



'''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__   (    UpperCAmelCase__  ):




					'''simple docstring'''



					A_         =    42
					A_         =    None







def     __UpperCamelCase					(				_lowercase,					_lowercase=0.9_9_9,					_lowercase="cosine",					)			->      str:


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_lowercase       ):
			return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_lowercase       ):
			return math.exp(t * -1_2.0       )

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

	_lowercase       :							List[Any]  	=		[]
	for i in range(_lowercase       ):
		_lowercase       :							int  	=		i / num_diffusion_timesteps
		_lowercase       :							Dict  	=		(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__   (    UpperCAmelCase__						, UpperCAmelCase__  ):




					'''simple docstring'''



					A_         =    [e.name for e in KarrasDiffusionSchedulers]
					A_         =    1




					@register_to_config
					def __init__(	self					:						Dict  ,	UpperCamelCase_					:						Optional[Any] = 1000  ,	UpperCamelCase_					:						Any = 0.00_01  ,	UpperCamelCase_					:						Any = 0.02  ,	UpperCamelCase_					:						List[Any] = "linear"  ,	UpperCamelCase_					:						Any = None  ,	UpperCamelCase_					:						Dict = False  ,	UpperCamelCase_					:						Optional[Any] = "epsilon"  ,	UpperCamelCase_					:						int = "linspace"  ,	UpperCamelCase_					:						Dict = 0  ,	)	->							Dict:



						'''simple docstring'''





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

						_lowercase       :							str  	=		1.0 - self.betas
						_lowercase       :							Tuple  	=		torch.cumprod(self.alphas  ,	dim=0       )

						_lowercase       :							Tuple  	=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
						_lowercase       :							List[Any]  	=		np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
						_lowercase       :							Optional[Any]  	=		torch.from_numpy(UpperCamelCase_       )

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




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



						'''simple docstring'''





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

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




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Any       )	->							torch.FloatTensor:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						str  ,	UpperCamelCase_					:						Union[str, Any]  ,	UpperCamelCase_					:						Any       )	->							Dict:



						'''simple docstring'''





						def lms_derivative(UpperCamelCase_					:						Dict       ):
							_lowercase       :							Optional[Any]  	=		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

						_lowercase       :							Optional[int]  	=		integrate.quad(UpperCamelCase_  ,	self.sigmas[t]  ,	self.sigmas[t + 1]  ,	epsrel=1E-4       )[0]

						return integrated_coeff




					def     __UpperCAmelCase  (	self					:						int  ,	UpperCamelCase_					:						Optional[Any]  ,	UpperCamelCase_					:						Dict = None       )	->							Any:



						'''simple docstring'''





						_lowercase       :							Dict  	=		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":
							_lowercase       :							Optional[Any]  	=		np.linspace(0  ,	self.config.num_train_timesteps - 1  ,	UpperCamelCase_  ,	dtype=UpperCamelCase_       )[
							    ::-1
							].copy()
						elif self.config.timestep_spacing == "leading":
							_lowercase       :							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
							_lowercase       :							int  	=		(np.arange(0  ,	UpperCamelCase_       ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_       )
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							_lowercase       :							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
							_lowercase       :							Any  	=		(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\'.'''       )

						_lowercase       :							Tuple  	=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
						_lowercase       :							Optional[Any]  	=		np.log(UpperCamelCase_       )
						_lowercase       :							Union[str, Any]  	=		np.interp(UpperCamelCase_  ,	np.arange(0  ,	len(UpperCamelCase_       )       )  ,	UpperCamelCase_       )

						if self.use_karras_sigmas:
							_lowercase       :							Tuple  	=		self._convert_to_karras(in_sigmas=UpperCamelCase_       )
							_lowercase       :							Dict  	=		np.array([self._sigma_to_t(UpperCamelCase_  ,	UpperCamelCase_       ) for sigma in sigmas]       )

						_lowercase       :							Optional[int]  	=		np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

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

						_lowercase       :							Optional[int]  	=		[]




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Union[str, Any]  ,	UpperCamelCase_					:						Any       )	->							Any:



						'''simple docstring'''





						_lowercase       :							int  	=		np.log(UpperCamelCase_       )

