'''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


_lowerCAmelCase  :List[Any]  				=    8


def     __lowerCAmelCase						(       a_       ,      a_=BITS		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       x.device

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       (x * 255).int().clamp(0       ,      255		)

     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       2 ** torch.arange(bits - 1       ,      -1       ,      -1       ,      device=a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       rearrange(a_       ,      'd -> d 1 1'		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       rearrange(a_       ,      'b c h w -> b c 1 h w'		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ((x & mask) != 0).float()
     SCREAMING_SNAKE_CASE					:			str    	=       rearrange(a_       ,      'b c d h w -> b (c d) h w'		)
     SCREAMING_SNAKE_CASE					:			str    	=       bits * 2 - 1
     return bits


def     __lowerCAmelCase						(       a_       ,      a_=BITS		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.device

     SCREAMING_SNAKE_CASE					:			Tuple    	=       (x > 0).int()
     SCREAMING_SNAKE_CASE					:			Dict    	=       2 ** torch.arange(bits - 1       ,      -1       ,      -1       ,      device=a_       ,      dtype=torch.intaa		)

     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       rearrange(a_       ,      'd -> d 1 1'		)
     SCREAMING_SNAKE_CASE					:			int    	=       rearrange(a_       ,      'b (c d) h w -> b c d h w'       ,      d=8		)
     SCREAMING_SNAKE_CASE					:			int    	=       reduce(x * mask       ,      'b c d h w -> b c h w'       ,      'sum'		)
     return (dec / 255).clamp(0.0       ,      1.0		)


def     __lowerCAmelCase						(       self       ,      a_       ,      a_       ,      a_       ,      a_ = 0.0       ,      a_ = True       ,      a_=None       ,      a_ = 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)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       timestep - self.config.num_train_timesteps // self.num_inference_steps

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

     SCREAMING_SNAKE_CASE					:			int    	=       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
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

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

          SCREAMING_SNAKE_CASE					:			List[str]    	=       prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=a_       ,      pred_original_sample=a_		)


def     __lowerCAmelCase						(       self       ,      a_       ,      a_       ,      a_       ,      a_="epsilon"       ,      a_=None       ,      a_ = True       ,      )      ->	Union[DDPMSchedulerOutput, Tuple]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       timestep

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

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

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

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

     SCREAMING_SNAKE_CASE					:			str    	=       pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=a_       ,      pred_original_sample=a_		)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 1.0    ,							)							-> List[Any]:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       bit_scale
         SCREAMING_SNAKE_CASE					:			int    	=       (
             ddim_bit_scheduler_step if isinstance(lowercase__    ,							lowercase__ ) else ddpm_bit_scheduler_step
         )

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






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

         self.scheduler.set_timesteps(lowercase__ )

         for t in self.progress_bar(self.scheduler.timesteps ):
              # predict the noise residual
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.unet(lowercase__    ,							lowercase__ ).sample

              # compute the previous noisy sample x_t -> x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

         SCREAMING_SNAKE_CASE					:			int    	=       bits_to_decimal(lowercase__ )

         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Tuple    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )




'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         get_activation('gelu' )
         get_activation('gelu_10' )
         get_activation('gelu_fast' )
         get_activation('gelu_new' )
         get_activation('gelu_python' )
         get_activation('gelu_pytorch_tanh' )
         get_activation('linear' )
         get_activation('mish' )
         get_activation('quick_gelu' )
         get_activation('relu' )
         get_activation('sigmoid' )
         get_activation('silu' )
         get_activation('swish' )
         get_activation('tanh' )
         with self.assertRaises(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



