ddim.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Dict						= torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= ddim(generator=__lowerCAmelCase     ,       eta=0.0     ,       output_type='''numpy'''			).images

        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        lowercase__    :   Optional[int]						= np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= '''google/ddpm-ema-bedroom-256'''

        lowercase__    :   Tuple						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= DDIMScheduler.from_pretrained(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddpm.to(__lowerCAmelCase			)
        ddpm.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= torch.manual_seed(0			)
        lowercase__    :   Optional[Any]						= ddpm(generator=__lowerCAmelCase     ,       output_type='''numpy'''			).images

        lowercase__    :   List[Any]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        lowercase__    :   Union[str, Any]						= np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

