code: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  lowercase_			       =				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class       a_  (       snake_case_ ):
  
  				'''simple docstring'''
  				UpperCamelCase									=				42
  				UpperCamelCase									=				None
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Any=0.999      , __lowerCamelCase				:					Optional[int]="cosine"      , )		->List[str]:
  				if alpha_transform_type == "cosine":
  
  								def alpha_bar_fn(__lowerCamelCase				:					Dict					):
  												return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2
  
  				elif alpha_transform_type == "exp":
  
  								def alpha_bar_fn(__lowerCamelCase				:					List[Any]					):
  												return math.exp(t * -12.0					)
  
  				else:
  								raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'					)
  
  				_SCREAMING_SNAKE_CASE             =    []
  				for i in range(__lowerCamelCase					):
  								_SCREAMING_SNAKE_CASE             =    i / num_diffusion_timesteps
  								_SCREAMING_SNAKE_CASE             =    (i + 1) / num_diffusion_timesteps
  								betas.append(min(1 - alpha_bar_fn(__lowerCamelCase					) / alpha_bar_fn(__lowerCamelCase					)      , __lowerCamelCase					)					)
  				return torch.tensor(__lowerCamelCase      , dtype=torch.floataa					)
  
  
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
  				'''simple docstring'''
  				UpperCamelCase									=				[e.name for e in KarrasDiffusionSchedulers]
  				UpperCamelCase									=				1
  
  
  				@register_to_config
  				def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = "epsilon"				,	A = "linspace"				,	A = 0				,	)	->     List[Any]:
  								if trained_betas is not None:
  												_SCREAMING_SNAKE_CASE             =    torch.tensor(A				,	dtype=torch.floataa )
  								elif beta_schedule == "linear":
  												_SCREAMING_SNAKE_CASE             =    torch.linspace(A				,	A				,	A				,	dtype=torch.floataa )
  								elif beta_schedule == "scaled_linear":
  												# this schedule is very specific to the latent diffusion model.
  												_SCREAMING_SNAKE_CASE             =    (
  												    torch.linspace(beta_start**0.5				,	beta_end**0.5				,	A				,	dtype=torch.floataa ) ** 2
  												)
  								elif beta_schedule == "squaredcos_cap_v2":
  												# Glide cosine schedule
  												_SCREAMING_SNAKE_CASE             =    betas_for_alpha_bar(A )
  								else:
  												raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
  
  								_SCREAMING_SNAKE_CASE             =    1.0 - self.betas
  								_SCREAMING_SNAKE_CASE             =    torch.cumprod(self.alphas				,	dim=0 )
  
  								_SCREAMING_SNAKE_CASE             =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
  								_SCREAMING_SNAKE_CASE             =    np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
  								_SCREAMING_SNAKE_CASE             =    torch.from_numpy(A )
  
  								# setable values
  								_SCREAMING_SNAKE_CASE             =    None
  								_SCREAMING_SNAKE_CASE             =    np.linspace(0				,	num_train_timesteps - 1				,	A				,	dtype=A )[::-1].copy()
  								_SCREAMING_SNAKE_CASE             =    torch.from_numpy(A )
  								_SCREAMING_SNAKE_CASE             =    False
  
  
  				@property
  				def     snake_case_(	self )	->     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     snake_case_(	self				,	A				,	A )	->     torch.FloatTensor:
  								if isinstance(A				,	torch.Tensor ):
  												_SCREAMING_SNAKE_CASE             =    timestep.to(self.timesteps.device )
  								_SCREAMING_SNAKE_CASE             =    (self.timesteps == timestep).nonzero().item()
  								_SCREAMING_SNAKE_CASE             =    self.sigmas[step_index]
  								_SCREAMING_SNAKE_CASE             =    sample / ((sigma**2 + 1) ** 0.5)
  								_SCREAMING_SNAKE_CASE             =    True
  								return sample
  
  
  				def     snake_case_(	self				,	A				,	A = None )	->     Optional[Any]:
  								_SCREAMING_SNAKE_CASE             =    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":
  												_SCREAMING_SNAKE_CASE             =    np.linspace(0				,	self.config.num_train_timesteps - 1				,	A				,	dtype=A )[
  												    ::-1
  												].copy()
  								elif self.config.timestep_spacing == "leading":
  												_SCREAMING_SNAKE_CASE             =    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
  												_SCREAMING_SNAKE_CASE             =    (np.arange(0				,	A ) * step_ratio).round()[::-1].copy().astype(A )
  												timesteps += self.config.steps_offset
  								elif self.config.timestep_spacing == "trailing":
  												_SCREAMING_SNAKE_CASE             =    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
  												_SCREAMING_SNAKE_CASE             =    (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\'.' )
  
