


'''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 _snake_case (			a__ ):
							"""simple docstring"""





							__a				       =  42
							__a				       =  None





def 				a_							(    lowercase				,    lowercase=0.9_9_9				,    lowercase="cosine"				,    )      ->Union[str, Any]:






				'''simple docstring'''

				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}'     )

				__lowerCamelCase		:   List[Any]           =       []
				for i in range(lowercase     ):
								__lowerCamelCase		:   str           =       i / num_diffusion_timesteps
								__lowerCamelCase		:   Any           =       (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 _snake_case (			a__     , a__ ):
							"""simple docstring"""





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







							@register_to_config
							def __init__( self      ,					__A = 1000      ,					__A = 0.0001      ,					__A = 0.02      ,					__A = "linear"      ,					__A = None      ,					__A = False      ,					__A = "epsilon"      ,					__A = "linspace"      ,					__A = 0      ,					)  ->List[Any]:



											"""simple docstring"""







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

											__lowerCamelCase		:   List[Any]           =       1.0 - self.betas
											__lowerCamelCase		:   Tuple           =       torch.cumprod(self.alphas      ,					dim=0						)

											__lowerCamelCase		:   int           =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											__lowerCamelCase		:   int           =       np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
											__lowerCamelCase		:   str           =       torch.from_numpy(__A						)

											# setable values
											__lowerCamelCase		:   Optional[Any]           =       None
											__lowerCamelCase		:   Union[str, Any]           =       use_karras_sigmas
											self.set_timesteps(__A      ,					__A						)
											__lowerCamelCase		:   Optional[Any]           =       []
											__lowerCamelCase		:   List[Any]           =       False







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[str]:



											"""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      ,					__A      ,					__A						)  ->torch.FloatTensor:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											def lms_derivative(__A						):
															__lowerCamelCase		:   int           =       1.0
															for k in range(__A						):
																			if current_order == k:
																							continue
																			prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
															return prod

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

											return integrated_coeff







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A = None						)  ->Optional[int]:



											"""simple docstring"""







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

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

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

											__lowerCamelCase		:   str           =       np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

											__lowerCamelCase		:   int           =       torch.from_numpy(__A						).to(device=__A						)
											if str(__A						).startswith("mps"						):
															# mps does not support float64
															__lowerCamelCase		:   Any           =       torch.from_numpy(__A						).to(__A      ,					dtype=torch.floataa						)
											else:
															__lowerCamelCase		:   List[str]           =       torch.from_numpy(__A						).to(device=__A						)

											__lowerCamelCase		:   Tuple           =       []







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   str           =       np.log(__A						)

											# get distribution
											__lowerCamelCase		:   int           =       log_sigma - log_sigmas[:, np.newaxis]

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

											__lowerCamelCase		:   str           =       log_sigmas[low_idx]
											__lowerCamelCase		:   Dict           =       log_sigmas[high_idx]

											# interpolate sigmas
											__lowerCamelCase		:   Any           =       (low - log_sigma) / (low - high)
											__lowerCamelCase		:   List[str]           =       np.clip(__A      ,					0      ,					1						)

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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->torch.FloatTensor:



											"""simple docstring"""







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

											__lowerCamelCase		:   Tuple           =       7.0  # 7.0 is the value used in the paper
											__lowerCamelCase		:   Any           =       np.linspace(0      ,					1      ,					self.num_inference_steps						)
											__lowerCamelCase		:   Dict           =       sigma_min ** (1 / rho)
											__lowerCamelCase		:   Any           =       sigma_max ** (1 / rho)
											__lowerCamelCase		:   str           =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A = 4      ,					__A = 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(__A      ,					torch.Tensor						):
															__lowerCamelCase		:   int           =       timestep.to(self.timesteps.device						)
											__lowerCamelCase		:   Union[str, Any]           =       (self.timesteps == timestep).nonzero().item()
											__lowerCamelCase		:   Optional[Any]           =       self.sigmas[step_index]

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

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

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

											if not return_dict:
															return (prev_sample,)

											return LMSDiscreteSchedulerOutput(prev_sample=__A      ,					pred_original_sample=__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					)  ->torch.FloatTensor:



											"""simple docstring"""







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

											__lowerCamelCase		:   List[Any]           =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

											__lowerCamelCase		:   List[Any]           =       original_samples + noise * sigma
											return noisy_samples







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



											"""simple docstring"""







											return self.config.num_train_timesteps









'''simple docstring'''




from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       word
											__lowerCamelCase		:   Optional[Any]           =       count







							def __eq__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count == other.count







							def __lt__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count < other.count





def 				a_							(    lowercase				,    lowercase     )      ->list[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Heap[WordCount]           =       Heap()
				__lowerCamelCase		:   int           =       Counter(lowercase     )
				heap.build_max_heap(
				    [WordCount(lowercase				,    lowercase     ) for word, count in count_by_word.items()]     )
				return [heap.extract_max().word for _ in range(min(lowercase				,    len(lowercase     )     )     )]


if __name__ == "__main__":
				import doctest

				doctest.testmod()





