



"""simple docstring"""
import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      torch.FloatTensor
					_A		:      Optional[torch.FloatTensor]						=					None

def        A_  (      _lowercase, _lowercase=0.999, _lowercase="cosine", ):







						'''simple docstring'''


						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

						snake_case_			:int						=      []
						for i in range(_lowercase     ):
												snake_case_			:List[str]						=      i / num_diffusion_timesteps
												snake_case_			:Tuple						=      (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowercase     ) / alpha_bar_fn(_lowercase     ), _lowercase     )     )
						return torch.tensor(_lowercase, dtype=torch.floataa     )

class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					[e.name for e in KarrasDiffusionSchedulers]
					_A		:      str						=					1






					@register_to_config
					def __init__(	self:							Union[str, Any]						,					snake_case:							int = 1_000						,					snake_case:							float = 0.0_0_0_1						,					snake_case:							float = 0.0_2						,					snake_case:							str = "linear"						,					snake_case:							Optional[Union[np.ndarray, List[float]]] = None						,					snake_case:							Optional[bool] = False						,					snake_case:							str = "epsilon"						,					snake_case:							str = "linspace"						,					snake_case:							int = 0						,					)       ->     str:
											if trained_betas is not None:
																	snake_case_			:List[Any]						=      torch.tensor(snake_case						,					dtype=torch.floataa						)
											elif beta_schedule == "linear":
																	snake_case_			:str						=      torch.linspace(snake_case						,					snake_case						,					snake_case						,					dtype=torch.floataa						)
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	snake_case_			:int						=      (
																	    torch.linspace(beta_start**0.5						,					beta_end**0.5						,					snake_case						,					dtype=torch.floataa						) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	snake_case_			:Tuple						=      betas_for_alpha_bar(snake_case						)
											else:
																	raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

											snake_case_			:Dict						=      1.0 - self.betas
											snake_case_			:Dict						=      torch.cumprod(self.alphas						,					dim=0						)

											snake_case_			:Union[str, Any]						=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											snake_case_			:List[str]						=      np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
											snake_case_			:str						=      torch.from_numpy(snake_case						)

											# setable values
											snake_case_			:Any						=      None
											snake_case_			:str						=      use_karras_sigmas
											self.set_timesteps(snake_case						,					snake_case						)
											snake_case_			:int						=      []
											snake_case_			:List[Any]						=      False






					@property
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Tuple:
											# standard deviation of the initial noise distribution
											if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

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






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							torch.FloatTensor						,					snake_case:							Union[float, torch.FloatTensor]						)       ->     torch.FloatTensor:
											if isinstance(snake_case						,					torch.Tensor						):
																	snake_case_			:List[Any]						=      timestep.to(self.timesteps.device						)
											snake_case_			:List[str]						=      (self.timesteps == timestep).nonzero().item()
											snake_case_			:Tuple						=      self.sigmas[step_index]
											snake_case_			:Dict						=      sample / ((sigma**2 + 1) ** 0.5)
											snake_case_			:Optional[int]						=      True
											return sample






					def        lowerCAmelCase_    (	self:							Tuple						,					snake_case:							Dict						,					snake_case:							Optional[int]						,					snake_case:							Any						)       ->     Tuple:

											def lms_derivative(snake_case:							List[str]						):
																	snake_case_			:Union[str, Any]						=      1.0
																	for k in range(snake_case						):
																							if current_order == k:
																													continue
																							prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																	return prod

											snake_case_			:List[Any]						=      integrate.quad(snake_case						,					self.sigmas[t]						,					self.sigmas[t + 1]						,					epsrel=1E-4						)[0]

											return integrated_coeff






					def        lowerCAmelCase_    (	self:							int						,					snake_case:							int						,					snake_case:							Union[str, torch.device] = None						)       ->     Optional[Any]:
											snake_case_			:Optional[int]						=      num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																	snake_case_			:str						=      np.linspace(0						,					self.config.num_train_timesteps - 1						,					snake_case						,					dtype=snake_case						)[
																	    ::-1
																	].copy()
											elif self.config.timestep_spacing == "leading":
																	snake_case_			:Tuple						=      self.config.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_			:List[str]						=      (np.arange(0						,					snake_case						) * step_ratio).round()[::-1].copy().astype(snake_case						)
																	timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																	snake_case_			:Tuple						=      self.config.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_			:List[str]						=      (np.arange(self.config.num_train_timesteps						,					0						,					-step_ratio						)).round().copy().astype(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_			:str						=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											snake_case_			:str						=      np.log(snake_case						)
											snake_case_			:Optional[Any]						=      np.interp(snake_case						,					np.arange(0						,					len(snake_case						)						)						,					snake_case						)

											if self.use_karras_sigmas:
																	snake_case_			:Optional[Any]						=      self._convert_to_karras(in_sigmas=snake_case						)
																	snake_case_			:Optional[Any]						=      np.array([self._sigma_to_t(snake_case						,					snake_case						) for sigma in sigmas]						)

											snake_case_			:Any						=      np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

											snake_case_			:Any						=      torch.from_numpy(snake_case						).to(device=snake_case						)
											if str(snake_case						).startswith("""mps"""						):
																	# mps does not support float64
																	snake_case_			:str						=      torch.from_numpy(snake_case						).to(snake_case						,					dtype=torch.floataa						)
											else:
																	snake_case_			:int						=      torch.from_numpy(snake_case						).to(device=snake_case						)

											snake_case_			:Any						=      []






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							List[str]						,					snake_case:							Union[str, Any]						)       ->     Any:
											# get log sigma
											snake_case_			:Union[str, Any]						=      np.log(snake_case						)