  								_SCREAMING_SNAKE_CASE             =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
  								_SCREAMING_SNAKE_CASE             =    np.interp(A				,	np.arange(0				,	len(A ) )				,	A )
  								_SCREAMING_SNAKE_CASE             =    np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
  								_SCREAMING_SNAKE_CASE             =    torch.from_numpy(A ).to(device=A )
  								if str(A ).startswith("""mps""" ):
  												# mps does not support float64
  												_SCREAMING_SNAKE_CASE             =    torch.from_numpy(A ).to(A				,	dtype=torch.floataa )
  								else:
  												_SCREAMING_SNAKE_CASE             =    torch.from_numpy(A ).to(device=A )
  
  
  				def     snake_case_(	self				,	A				,	A				,	A				,	A = None				,	A = True				,	)	->     Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:
  
  								if (
  								    isinstance(A				,	A )
  								    or isinstance(A				,	torch.IntTensor )
  								    or isinstance(A				,	torch.LongTensor )
  								):
  												raise ValueError(
  												    (
  												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
  												        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
  												        """ one of the `scheduler.timesteps` as a timestep."""
  												    )				,	)
  
  								if not self.is_scale_input_called:
  												logger.warning(
  												    """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 ):
  												_SCREAMING_SNAKE_CASE             =    timestep.to(self.timesteps.device )
  
  								_SCREAMING_SNAKE_CASE             =    (self.timesteps == timestep).nonzero().item()
  								_SCREAMING_SNAKE_CASE             =    self.sigmas[step_index]
  
  								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  								if self.config.prediction_type == "epsilon":
  												_SCREAMING_SNAKE_CASE             =    sample - sigma * model_output
  								elif self.config.prediction_type == "v_prediction":
  												# * c_out + input * c_skip
  												_SCREAMING_SNAKE_CASE             =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`' )
  
  								_SCREAMING_SNAKE_CASE             =    self.sigmas[step_index]
  								_SCREAMING_SNAKE_CASE             =    self.sigmas[step_index + 1]
  								_SCREAMING_SNAKE_CASE             =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
  								_SCREAMING_SNAKE_CASE             =    (sigma_to**2 - sigma_up**2) ** 0.5
  
  								# 2. Convert to an ODE derivative
  								_SCREAMING_SNAKE_CASE             =    (sample - pred_original_sample) / sigma
  
  								_SCREAMING_SNAKE_CASE             =    sigma_down - sigma
  
  								_SCREAMING_SNAKE_CASE             =    sample + derivative * dt
  
  								_SCREAMING_SNAKE_CASE             =    model_output.device
  								_SCREAMING_SNAKE_CASE             =    randn_tensor(model_output.shape				,	dtype=model_output.dtype				,	device=A				,	generator=A )
  
  								_SCREAMING_SNAKE_CASE             =    prev_sample + noise * sigma_up
  
  								if not return_dict:
  												return (prev_sample,)
  
  								return EulerAncestralDiscreteSchedulerOutput(
  								    prev_sample=A				,	pred_original_sample=A )
  
  
  				def     snake_case_(	self				,	A				,	A				,	A				,	)	->     torch.FloatTensor:
  								# Make sure sigmas and timesteps have the same device and dtype as original_samples
  								_SCREAMING_SNAKE_CASE             =    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
  												_SCREAMING_SNAKE_CASE             =    self.timesteps.to(original_samples.device				,	dtype=torch.floataa )
  												_SCREAMING_SNAKE_CASE             =    timesteps.to(original_samples.device				,	dtype=torch.floataa )
  								else:
  												_SCREAMING_SNAKE_CASE             =    self.timesteps.to(original_samples.device )
  												_SCREAMING_SNAKE_CASE             =    timesteps.to(original_samples.device )
  
  								_SCREAMING_SNAKE_CASE             =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  								_SCREAMING_SNAKE_CASE             =    sigmas[step_indices].flatten()
  								while len(sigma.shape ) < len(original_samples.shape ):
  												_SCREAMING_SNAKE_CASE             =    sigma.unsqueeze(-1 )
  
  								_SCREAMING_SNAKE_CASE             =    original_samples + noise * sigma
  								return noisy_samples
  
  
  
  
  
  				def __len__(	self )	->     Optional[int]:
  								return self.config.num_train_timesteps
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      _SCREAMING_SNAKE_CASE             =    word.upper()
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    {}
      _SCREAMING_SNAKE_CASE             =    []
  
      for letter in word:
          if letter not in letter_nums:
              _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
              next_num += 1
          word_pattern.append(letter_nums[letter]					)
      return ".".join(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        import pprint
        import time
  
        lowercase_			       =				time.time()
        with open("""dictionary.txt""") as in_file:
              lowercase_			       =				in_file.read().splitlines()
  
        lowercase_			       =				{}
        for word in word_list:
              lowercase_			       =				get_word_pattern(word)
              if pattern in all_patterns:
                    all_patterns[pattern].append(word)
              else:
                    lowercase_			       =				[word]
  
        with open("""word_patterns.txt""", """w""") as out_file:
              out_file.write(pprint.pformat(all_patterns))
  
        lowercase_			       =				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.
  
  
  
  