





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


__UpperCAmelCase       :   List[Any]				       =							8







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=BITS)   ->    List[str]:
  __snake_case:						Tuple   =						x.device

  __snake_case:						str   =						(x * 255).int().clamp(0					,					255)

  __snake_case:						List[str]   =						2 ** torch.arange(bits - 1					,					-1					,					-1					,					device=SCREAMING_SNAKE_CASE__)
  __snake_case:						Union[str, Any]   =						rearrange(SCREAMING_SNAKE_CASE__					,					"""d -> d 1 1""")
  __snake_case:						Any   =						rearrange(SCREAMING_SNAKE_CASE__					,					"""b c h w -> b c 1 h w""")

  __snake_case:						Optional[Any]   =						((x & mask) != 0).float()
  __snake_case:						Tuple   =						rearrange(SCREAMING_SNAKE_CASE__					,					"""b c d h w -> b (c d) h w""")
  __snake_case:						int   =						bits * 2 - 1
  return bits







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=BITS)   ->    int:
  __snake_case:						List[str]   =						x.device

  __snake_case:						Union[str, Any]   =						(x > 0).int()
  __snake_case:						List[Any]   =						2 ** torch.arange(bits - 1					,					-1					,					-1					,					device=SCREAMING_SNAKE_CASE__					,					dtype=torch.intaa)

  __snake_case:						List[str]   =						rearrange(SCREAMING_SNAKE_CASE__					,					"""d -> d 1 1""")
  __snake_case:						List[str]   =						rearrange(SCREAMING_SNAKE_CASE__					,					"""b (c d) h w -> b c d h w"""					,					d=8)
  __snake_case:						Optional[Any]   =						reduce(x * mask					,					"""b c d h w -> b c h w"""					,					"""sum""")
  return (dec / 255).clamp(0.0					,					1.0)







def 			A__							(    self					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__ = 0.0					,					SCREAMING_SNAKE_CASE__ = True					,					SCREAMING_SNAKE_CASE__=None					,					SCREAMING_SNAKE_CASE__ = True					,					)   ->    Union[DDIMSchedulerOutput, Tuple]:
  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)
  __snake_case:						Optional[int]   =						timestep - self.config.num_train_timesteps // self.num_inference_steps

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

  __snake_case:						Optional[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
  __snake_case:						Dict   =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __snake_case:						List[Any]   =						self.bit_scale
  if self.config.clip_sample:
    __snake_case:						List[str]   =						torch.clamp(SCREAMING_SNAKE_CASE__					,					-scale					,					SCREAMING_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)
  __snake_case:						Optional[Any]   =						self._get_variance(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  __snake_case:						Dict   =						eta * variance ** 0.5

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

    __snake_case:						Optional[int]   =						prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__					,					pred_original_sample=SCREAMING_SNAKE_CASE__)







def 			A__							(    self					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__="epsilon"					,					SCREAMING_SNAKE_CASE__=None					,					SCREAMING_SNAKE_CASE__ = True					,					)   ->    Union[DDPMSchedulerOutput, Tuple]:
  __snake_case:						Dict   =						timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    __snake_case  ,   __snake_case:						Optional[int]   =						torch.split(SCREAMING_SNAKE_CASE__					,					sample.shape[1]					,					dim=1)
  else:
    __snake_case:						List[str]   =						None

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

  # 3. Clip "predicted x_0"
  __snake_case:						int   =						self.bit_scale
  if self.config.clip_sample:
    __snake_case:						int   =						torch.clamp(SCREAMING_SNAKE_CASE__					,					-scale					,					SCREAMING_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
  __snake_case:						Dict   =						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __snake_case:						Union[str, Any]   =						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
  __snake_case:						List[Any]   =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

  __snake_case:						Union[str, Any]   =						pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__					,					pred_original_sample=SCREAMING_SNAKE_CASE__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    List[str]							,				A :    UNetaDConditionModel							,				A :    Union[DDIMScheduler, DDPMScheduler]							,				A :    Optional[float] = 1.0							,				):
         super().__init__()
         __snake_case:						Optional[int]   =						bit_scale
         __snake_case:						List[Any]   =						(
             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 :    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 :    Tuple							,				):
         __snake_case:						Optional[Any]   =						torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)							,				generator=A							,				)
         __snake_case:						int   =						decimal_to_bits(A		) * self.bit_scale
         __snake_case:						List[Any]   =						latents.to(self.device		)

         self.scheduler.set_timesteps(A		)

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

           # compute the previous noisy sample x_t -> x_t-1
           __snake_case:						int   =						self.scheduler.step(A							,				A							,				A		).prev_sample

         __snake_case:						int   =						bits_to_decimal(A		)

         if output_type == "pil":
           __snake_case:						Optional[int]   =						self.numpy_to_pil(A		)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=A		)













import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp







@slow
@require_flax
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Dict							,				A :    Any		):
         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A		) for s in shape]		)}.npy'''




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[int]=0							,				A :    Tuple=(4, 4, 64, 64)							,				A :    Tuple=False		):
         __snake_case:						Dict   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						str   =						jnp.array(load_hf_numpy(self.get_file_format(A							,				A		)		)							,				dtype=A		)
         return image




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Any=False							,				A :    Optional[Any]="CompVis/stable-diffusion-v1-4"		):
         __snake_case:						List[Any]   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Union[str, Any]   =						"""bf16""" if fpaa else None

         __snake_case  ,   __snake_case:						Optional[int]   =						FlaxUNetaDConditionModel.from_pretrained(
             A							,				subfolder="""unet"""							,				dtype=A							,				revision=A		)
         return model, params




       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    Tuple=0							,				A :    str=(4, 77, 768)							,				A :    List[str]=False		):
         __snake_case:						Any   =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Dict   =						jnp.array(load_hf_numpy(self.get_file_format(A							,				A		)		)							,				dtype=A		)
         return hidden_states




       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
               [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
               [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
               [3, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
               # fmt: on
           ]		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[Any]							,				A :    str							,				A :    Any		):
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=A		)
         __snake_case:						Tuple   =						self.get_latents(A							,				fpaa=A		)
         __snake_case:						int   =						self.get_encoder_hidden_states(A							,				fpaa=A		)

         __snake_case:						List[Any]   =						model.apply(
             {"""params""": params}							,				A							,				jnp.array(A							,				dtype=jnp.intaa		)							,				encoder_hidden_states=A							,				).sample

         assert sample.shape == latents.shape

         __snake_case:						str   =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,				dtype=jnp.floataa		)
         __snake_case:						Optional[int]   =						jnp.array(A							,				dtype=jnp.floataa		)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(A							,				A							,				atol=1E-2		)






       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
               [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
               [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
               [3, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
               # fmt: on
           ]		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    int							,				A :    Tuple							,				A :    List[str]		):
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=A		)
         __snake_case:						Optional[int]   =						self.get_latents(A							,				shape=(4, 4, 96, 96)							,				fpaa=A		)
         __snake_case:						str   =						self.get_encoder_hidden_states(A							,				shape=(4, 77, 1_024)							,				fpaa=A		)

         __snake_case:						str   =						model.apply(
             {"""params""": params}							,				A							,				jnp.array(A							,				dtype=jnp.intaa		)							,				encoder_hidden_states=A							,				).sample

         assert sample.shape == latents.shape

         __snake_case:						Dict   =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)							,				dtype=jnp.floataa		)
         __snake_case:						Any   =						jnp.array(A							,				dtype=jnp.floataa		)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(A							,				A							,				atol=1E-2		)






