



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__								=						8



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=BITS      )   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	x.device

				snake_case__	:      Union[str, Any]     		=	(x * 255).int().clamp(0  ,  255      )

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

				snake_case__	:      List[Any]     		=	((x & mask) != 0).float()
				snake_case__	:      Union[str, Any]     		=	rearrange(__lowerCAmelCase  ,  '''b c d h w -> b (c d) h w'''      )
				snake_case__	:      Dict     		=	bits * 2 - 1
				return bits



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=BITS      )   ->					Dict:


				"""simple docstring"""

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

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

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



def 			_lowerCAmelCase       (					self  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase = 0.0  ,  __lowerCAmelCase = True  ,  __lowerCAmelCase=None  ,  __lowerCAmelCase = 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)
				snake_case__	:      Tuple     		=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

				snake_case__	:      Dict     		=	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__	:      Optional[Any]     		=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				snake_case__	:      int     		=	self.bit_scale
				if self.config.clip_sample:
								snake_case__	:      str     		=	torch.clamp(__lowerCAmelCase  ,  -scale  ,  __lowerCAmelCase      )

				# 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__	:      Any     		=	self._get_variance(__lowerCAmelCase  ,  __lowerCAmelCase      )
				snake_case__	:      Optional[int]     		=	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__	:      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
				snake_case__	:      List[str]     		=	(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__	:      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
								snake_case__	:      List[str]     		=	model_output.device if torch.is_tensor(__lowerCAmelCase      ) else '''cpu'''
								snake_case__	:      Tuple     		=	torch.randn(model_output.shape  ,  dtype=model_output.dtype  ,  generator=__lowerCAmelCase      ).to(__lowerCAmelCase      )
								snake_case__	:      Any     		=	self._get_variance(__lowerCAmelCase  ,  __lowerCAmelCase      ) ** 0.5 * eta * noise

								snake_case__	:      int     		=	prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase  ,  pred_original_sample=__lowerCAmelCase      )



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


				"""simple docstring"""

				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__	:      List[Any]     		=	torch.split(__lowerCAmelCase  ,  sample.shape[1]  ,  dim=1      )
				else:
								snake_case__	:      Optional[int]     		=	None

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

				# 3. Clip "predicted x_0"
				snake_case__	:      Tuple     		=	self.bit_scale
				if self.config.clip_sample:
								snake_case__	:      List[str]     		=	torch.clamp(__lowerCAmelCase  ,  -scale  ,  __lowerCAmelCase      )

				# 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__	:      Tuple     		=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				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
				snake_case__	:      int     		=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				snake_case__	:      str     		=	0
				if t > 0:
								snake_case__	:      Tuple     		=	torch.randn(
								    model_output.size()  ,  dtype=model_output.dtype  ,  layout=model_output.layout  ,  generator=__lowerCAmelCase      ).to(model_output.device      )
								snake_case__	:      Tuple     		=	(self._get_variance(__lowerCAmelCase  ,  predicted_variance=__lowerCAmelCase      ) ** 0.5) * noise

				snake_case__	:      Optional[Any]     		=	pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase  ,  pred_original_sample=__lowerCAmelCase      )





class       a							(			__lowerCamelCase				):



		def __init__(     self     :Union[str, Any]   ,__lowercase     :UNetaDConditionModel   ,__lowercase     :Union[DDIMScheduler, DDPMScheduler]   ,__lowercase     :Optional[float] = 1.0   ,):
						super().__init__()
						snake_case__	:      List[str]     		=	bit_scale
						snake_case__	:      str     		=	(
						    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     :List[Any]   ,__lowercase     :Optional[int] = 2_5_6   ,__lowercase     :Optional[int] = 2_5_6   ,__lowercase     :Optional[int] = 5_0   ,__lowercase     :Optional[torch.Generator] = None   ,__lowercase     :Optional[int] = 1   ,__lowercase     :Optional[str] = "pil"   ,__lowercase     :bool = True   ,**__lowercase     :Any   ,):
						snake_case__	:      Optional[int]     		=	torch.randn(
						    (batch_size, self.unet.config.in_channels, height, width)   ,generator=__lowercase   ,)
						snake_case__	:      List[str]     		=	decimal_to_bits(__lowercase  ) * self.bit_scale
						snake_case__	:      str     		=	latents.to(self.device  )

						self.scheduler.set_timesteps(__lowercase  )

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

										# compute the previous noisy sample x_t -> x_t-1
										snake_case__	:      List[str]     		=	self.scheduler.step(__lowercase   ,__lowercase   ,__lowercase  ).prev_sample

						snake_case__	:      Optional[Any]     		=	bits_to_decimal(__lowercase  )

						if output_type == "pil":
										snake_case__	:      Optional[int]     		=	self.numpy_to_pil(__lowercase  )

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=__lowercase  )






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       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      Union[str, Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      int     		=	get_activation('''gelu'''  )
						self.assertTrue(torch.allclose(gelu_python(__lowercase  )   ,torch_builtin(__lowercase  )  )  )
						self.assertFalse(torch.allclose(gelu_python(__lowercase  )   ,gelu_new(__lowercase  )  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      List[Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      Union[str, Any]     		=	get_activation('''gelu'''  )
						snake_case__	:      int     		=	get_activation('''gelu_10'''  )

						snake_case__	:      Optional[int]     		=	torch_builtin(__lowercase  )
						snake_case__	:      str     		=	geluaa(__lowercase  )

						snake_case__	:      Tuple     		=	torch.where(y_gelu_aa < 10.0   ,1   ,0  )

						self.assertTrue(torch.max(__lowercase  ).item() == 10.0  )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,y_gelu_aa * clipped_mask  )  )



		def 	__lowerCamelCase			(     self     :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 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	get_activation('''gelu'''  )
						snake_case__	:      List[Any]     		=	1
						snake_case__	:      Optional[Any]     		=	get_activation('''gelu'''  )
						self.assertEqual(acta.a   ,1  )
						with self.assertRaises(__lowercase  ):
										snake_case__	:      str     		=	acta.a

