






'''simple docstring'''



import os
from collections.abc import Iterator


def 				lowercase_					(     __A			:							str = "."      )							->   Iterator[str]:



 """simple docstring"""




 for dir_path, dir_names, filenames in os.walk(__A      ):
  lowercase       : List[Any]						=[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
  for filename in filenames:
   if filename == "__init__.py":
    continue
   if os.path.splitext(__A      )[1] in (".py", ".ipynb"):
    yield os.path.join(__A       ,   __A      ).lstrip('''./'''      )


def 				lowercase_					(     __A			:							Dict      )							->   int:



 """simple docstring"""




 return F'{i * "  "}*' if i else "\n##"


def 				lowercase_					(     __A			:							str       ,   __A			:							str      )							->   str:



 """simple docstring"""




 lowercase       : Any						=old_path.split(os.sep      )
 for i, new_part in enumerate(new_path.split(os.sep      )      ):
  if (i + 1 > len(__A      ) or old_parts[i] != new_part) and new_part:
   print(F'{md_prefix(__A      )} {new_part.replace("_"       ,   " "      ).title()}'      )
 return new_path


def 				lowercase_					(     __A			:							str = "."      )							->   None:



 """simple docstring"""




 lowercase       : List[str]						=''''''
 for filepath in sorted(good_file_paths(__A      )      ):
  lowercase	,		lowercase       : List[Any]						=os.path.split(__A      )
  if filepath != old_path:
   lowercase       : List[str]						=print_path(__A       ,   __A      )
  lowercase       : List[Any]						=(filepath.count(os.sep      ) + 1) if filepath else 0
  lowercase       : str						=F'{filepath}/{filename}'.replace(''' '''       ,   '''%20'''      )
  lowercase       : List[str]						=os.path.splitext(filename.replace('''_'''       ,   ''' '''      ).title()      )[0]
  print(F'{md_prefix(__A      )} [{filename}]({url})'      )


if __name__ == "__main__":
      print_directory_md('.')







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=0.9_99   ,						_lowerCAmelCase="cosine"   ,						)    ->						int:


						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCAmelCase			):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowerCAmelCase			):
																		return math.exp(t * -12.0			)

						else:
												raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

						__snake_case				=		[]
						for i in range(_lowerCAmelCase			):
												__snake_case				=		i / num_diffusion_timesteps
												__snake_case				=		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase			) / alpha_bar_fn(_lowerCAmelCase			)   ,						_lowerCAmelCase			)			)
						return torch.tensor(_lowerCAmelCase   ,						dtype=torch.floataa			)






class      UpperCamelCase(	_a  ,				_a ):
					snake_case_		:					Tuple    					=						[e.name for e in KarrasDiffusionSchedulers]
					snake_case_		:					Union[str, Any]    					=						2


					@register_to_config
					def __init__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		int = 1_0_0_0  ,     SCREAMING_SNAKE_CASE						:		float = 0.00085  ,     SCREAMING_SNAKE_CASE						:		float = 0.012  ,     SCREAMING_SNAKE_CASE						:		str = "linear"  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[np.ndarray, List[float]]] = None  ,     SCREAMING_SNAKE_CASE						:		str = "epsilon"  ,     SCREAMING_SNAKE_CASE						:		str = "linspace"  ,     SCREAMING_SNAKE_CASE						:		int = 0  ,     )      ->						Optional[int]:




											'''simple docstring'''

											if trained_betas is not None:
																	__snake_case				=		torch.tensor(SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											elif beta_schedule == "linear":
																	__snake_case				=		torch.linspace(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	__snake_case				=		(
																	    torch.linspace(beta_start**0.5  ,     beta_end**0.5  ,     SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    ) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	__snake_case				=		betas_for_alpha_bar(SCREAMING_SNAKE_CASE    )
											else:
																	raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

											__snake_case				=		1.0 - self.betas
											__snake_case				=		torch.cumprod(self.alphas  ,     dim=0    )

											#  set all values
											self.set_timesteps(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		int=None    )      ->						Optional[int]:




											'''simple docstring'''

											if schedule_timesteps is None:
																	__snake_case				=		self.timesteps

											__snake_case				=		(schedule_timesteps == timestep).nonzero()

											# The sigma index that is taken for the **very** first `step`
											# is always the second index (or the last index if there is only 1)
											# This way we can ensure we don't accidentally skip a sigma in
											# case we start in the middle of the denoising schedule (e.g. for image-to-image)
											if len(self._index_counter    ) == 0:
																	__snake_case				=		1 if len(SCREAMING_SNAKE_CASE    ) > 1 else 0
											else:
																	__snake_case				=		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE    ) else timestep
																	__snake_case				=		self._index_counter[timestep_int]

											return indices[pos].item()


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						int:




											'''simple docstring'''

											if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		Union[float, torch.FloatTensor]  ,     )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		self.index_for_timestep(SCREAMING_SNAKE_CASE    )

											if self.state_in_first_order:
																	__snake_case				=		self.sigmas[step_index]
											else:
																	__snake_case				=		self.sigmas_interpol[step_index]

											__snake_case				=		sample / ((sigma**2 + 1) ** 0.5)
											return sample


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, torch.device] = None  ,     SCREAMING_SNAKE_CASE						:		Optional[int] = None  ,     )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		num_inference_steps

