






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					:		Optional[int]	  =						8
def      a_ (      _A      ,   _A=BITS  )			->			List[Any]:







						"""simple docstring"""
						snake_case__									=     x.device

						snake_case__									=     (x * 255).int().clamp(0      ,   255  )

						snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A  )
						snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
						snake_case__									=     rearrange(_A      ,   'b c h w -> b c 1 h w'  )

						snake_case__									=     ((x & mask) != 0).float()
						snake_case__									=     rearrange(_A      ,   'b c d h w -> b (c d) h w'  )
						snake_case__									=     bits * 2 - 1
						return bits
def      a_ (      _A      ,   _A=BITS  )			->			List[str]:







						"""simple docstring"""
						snake_case__									=     x.device

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

						snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
						snake_case__									=     rearrange(_A      ,   'b (c d) h w -> b c d h w'      ,   d=8  )
						snake_case__									=     reduce(x * mask      ,   'b c d h w -> b c h w'      ,   'sum'  )
						return (dec / 255).clamp(0.0      ,   1.0  )
def      a_ (      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)
						snake_case__									=     timestep - self.config.num_train_timesteps // self.num_inference_steps

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

						snake_case__									=     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__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						snake_case__									=     self.bit_scale
						if self.config.clip_sample:
												snake_case__									=     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)
						snake_case__									=     self._get_variance(_A      ,   _A  )
						snake_case__									=     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__									=     (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__									=     (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__									=     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__									=     model_output.device if torch.is_tensor(_A  ) else 'cpu'
												snake_case__									=     torch.randn(model_output.shape      ,   dtype=model_output.dtype      ,   generator=_A  ).to(_A  )
												snake_case__									=     self._get_variance(_A      ,   _A  ) ** 0.5 * eta * noise

												snake_case__									=     prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A="epsilon"      ,   _A=None      ,   _A = True      ,   )			->			Union[DDPMSchedulerOutput, Tuple]:







						"""simple docstring"""
						snake_case__									=     timestep

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

						# 1. compute alphas, betas
						snake_case__									=     self.alphas_cumprod[t]
						snake_case__									=     self.alphas_cumprod[t - 1] if t > 0 else self.one
						snake_case__									=     1 - alpha_prod_t
						snake_case__									=     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__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												snake_case__									=     model_output
						else:
												raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''  )

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

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

						snake_case__									=     pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )


class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDConditionModel       ,	UpperCamelCase:      Union[DDIMScheduler, DDPMScheduler]       ,	UpperCamelCase:      Optional[float] = 1.0       ,	)		->		Union[str, Any]:
													super().__init__()
													snake_case__									=     bit_scale
													snake_case__									=     (
													    ddim_bit_scheduler_step if isinstance(UpperCamelCase       ,	UpperCamelCase						) else ddpm_bit_scheduler_step
													)

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


							@torch.no_grad()
							def __call__(     self:      Dict       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 50       ,	UpperCamelCase:      Optional[torch.Generator] = None       ,	UpperCamelCase:      Optional[int] = 1       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[Tuple, ImagePipelineOutput]:
													snake_case__									=     torch.randn(
													    (batch_size, self.unet.config.in_channels, height, width)       ,	generator=UpperCamelCase       ,	)
													snake_case__									=     decimal_to_bits(UpperCamelCase						) * self.bit_scale
													snake_case__									=     latents.to(self.device						)

													self.scheduler.set_timesteps(UpperCamelCase						)

													for t in self.progress_bar(self.scheduler.timesteps						):
																			# predict the noise residual
																			snake_case__									=     self.unet(UpperCamelCase       ,	UpperCamelCase						).sample

																			# compute the previous noisy sample x_t -> x_t-1
																			snake_case__									=     self.scheduler.step(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						).prev_sample

													snake_case__									=     bits_to_decimal(UpperCamelCase						)

													if output_type == "pil":
																			snake_case__									=     self.numpy_to_pil(UpperCamelCase						)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=UpperCamelCase						)












import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)




