



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_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			torch.FloatTensor
		SCREAMING_SNAKE_CASE_       :			Optional[torch.FloatTensor]					       =	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase=0.999      ,					_UpperCamelCase="cosine"      ,					):
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				_UpperCAmelCase						:  int     					=	[]
				for i in range(_UpperCamelCase			):
								_UpperCAmelCase						:  List[Any]     					=	i / num_diffusion_timesteps
								_UpperCAmelCase						:  Optional[int]     					=	(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_UpperCamelCase			) / alpha_bar_fn(_UpperCamelCase			)      ,					_UpperCamelCase			)			)
				return torch.tensor(_UpperCamelCase      ,					dtype=torch.floataa			)






class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			List[Any]					       =	[e.name for e in KarrasDiffusionSchedulers]
		SCREAMING_SNAKE_CASE_       :			int					       =	1







		@register_to_config
		def __init__(    self  :   List[str]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[Union[np.ndarray, List[float]]] = None       ,		UpperCAmelCase_  :   Optional[bool] = False       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   str = "linspace"       ,		UpperCAmelCase_  :   int = 0       ,		)       ->		str:


						'''simple docstring'''







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

						_UpperCAmelCase						:  List[str]     					=	1.0 - self.betas
						_UpperCAmelCase						:  Optional[Any]     					=	torch.cumprod(self.alphas       ,		dim=0					)

						_UpperCAmelCase						:  Any     					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
						_UpperCAmelCase						:  Optional[Any]     					=	np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
						_UpperCAmelCase						:  int     					=	torch.from_numpy(UpperCAmelCase_					)

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







		@property
		def 					a_  (    self  :   int					)       ->		Dict:


						'''simple docstring'''







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

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







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   Union[float, torch.FloatTensor]					)       ->		torch.FloatTensor:


						'''simple docstring'''







						if isinstance(UpperCAmelCase_       ,		torch.Tensor					):
										_UpperCAmelCase						:  Union[str, Any]     					=	timestep.to(self.timesteps.device					)
						_UpperCAmelCase						:  Union[str, Any]     					=	(self.timesteps == timestep).nonzero().item()
						_UpperCAmelCase						:  Optional[Any]     					=	self.sigmas[step_index]
						_UpperCAmelCase						:  Optional[int]     					=	sample / ((sigma**2 + 1) ** 0.5)
						_UpperCAmelCase						:  Any     					=	True
						return sample







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Union[str, Any]       ,		UpperCAmelCase_  :   Union[str, Any]       ,		UpperCAmelCase_  :   int					)       ->		Tuple:


						'''simple docstring'''







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

						_UpperCAmelCase						:  Union[str, Any]     					=	integrate.quad(UpperCAmelCase_       ,		self.sigmas[t]       ,		self.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   int       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Union[str, torch.device] = None					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  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":
										_UpperCAmelCase						:  int     					=	np.linspace(0       ,		self.config.num_train_timesteps - 1       ,		UpperCAmelCase_       ,		dtype=UpperCAmelCase_					)[
										    ::-1
										].copy()
						elif self.config.timestep_spacing == "leading":
										_UpperCAmelCase						:  List[Any]     					=	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
										_UpperCAmelCase						:  List[str]     					=	(np.arange(0       ,		UpperCAmelCase_					) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_					)
										timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
										_UpperCAmelCase						:  List[str]     					=	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
										_UpperCAmelCase						:  int     					=	(np.arange(self.config.num_train_timesteps       ,		0       ,		-step_ratio					)).round().copy().astype(UpperCAmelCase_					)
										timesteps -= 1
						else:
										raise ValueError(
										    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

						_UpperCAmelCase						:  List[str]     					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
						_UpperCAmelCase						:  List[Any]     					=	np.log(UpperCAmelCase_					)
						_UpperCAmelCase						:  List[Any]     					=	np.interp(UpperCAmelCase_       ,		np.arange(0       ,		len(UpperCAmelCase_					)					)       ,		UpperCAmelCase_					)

						if self.use_karras_sigmas:
										_UpperCAmelCase						:  Optional[int]     					=	self._convert_to_karras(in_sigmas=UpperCAmelCase_					)
										_UpperCAmelCase						:  List[Any]     					=	np.array([self._sigma_to_t(UpperCAmelCase_       ,		UpperCAmelCase_					) for sigma in sigmas]					)

						_UpperCAmelCase						:  Dict     					=	np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)

						_UpperCAmelCase						:  str     					=	torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_					)
						if str(UpperCAmelCase_					).startswith('''mps'''					):
										# mps does not support float64
										_UpperCAmelCase						:  Any     					=	torch.from_numpy(UpperCAmelCase_					).to(UpperCAmelCase_       ,		dtype=torch.floataa					)
						else:
										_UpperCAmelCase						:  str     					=	torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_					)

						_UpperCAmelCase						:  List[Any]     					=	[]







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   Optional[Any]       ,		UpperCAmelCase_  :   List[Any]					)       ->		List[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	np.log(UpperCAmelCase_					)

