

def  UpperCamelCase_(							_snake_case     :						str			):




			"""simple docstring"""






			__a						   =word.upper()
			__a						   =0
			__a						   ={}
			__a						   =[]

			for letter in word:
						if letter not in letter_nums:
									__a						   =str(_snake_case			)
									next_num += 1
						word_pattern.append(letter_nums[letter]			)
			return ".".join(_snake_case			)


if __name__ == "__main__":
						import pprint
						import time

						_lowerCAmelCase      :    int						       =    time.time()
						with open("dictionary.txt") as in_file:
												_lowerCAmelCase      :    Optional[Any]						       =    in_file.read().splitlines()

						_lowerCAmelCase      :    dict						       =    {}
						for word in word_list:
												_lowerCAmelCase      :    Optional[int]						       =    get_word_pattern(word)
												if pattern in all_patterns:
																		all_patterns[pattern].append(word)
												else:
																		_lowerCAmelCase      :    Optional[Any]						       =    [word]

						with open("word_patterns.txt", "w") as out_file:
												out_file.write(pprint.pformat(all_patterns))

						_lowerCAmelCase      :    Union[str, Any]						       =    round(time.time() - start_time, 2)
						print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
						# Done!  9,581 word patterns found in 0.58 seconds.









import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def  UpperCamelCase_(							_snake_case     :						List[Any] ,  _snake_case     :						Optional[int]=0.999 ,  _snake_case     :						List[Any]="cosine" ,  ):




			"""simple docstring"""






			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_snake_case     :						Union[str, Any]			):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

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

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

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






class 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		[e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE							     =		2





			@register_to_config
			def __init__(	self      ,						__snake_case = 1000      ,						__snake_case = 0.0_0085      ,						__snake_case = 0.012      ,						__snake_case = "linear"      ,						__snake_case = None      ,						__snake_case = "epsilon"      ,						__snake_case = "linspace"      ,						__snake_case = 0      ,						)     ->				Union[str, Any]:




						'''simple docstring'''
						if trained_betas is not None:
									__a						   =torch.tensor(__snake_case      ,						dtype=torch.floataa      )
						elif beta_schedule == "linear":
									__a						   =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.
									__a						   =(
									    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
									__a						   =betas_for_alpha_bar(__snake_case      )
						else:
									raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'      )

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

						#  set all values
						self.set_timesteps(__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case=None      )     ->				Any:




						'''simple docstring'''
						if schedule_timesteps is None:
									__a						   =self.timesteps

						__a						   =(schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter      ) == 0:
									__a						   =1 if len(__snake_case      ) > 1 else 0
						else:
									__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
									__a						   =self._index_counter[timestep_int]

						return indices[pos].item()





			@property
			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						# 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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
						else:
									__a						   =self.sigmas_interpol[step_index - 1]

						__a						   =sample / ((sigma**2 + 1) ** 0.5)
						return sample





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = None      ,						)     ->				List[str]:




						'''simple docstring'''
						__a						   =num_inference_steps

						__a						   =num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
									__a						   =np.linspace(0      ,						num_train_timesteps - 1      ,						__snake_case      ,						dtype=__snake_case      )[::-1].copy()
						elif self.config.timestep_spacing == "leading":
									__a						   =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
									__a						   =(np.arange(0      ,						__snake_case      ) * step_ratio).round()[::-1].copy().astype(__snake_case      )
									timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
									__a						   =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
									__a						   =(np.arange(__snake_case      ,						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\'.'      )

						__a						   =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						__a						   =torch.from_numpy(np.log(__snake_case      )      ).to(__snake_case      )

						__a						   =np.interp(__snake_case      ,						np.arange(0      ,						len(__snake_case      )      )      ,						__snake_case      )
						__a						   =np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
						__a						   =torch.from_numpy(__snake_case      ).to(device=__snake_case      )

						# compute up and down sigmas
						__a						   =sigmas.roll(-1      )
						__a						   =0.0
						__a						   =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
						__a						   =(sigmas_next**2 - sigmas_up**2) ** 0.5
						__a						   =0.0

						# compute interpolated sigmas
						__a						   =sigmas.log().lerp(sigmas_down.log()      ,						0.5      ).exp()
						__a						   =0.0

						# set sigmas
						__a						   =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
						__a						   =torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
						__a						   =torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
						__a						   =torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

						if str(__snake_case      ).startswith('mps'      ):
									# mps does not support float64
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      ,						dtype=torch.floataa      )
						else:
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      )

						__a						   =self.sigma_to_t(__snake_case      ).to(__snake_case      ,						dtype=timesteps.dtype      )
						__a						   =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,						dim=-1      ).flatten()

						__a						   =torch.cat([timesteps[:1], interleaved_timesteps]      )

						__a						   =None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						__a						   =defaultdict(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						# get log sigma
						__a						   =sigma.log()

						# get distribution
						__a						   =log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						__a						   =dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
						__a						   =low_idx + 1

						__a						   =self.log_sigmas[low_idx]
						__a						   =self.log_sigmas[high_idx]

						# interpolate sigmas
						__a						   =(low - log_sigma) / (low - high)
						__a						   =w.clamp(0      ,						1      )

						# transform interpolation to time range
						__a						   =(1 - w) * low_idx + w * high_idx
						__a						   =t.view(sigma.shape      )
						return t





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




						'''simple docstring'''
						return self.sample is None





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = True      ,						)     ->				Union[SchedulerOutput, Tuple]:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						# advance index counter by 1
						__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
									__a						   =self.sigmas_interpol[step_index]
									__a						   =self.sigmas_up[step_index]
									__a						   =self.sigmas_down[step_index - 1]
						else:
									# 2nd order / KPDM2's method
									__a						   =self.sigmas[step_index - 1]
									__a						   =self.sigmas_interpol[step_index - 1]
									__a						   =self.sigmas_up[step_index - 1]
									__a						   =self.sigmas_down[step_index - 1]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						__a						   =0
						__a						   =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						__a						   =model_output.device
						__a						   =randn_tensor(model_output.shape      ,						dtype=model_output.dtype      ,						device=__snake_case      ,						generator=__snake_case      )

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
						elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample'      )
						else:
									raise ValueError(
									    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

						if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									__a						   =(sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__a						   =sigma_interpol - sigma_hat

									# store for 2nd order step
									__a						   =sample
									__a						   =dt
									__a						   =sample + derivative * dt
						else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									__a						   =(sample - pred_original_sample) / sigma_interpol
									# 3. delta timestep
									__a						   =sigma_down - sigma_hat

									__a						   =self.sample
									__a						   =None

									__a						   =sample + derivative * dt
									__a						   =prev_sample + noise * sigma_up

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__a						   =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
									__a						   =self.timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
									__a						   =timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
						else:
									__a						   =self.timesteps.to(original_samples.device      )
									__a						   =timesteps.to(original_samples.device      )

						__a						   =[self.index_for_timestep(__snake_case      ,						__snake_case      ) for t in timesteps]

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

						__a						   =original_samples + noise * sigma
						return noisy_samples





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




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






