
"""simple docstring"""






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: torch.FloatTensor
							_lowerCamelCase				: torch.FloatTensor





def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__=0.999 ,      __magic_name__="cosine" ,      )	->Union[str, Any]:
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__magic_name__			):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

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

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

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







class 				__a    (   __a				,      __a					):



							'''simple docstring'''







							_lowerCamelCase				: int					      =      1
							@register_to_config
							def __init__(    self     ,						_lowerCamelCase = 1_000     ,						_lowerCamelCase = 0.0001     ,						_lowerCamelCase = 0.02     ,						_lowerCamelCase = "linear"     ,						_lowerCamelCase = 0.0     ,						_lowerCamelCase = None     ,						_lowerCamelCase = True     ,						) ->       Optional[Any]:




										'''simple docstring'''
										if trained_betas is not None:
													__lowercase						      =					torch.from_numpy(_lowerCamelCase					)
										elif beta_schedule == "linear":
													__lowercase						      =					torch.linspace(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowercase						      =					(
													    torch.linspace(beta_start**0.5     ,						beta_end**0.5     ,						_lowerCamelCase     ,						dtype=torch.floataa					) ** 2
													)
										elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowercase						      =					betas_for_alpha_bar(_lowerCamelCase					)
										elif beta_schedule == "sigmoid":
													# GeoDiff sigmoid schedule
													__lowercase						      =					torch.linspace(-6     ,						6     ,						_lowerCamelCase					)
													__lowercase						      =					torch.sigmoid(_lowerCamelCase					) * (beta_end - beta_start) + beta_start
										else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

										__lowercase						      =					torch.tensor(1.0					)

										# standard deviation of the initial noise distribution
										__lowercase						      =					1.0

										# setable values
										__lowercase						      =					None
										__lowercase						      =					torch.from_numpy(np.arange(0     ,						_lowerCamelCase					)[::-1].copy()					)

										__lowercase						      =					eta
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase = None					) ->       torch.FloatTensor:




										'''simple docstring'''
										return sample
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase = 10     ,						_lowerCamelCase = 10     ,						_lowerCamelCase = None     ,						) ->       Any:




										'''simple docstring'''
										__lowercase						      =					min(self.config.num_train_timesteps     ,						_lowerCamelCase					)
										__lowercase						      =					num_inference_steps

										__lowercase						      =					[]

										__lowercase						      =					{}
										for j in range(0     ,						num_inference_steps - jump_length     ,						_lowerCamelCase					):
													__lowercase						      =					jump_n_sample - 1

										__lowercase						      =					num_inference_steps
										while t >= 1:
													__lowercase						      =					t - 1
													timesteps.append(_lowerCamelCase					)

													if jumps.get(_lowerCamelCase     ,						0					) > 0:
																__lowercase						      =					jumps[t] - 1
																for _ in range(_lowerCamelCase					):
																			__lowercase						      =					t + 1
																			timesteps.append(_lowerCamelCase					)

										__lowercase						      =					np.array(_lowerCamelCase					) * (self.config.num_train_timesteps // self.num_inference_steps)
										__lowercase						      =					torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					t - self.config.num_train_timesteps // self.num_inference_steps

										__lowercase						      =					self.alphas_cumprod[t]
										__lowercase						      =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										__lowercase						      =					1 - alpha_prod_t
										__lowercase						      =					1 - alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from
										# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
										# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
										# variance to pred_sample
										# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
										# without eta.
										# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
										__lowercase						      =					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						_lowerCamelCase = True     ,						) ->       Union[RePaintSchedulerOutput, Tuple]:




										'''simple docstring'''
										__lowercase						      =					timestep
										__lowercase						      =					timestep - self.config.num_train_timesteps // self.num_inference_steps

										# 1. compute alphas, betas
										__lowercase						      =					self.alphas_cumprod[t]
										__lowercase						      =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										__lowercase						      =					1 - alpha_prod_t

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										__lowercase						      =					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
													__lowercase						      =					torch.clamp(_lowerCamelCase     ,						-1     ,						1					)

										# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
										# substitute formula (7) in the algorithm coming from DDPM paper
										# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
										# DDIM schedule gives the same results as DDPM with eta = 1.0
										# Noise is being reused in 7. and 8., but no impact on quality has
										# been observed.

										# 5. Add noise
										__lowercase						      =					model_output.device
										__lowercase						      =					randn_tensor(model_output.shape     ,						generator=_lowerCamelCase     ,						device=_lowerCamelCase     ,						dtype=model_output.dtype					)
										__lowercase						      =					self.eta * self._get_variance(_lowerCamelCase					) ** 0.5

										__lowercase						      =					0
										if t > 0 and self.eta > 0:
													__lowercase						      =					std_dev_t * noise

										# 6. compute "direction pointing to x_t" of formula (12)
										# from https://arxiv.org/pdf/2010.02502.pdf
										__lowercase						      =					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

										# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										__lowercase						      =					alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

										# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
										__lowercase						      =					(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

										# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
										__lowercase						      =					mask * prev_known_part + (1.0 - mask) * prev_unknown_part

										if not return_dict:
													return (
													    pred_prev_sample,
													    pred_original_sample,
													)

										return RePaintSchedulerOutput(prev_sample=_lowerCamelCase     ,						pred_original_sample=_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=None					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.config.num_train_timesteps // self.num_inference_steps

										for i in range(_lowerCamelCase					):
													__lowercase						      =					self.betas[timestep + i]
													if sample.device.type == "mps":
																# randn does not work reproducibly on mps
																__lowercase						      =					randn_tensor(sample.shape     ,						dtype=sample.dtype     ,						generator=_lowerCamelCase					)
																__lowercase						      =					noise.to(sample.device					)
													else:
																__lowercase						      =					randn_tensor(sample.shape     ,						generator=_lowerCamelCase     ,						device=sample.device     ,						dtype=sample.dtype					)

													# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
													__lowercase						      =					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

										return sample
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       torch.FloatTensor:




										'''simple docstring'''
										raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)





							def __len__(    self					) ->       List[str]:




										'''simple docstring'''
										return self.config.num_train_timesteps







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