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

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

						_UpperCAmelCase						:  Optional[Any]     					=	log_sigmas[low_idx]
						_UpperCAmelCase						:  Optional[int]     					=	log_sigmas[high_idx]

						# interpolate sigmas
						_UpperCAmelCase						:  Optional[int]     					=	(low - log_sigma) / (low - high)
						_UpperCAmelCase						:  Optional[int]     					=	np.clip(UpperCAmelCase_       ,		0       ,		1					)

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







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   torch.FloatTensor					)       ->		torch.FloatTensor:


						'''simple docstring'''







						_UpperCAmelCase						:  float     					=	in_sigmas[-1].item()
						_UpperCAmelCase						:  float     					=	in_sigmas[0].item()

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







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   Union[float, torch.FloatTensor]       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[LMSDiscreteSchedulerOutput, Tuple]:


						'''simple docstring'''







						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(UpperCAmelCase_       ,		torch.Tensor					):
										_UpperCAmelCase						:  Union[str, Any]     					=	timestep.to(self.timesteps.device					)
						_UpperCAmelCase						:  str     					=	(self.timesteps == timestep).nonzero().item()
						_UpperCAmelCase						:  Dict     					=	self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Union[str, Any]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Dict     					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
										_UpperCAmelCase						:  str     					=	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
						_UpperCAmelCase						:  Optional[Any]     					=	(sample - pred_original_sample) / sigma
						self.derivatives.append(UpperCAmelCase_					)
						if len(self.derivatives					) > order:
										self.derivatives.pop(0					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  Optional[Any]     					=	min(step_index + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  List[Any]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

						# 4. Compute previous sample based on the derivatives path
						_UpperCAmelCase						:  Dict     					=	sample + sum(
						    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_       ,		reversed(self.derivatives					)					)					)

						if not return_dict:
										return (prev_sample,)

						return LMSDiscreteSchedulerOutput(prev_sample=UpperCAmelCase_       ,		pred_original_sample=UpperCAmelCase_					)







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		)       ->		torch.FloatTensor:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	self.sigmas.to(device=original_samples.device       ,		dtype=original_samples.dtype					)
						if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_					):
										# mps does not support float64
										_UpperCAmelCase						:  List[Any]     					=	self.timesteps.to(original_samples.device       ,		dtype=torch.floataa					)
										_UpperCAmelCase						:  List[Any]     					=	timesteps.to(original_samples.device       ,		dtype=torch.floataa					)
						else:
										_UpperCAmelCase						:  int     					=	self.timesteps.to(original_samples.device					)
										_UpperCAmelCase						:  Tuple     					=	timesteps.to(original_samples.device					)

						_UpperCAmelCase						:  Optional[int]     					=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

						_UpperCAmelCase						:  Dict     					=	original_samples + noise * sigma
						return noisy_samples







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


						'''simple docstring'''







						return self.config.num_train_timesteps





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


UpperCAmelCase__			:   str      			=    importlib.metadata.version('numpy')
UpperCAmelCase__			:   Optional[int]      			=    '.'.join([str(x) for x in sys.version_info[:3]])





class 							lowerCAmelCase_		(		lowercase_     ):







		def 					a_  (    self  :   Tuple					)       ->		Tuple:


						'''simple docstring'''







						require_version_core('''numpy<1000.4.5'''					)
						require_version_core('''numpy<1000.4'''					)
						require_version_core('''numpy<1000'''					)

						# le
						require_version_core('''numpy<=1000.4.5'''					)
						require_version_core(F'''numpy<={numpy_ver}'''					)

						# eq
						require_version_core(F'''numpy=={numpy_ver}'''					)

						# ne
						require_version_core('''numpy!=1000.4.5'''					)

						# ge
						require_version_core('''numpy>=1.0'''					)
						require_version_core('''numpy>=1.0.0'''					)
						require_version_core(F'''numpy>={numpy_ver}'''					)

						# gt
						require_version_core('''numpy>1.0.0'''					)

						# mix
						require_version_core('''numpy>1.0.0,<1000'''					)

						# requirement w/o version
						require_version_core('''numpy'''					)

						# unmet requirements due to version conflict
						for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''but found'''       ,		str(UpperCAmelCase_					)					)

        # unmet requirements due to missing module
						for req in ["numpipypie>1", "numpipypie2"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except importlib.metadata.PackageNotFoundError as e:
														self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''Try: pip install transformers -U'''       ,		str(UpperCAmelCase_					)					)

        # bogus requirements formats:
        # 1. whole thing
						for req in ["numpy??1.0.0", "numpy1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''requirement needs to be in the pip package format'''       ,		str(UpperCAmelCase_					)					)
        # 2. only operators
						for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''need one of '''       ,		str(UpperCAmelCase_					)					)







		def 					a_  (    self  :   Tuple					)       ->		List[Any]:


						'''simple docstring'''







						require_version('''python>=3.6.0'''					)

						# not matching requirements
						for req in ["python>9.9.9", "python<3.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn(F'''but found python=={python_ver}'''       ,		str(UpperCAmelCase_					)					)