											__snake_case				=		num_train_timesteps or self.config.num_train_timesteps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																	__snake_case				=		np.linspace(0  ,     num_train_timesteps - 1  ,     SCREAMING_SNAKE_CASE  ,     dtype=SCREAMING_SNAKE_CASE    )[::-1].copy()
											elif self.config.timestep_spacing == "leading":
																	__snake_case				=		num_train_timesteps // self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	__snake_case				=		(np.arange(0  ,     SCREAMING_SNAKE_CASE    ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE    )
																	timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																	__snake_case				=		num_train_timesteps / self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	__snake_case				=		(np.arange(SCREAMING_SNAKE_CASE  ,     0  ,     -step_ratio    )).round().copy().astype(SCREAMING_SNAKE_CASE    )
																	timesteps -= 1
											else:
																	raise ValueError(
																	    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

											__snake_case				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											__snake_case				=		torch.from_numpy(np.log(SCREAMING_SNAKE_CASE    )    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		np.interp(SCREAMING_SNAKE_CASE  ,     np.arange(0  ,     len(SCREAMING_SNAKE_CASE    )    )  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
											__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(device=SCREAMING_SNAKE_CASE    )

											# interpolate sigmas
											__snake_case				=		sigmas.log().lerp(sigmas.roll(1    ).log()  ,     0.5    ).exp()

											__snake_case				=		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
											__snake_case				=		torch.cat(
											    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

											if str(SCREAMING_SNAKE_CASE    ).startswith("mps"    ):
																	# mps does not support float64
																	__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											else:
																	__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(SCREAMING_SNAKE_CASE    )

											# interpolate timesteps
											__snake_case				=		self.sigma_to_t(SCREAMING_SNAKE_CASE    ).to(SCREAMING_SNAKE_CASE  ,     dtype=timesteps.dtype    )
											__snake_case				=		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,     dim=-1    ).flatten()

											__snake_case				=		torch.cat([timesteps[:1], interleaved_timesteps]    )

											__snake_case				=		None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											__snake_case				=		defaultdict(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		sigma.log()

											# get distribution
											__snake_case				=		log_sigma - self.log_sigmas[:, None]

											# get sigmas range
											__snake_case				=		dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
											__snake_case				=		low_idx + 1

											__snake_case				=		self.log_sigmas[low_idx]
											__snake_case				=		self.log_sigmas[high_idx]

											# interpolate sigmas
											__snake_case				=		(low - log_sigma) / (low - high)
											__snake_case				=		w.clamp(0  ,     1    )

											# transform interpolation to time range
											__snake_case				=		(1 - w) * low_idx + w * high_idx
											__snake_case				=		t.view(sigma.shape    )
											return t


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											return self.sample is None


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, np.ndarray]  ,     SCREAMING_SNAKE_CASE						:		Union[float, torch.FloatTensor]  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, np.ndarray]  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     )      ->						Union[SchedulerOutput, Tuple]:




											'''simple docstring'''

											__snake_case				=		self.index_for_timestep(SCREAMING_SNAKE_CASE    )

											# advance index counter by 1
											__snake_case				=		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE    ) else timestep
											self._index_counter[timestep_int] += 1

											if self.state_in_first_order:
																	__snake_case				=		self.sigmas[step_index]
																	__snake_case				=		self.sigmas_interpol[step_index + 1]
																	__snake_case				=		self.sigmas[step_index + 1]
											else:
																	# 2nd order / KDPM2's method
																	__snake_case				=		self.sigmas[step_index - 1]
																	__snake_case				=		self.sigmas_interpol[step_index]
																	__snake_case				=		self.sigmas[step_index]

											# currently only gamma=0 is supported. This usually works best anyways.
											# We can support gamma in the future but then need to scale the timestep before
											# passing it to the model which requires a change in API
											__snake_case				=		0
											__snake_case				=		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																	__snake_case				=		sigma_hat if self.state_in_first_order else sigma_interpol
																	__snake_case				=		sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
																	__snake_case				=		sigma_hat if self.state_in_first_order else sigma_interpol
																	__snake_case				=		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
											elif self.config.prediction_type == "sample":
																	raise NotImplementedError("prediction_type not implemented yet: sample"    )
											else:
																	raise ValueError(
																	    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

											if self.state_in_first_order:
																	# 2. Convert to an ODE derivative for 1st order
																	__snake_case				=		(sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	__snake_case				=		sigma_interpol - sigma_hat

																	# store for 2nd order step
																	__snake_case				=		sample
											else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	__snake_case				=		(sample - pred_original_sample) / sigma_interpol

																	# 3. delta timestep
																	__snake_case				=		sigma_next - sigma_hat

																	__snake_case				=		self.sample
																	__snake_case				=		None

											__snake_case				=		sample + derivative * dt

											if not return_dict:
																	return (prev_sample,)

											return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		self.sigmas.to(device=original_samples.device  ,     dtype=original_samples.dtype    )
											if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE    ):
																	# mps does not support float64
																	__snake_case				=		self.timesteps.to(original_samples.device  ,     dtype=torch.floataa    )
																	__snake_case				=		timesteps.to(original_samples.device  ,     dtype=torch.floataa    )
											else:
																	__snake_case				=		self.timesteps.to(original_samples.device    )
																	__snake_case				=		timesteps.to(original_samples.device    )

											__snake_case				=		[self.index_for_timestep(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ) for t in timesteps]

											__snake_case				=		sigmas[step_indices].flatten()
											while len(sigma.shape    ) < len(original_samples.shape    ):
																	__snake_case				=		sigma.unsqueeze(-1    )

											__snake_case				=		original_samples + noise * sigma
											return noisy_samples






					def __len__(    self						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											return self.config.num_train_timesteps