											# get distribution
											snake_case_			:List[str]						=      log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											snake_case_			:Union[str, Any]						=      np.cumsum((dists >= 0)						,					axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
											snake_case_			:str						=      low_idx + 1

											snake_case_			:List[Any]						=      log_sigmas[low_idx]
											snake_case_			:Dict						=      log_sigmas[high_idx]

											# interpolate sigmas
											snake_case_			:Optional[Any]						=      (low - log_sigma) / (low - high)
											snake_case_			:Dict						=      np.clip(snake_case						,					0						,					1						)

											# transform interpolation to time range
											snake_case_			:Union[str, Any]						=      (1 - w) * low_idx + w * high_idx
											snake_case_			:Any						=      t.reshape(sigma.shape						)
											return t






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							torch.FloatTensor						)       ->     torch.FloatTensor:
											snake_case_			:float						=      in_sigmas[-1].item()
											snake_case_			:float						=      in_sigmas[0].item()

											snake_case_			:Optional[Any]						=      7.0  # 7.0 is the value used in the paper
											snake_case_			:Optional[int]						=      np.linspace(0						,					1						,					self.num_inference_steps						)
											snake_case_			:int						=      sigma_min ** (1 / rho)
											snake_case_			:List[str]						=      sigma_max ** (1 / rho)
											snake_case_			:int						=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas






					def        lowerCAmelCase_    (	self:							str						,					snake_case:							torch.FloatTensor						,					snake_case:							Union[float, torch.FloatTensor]						,					snake_case:							torch.FloatTensor						,					snake_case:							int = 4						,					snake_case:							bool = True						,					)       ->     Union[LMSDiscreteSchedulerOutput, Tuple]:
											if not self.is_scale_input_called:
																	warnings.warn(
																	    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
																	    """See `StableDiffusionPipeline` for a usage example."""						)

											if isinstance(snake_case						,					torch.Tensor						):
																	snake_case_			:int						=      timestep.to(self.timesteps.device						)
											snake_case_			:Union[str, Any]						=      (self.timesteps == timestep).nonzero().item()
											snake_case_			:Union[str, Any]						=      self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																	snake_case_			:Dict						=      sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																	# * c_out + input * c_skip
																	snake_case_			:Dict						=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
																	snake_case_			:Any						=      model_output
											else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

											# 2. Convert to an ODE derivative
											snake_case_			:str						=      (sample - pred_original_sample) / sigma
											self.derivatives.append(snake_case						)
											if len(self.derivatives						) > order:
																	self.derivatives.pop(0						)

											# 3. Compute linear multistep coefficients
											snake_case_			:List[Any]						=      min(step_index + 1						,					snake_case						)
											snake_case_			:Union[str, Any]						=      [self.get_lms_coefficient(snake_case						,					snake_case						,					snake_case						) for curr_order in range(snake_case						)]

											# 4. Compute previous sample based on the derivatives path
											snake_case_			:Optional[Any]						=      sample + sum(
											    coeff * derivative for coeff, derivative in zip(snake_case						,					reversed(self.derivatives						)						)						)

											if not return_dict:
																	return (prev_sample,)

											return LMSDiscreteSchedulerOutput(prev_sample=snake_case						,					pred_original_sample=snake_case						)






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							torch.FloatTensor						,					snake_case:							torch.FloatTensor						,					snake_case:							torch.FloatTensor						,					)       ->     torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											snake_case_			:Union[str, Any]						=      self.sigmas.to(device=original_samples.device						,					dtype=original_samples.dtype						)
											if original_samples.device.type == "mps" and torch.is_floating_point(snake_case						):
																	# mps does not support float64
																	snake_case_			:int						=      self.timesteps.to(original_samples.device						,					dtype=torch.floataa						)
																	snake_case_			:Union[str, Any]						=      timesteps.to(original_samples.device						,					dtype=torch.floataa						)
											else:
																	snake_case_			:Any						=      self.timesteps.to(original_samples.device						)
																	snake_case_			:Union[str, Any]						=      timesteps.to(original_samples.device						)

											snake_case_			:Tuple						=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

											snake_case_			:int						=      original_samples + noise * sigma
											return noisy_samples






					def __len__(	self:							List[str]						)       ->     Optional[int]:
											return self.config.num_train_timesteps





"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					@patch("""diffusers.utils.hub_utils.get_full_repo_name"""						)
					def        lowerCAmelCase_    (	self:							int						,					snake_case:							Mock						)       ->     None:
											snake_case_			:str						=      """full_repo_name"""
											with TemporaryDirectory() as tmpdir:
																	# Dummy args values
																	snake_case_			:int						=      Mock()
																	snake_case_			:Any						=      tmpdir
																	snake_case_			:int						=      0
																	snake_case_			:Optional[int]						=      """hub_token"""
																	snake_case_			:str						=      """dataset_name"""
																	snake_case_			:List[str]						=      0.0_1
																	snake_case_			:Dict						=      100_000
																	snake_case_			:Optional[Any]						=      10_000
																	snake_case_			:Optional[Any]						=      0.0_1
																	snake_case_			:Dict						=      0.0_2
																	snake_case_			:Dict						=      0.0_3
																	snake_case_			:Union[str, Any]						=      0.0_0_0_5
																	snake_case_			:Optional[Any]						=      0.0_0_0_0_0_1
																	snake_case_			:List[Any]						=      1
																	snake_case_			:Optional[Any]						=      10
																	snake_case_			:Dict						=      0.0_0_1
																	snake_case_			:Any						=      0.1
																	snake_case_			:List[Any]						=      0.2
																	snake_case_			:Any						=      True

																	# Model card mush be rendered and saved
																	diffusers.utils.hub_utils.create_model_card(snake_case						,					model_name="""model_name"""						)
																	self.assertTrue((Path(snake_case						) / """README.md""").is_file()						)
