






'''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:      Tuple  				=				8






def 	_UpperCAmelCase    (  a		:	Optional[int]						,			a		:	int=BITS							)			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			x.device

						lowercase_      :   Optional[Any]        =			(x * 2_5_5).int().clamp(0						,			2_5_5							)

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

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






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	int=BITS							)			->			Optional[Any]:





						"""simple docstring"""
						lowercase_      :   Union[str, Any]        =			x.device

						lowercase_      :   List[Any]        =			(x > 0).int()
						lowercase_      :   Optional[Any]        =			2 ** torch.arange(bits - 1						,			-1						,			-1						,			device=a						,			dtype=torch.intaa							)

						lowercase_      :   int        =			rearrange(a						,			'd -> d 1 1'							)
						lowercase_      :   Tuple        =			rearrange(a						,			'b (c d) h w -> b c d h w'						,			d=8							)
						lowercase_      :   List[Any]        =			reduce(x * mask						,			'b c d h w -> b c h w'						,			'sum'							)
						return (dec / 2_5_5).clamp(0.0						,			1.0							)






def 	_UpperCAmelCase    (  self		:	Optional[Any]						,			a		:	torch.FloatTensor						,			a		:	int						,			a		:	torch.FloatTensor						,			a		:	float = 0.0						,			a		:	bool = True						,			a		:	int=None						,			a		:	bool = 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)
						lowercase_      :   List[Any]        =			timestep - self.config.num_train_timesteps // self.num_inference_steps

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

						lowercase_      :   Union[str, Any]        =			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_      :   Union[str, Any]        =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						lowercase_      :   Any        =			self.bit_scale
						if self.config.clip_sample:
												lowercase_      :   str        =			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)
						lowercase_      :   str        =			self._get_variance(a						,			a							)
						lowercase_      :   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
												lowercase_      :   int        =			(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_      :   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
						lowercase_      :   Dict        =			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_      :   int        =			model_output.device if torch.is_tensor(a							) else 'cpu'
												lowercase_      :   Any        =			torch.randn(model_output.shape						,			dtype=model_output.dtype						,			generator=a							).to(a							)
												lowercase_      :   Union[str, Any]        =			self._get_variance(a						,			a							) ** 0.5 * eta * noise

												lowercase_      :   List[str]        =			prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=a						,			pred_original_sample=a							)






def 	_UpperCAmelCase    (  self		:	Any						,			a		:	torch.FloatTensor						,			a		:	int						,			a		:	torch.FloatTensor						,			a		:	Tuple="epsilon"						,			a		:	Any=None						,			a		:	bool = True						,			)			->			Union[DDPMSchedulerOutput, Tuple]:





						"""simple docstring"""
						lowercase_      :   Tuple        =			timestep

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

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

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

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

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

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=a						,			pred_original_sample=a							)






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase = 1.0  ,      )						->  Union[str, Any]:
							super().__init__()
							lowercase_      :   Union[str, Any]        =			bit_scale
							lowercase_      :   Union[str, Any]        =			(
							    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]:
							lowercase_      :   List[Any]        =			torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)  ,      generator=_lowercase  ,      )
							lowercase_      :   Union[str, Any]        =			decimal_to_bits(_lowercase       ) * self.bit_scale
							lowercase_      :   Optional[int]        =			latents.to(self.device       )

							self.scheduler.set_timesteps(_lowercase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													# predict the noise residual
													lowercase_      :   Union[str, Any]        =			self.unet(_lowercase  ,      _lowercase       ).sample

													# compute the previous noisy sample x_t -> x_t-1
													lowercase_      :   Tuple        =			self.scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							lowercase_      :   List[str]        =			bits_to_decimal(_lowercase       )

							if output_type == "pil":
													lowercase_      :   Optional[int]        =			self.numpy_to_pil(_lowercase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_lowercase       )








'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   Tuple        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   Tuple        =			[
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   int        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    # Removed: 'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Dict        =			[
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   List[Any]        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    # Removed: 'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   str        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Union[str, Any]        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Optional[int]        =			[
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							# pass variant but use the non-variant filenames
							lowercase_      :   Optional[int]        =			[
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   int        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertFalse(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   str        =			[
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							]
							lowercase_      :   str        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							# pass variant but use the non-variant filenames
							lowercase_      :   List[Any]        =			[
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )







	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Union[str, Any]        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    # 'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertFalse(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