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

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

						_lowercase       :							Any  	=		log_sigmas[low_idx]
						_lowercase       :							Dict  	=		log_sigmas[high_idx]

						# interpolate sigmas
						_lowercase       :							Optional[Any]  	=		(low - log_sigma) / (low - high)
						_lowercase       :							Tuple  	=		np.clip(UpperCamelCase_  ,	0  ,	1       )

						# transform interpolation to time range
						_lowercase       :							Dict  	=		(1 - w) * low_idx + w * high_idx
						_lowercase       :							Any  	=		t.reshape(sigma.shape       )
						return t




					def     __UpperCAmelCase  (	self					:						List[str]  ,	UpperCamelCase_					:						str       )	->							torch.FloatTensor:



						'''simple docstring'''





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

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




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						int = 4  ,	UpperCamelCase_					:						str = 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       ):
							_lowercase       :							Dict  	=		timestep.to(self.timesteps.device       )
						_lowercase       :							Dict  	=		(self.timesteps == timestep).nonzero().item()
						_lowercase       :							Tuple  	=		self.sigmas[step_index]

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

						# 3. Compute linear multistep coefficients
						_lowercase       :							Tuple  	=		min(step_index + 1  ,	UpperCamelCase_       )
						_lowercase       :							int  	=		[self.get_lms_coefficient(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_       ) for curr_order in range(UpperCamelCase_       )]

						# 4. Compute previous sample based on the derivatives path
						_lowercase       :							Tuple  	=		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     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Tuple  ,	UpperCamelCase_					:						Tuple  ,	UpperCamelCase_					:						List[Any]  ,	)	->							torch.FloatTensor:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		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
							_lowercase       :							Any  	=		self.timesteps.to(original_samples.device  ,	dtype=torch.floataa       )
							_lowercase       :							Any  	=		timesteps.to(original_samples.device  ,	dtype=torch.floataa       )
						else:
							_lowercase       :							str  	=		self.timesteps.to(original_samples.device       )
							_lowercase       :							Union[str, Any]  	=		timesteps.to(original_samples.device       )

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

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

						_lowercase       :							Dict  	=		original_samples + noise * sigma
						return noisy_samples




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



						'''simple docstring'''





						return self.config.num_train_timesteps


import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    np.arange(start=3     ,						stop=SCREAMING_SNAKE_CASE     ,						step=4							).tolist() if k > 0 else [10]
			UpperCAmelCase__		:List[Any] 		=    _quantize(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			# count number of elements per group
			UpperCAmelCase__		:Dict 		=    np.unique(SCREAMING_SNAKE_CASE     ,						return_counts=SCREAMING_SNAKE_CASE							)[1]
			UpperCAmelCase__		:Any 		=    [0] + bins + [np.inf]
			logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE							)							)
			logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE							)							)
			return groups




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A  , A	)  ->Union[str, Any]:
									if not isinstance(A  , A	):
												raise ValueError(
												    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(A	)	)
									UpperCAmelCase__		:Tuple 		=    sampler
									UpperCAmelCase__		:Tuple 		=    group_ids
									UpperCAmelCase__		:str 		=    batch_size


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

									UpperCAmelCase__		:Any 		=    0
									for idx in self.sampler:
												UpperCAmelCase__		:List[Any] 		=    self.group_ids[idx]
												buffer_per_group[group_id].append(A	)
												samples_per_group[group_id].append(A	)
												if len(buffer_per_group[group_id]	) == self.batch_size:
															yield buffer_per_group[group_id]  # TODO
															num_batches += 1
															del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]	) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									UpperCAmelCase__		:Union[str, Any] 		=    len(self	)
									UpperCAmelCase__		:Tuple 		=    expected_num_batches - num_batches
									if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												UpperCAmelCase__		:str 		=    []
												for group_id, idxs in sorted(buffer_per_group.items()  , key=lambda A	: x[0]	):
															batch_idx.extend(A	)
															if len(A	) >= self.batch_size:
																		yield batch_idx[: self.batch_size]
																		UpperCAmelCase__		:int 		=    batch_idx[self.batch_size :]
																		num_remaining -= 1
												if len(A	) > 0:
															yield batch_idx
															num_remaining -= 1
									assert num_remaining == 0


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





