'''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


SCREAMING_SNAKE_CASE_:					Tuple  =8





def      lowerCAmelCase_    ( snake_case_ ,   snake_case_=BITS      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       x.device

     UpperCAmelCase_	   =       (x * 2_55).int().clamp(0 ,   2_55      )

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

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





def      lowerCAmelCase_    ( snake_case_ ,   snake_case_=BITS      )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       x.device

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

     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "d -> d 1 1"      )
     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "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 / 2_55).clamp(0.0 ,   1.0      )





def      lowerCAmelCase_    ( self ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ = 0.0 ,   snake_case_ = True ,   snake_case_=None ,   snake_case_ = 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(snake_case_ ,   -scale ,   snake_case_      )

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

          UpperCAmelCase_	   =       prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=snake_case_ ,   pred_original_sample=snake_case_      )





def      lowerCAmelCase_    ( self ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_="epsilon" ,   snake_case_=None ,   snake_case_ = 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(snake_case_ ,   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(snake_case_ ,   -scale ,   snake_case_      )

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

     UpperCAmelCase_	   =       pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=snake_case_ ,   pred_original_sample=snake_case_      )






class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[str]       ,      __a   :   UNetaDConditionModel       ,      __a   :   Union[DDIMScheduler, DDPMScheduler]       ,      __a   :   Optional[float] = 1.0       ,      ):
         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[Any]       ,      __a   :   Optional[int] = 256       ,      __a   :   Optional[int] = 256       ,      __a   :   Optional[int] = 50       ,      __a   :   Optional[torch.Generator] = None       ,      __a   :   Optional[int] = 1       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Union[str, Any]       ,      ):
         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			)





'''simple docstring'''





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class 	__A				:



    @property
    def  _lowercase						(self   :   int			):
         return self.get_dummy_input()



    @property
    def  _lowercase						(self   :   Any			):
         if self.block_type == "down":
              return (4, 32, 16, 16)
         elif self.block_type == "mid":
              return (4, 32, 32, 32)
         elif self.block_type == "up":
              return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[str]=True       ,      __a   :   Any=False       ,      __a   :   List[Any]=False       ,      __a   :   Any=False       ,      ):
         UpperCAmelCase_	   =       4
         UpperCAmelCase_	   =       32
         UpperCAmelCase_	   =       (32, 32)

         UpperCAmelCase_	   =       torch.manual_seed(0			)
         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       (batch_size, num_channels) + sizes
         UpperCAmelCase_	   =       randn_tensor(__a       ,      generator=__a       ,      device=__a			)
         UpperCAmelCase_	   =       {"hidden_states": hidden_states}

         if include_temb:
              UpperCAmelCase_	   =       128
              UpperCAmelCase_	   =       randn_tensor((batch_size, temb_channels)       ,      generator=__a       ,      device=__a			)

         if include_res_hidden_states_tuple:
              UpperCAmelCase_	   =       torch.manual_seed(1			)
              UpperCAmelCase_	   =       (randn_tensor(__a       ,      generator=__a       ,      device=__a			),)

         if include_encoder_hidden_states:
              UpperCAmelCase_	   =       floats_tensor((batch_size, 32, 32)			).to(__a			)

         if include_skip_sample:
              UpperCAmelCase_	   =       randn_tensor(((batch_size, 3) + sizes)       ,      generator=__a       ,      device=__a			)

         return dummy_input



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       {
             "in_channels": 32,
             "out_channels": 32,
             "temb_channels": 128,
         }
         if self.block_type == "up":
              UpperCAmelCase_	   =       32

         if self.block_type == "mid":
              init_dict.pop("out_channels"			)

         UpperCAmelCase_	   =       self.dummy_input
         return init_dict, inputs_dict



    def  _lowercase						(self   :   Tuple       ,      __a   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         unet_block.to(__a			)
         unet_block.eval()

         with torch.no_grad():
              UpperCAmelCase_	   =       unet_block(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         self.assertEqual(output.shape       ,      self.output_shape			)

         UpperCAmelCase_	   =       output[0, -1, -3:, -3:]
         UpperCAmelCase_	   =       torch.tensor(__a			).to(__a			)
         assert torch_all_close(output_slice.flatten()       ,      __a       ,      atol=5E-3			)






    @unittest.skipIf(torch_device == "mps"       ,      "Training is not supported in mps"			)
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         model.to(__a			)
         model.train()
         UpperCAmelCase_	   =       model(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       randn_tensor(output.shape       ,      device=__a			)
         UpperCAmelCase_	   =       torch.nn.functional.mse_loss(__a       ,      __a			)
         loss.backward()




