



'''simple docstring'''







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 SchedulerMixin


_UpperCamelCase  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	42





class      lowerCamelCase__     (     _A,     _A				):






	'''simple docstring'''

	A__		=	1




	@register_to_config
	def __init__(  self		:  Any   ,		__A		:  int = 40   ,		__A		:  float = 0.0_0_2   ,		__A		:  float = 8_0.0   ,		__A		:  float = 0.5   ,		__A		:  float = 1.0   ,		__A		:  float = 7.0   ,		__A		:  bool = True   ,		)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   sigma_max

								lowerCAmelCase__  =   np.linspace(0   ,		1   ,		__A					)
								lowerCAmelCase__  =   self._convert_to_karras(__A					)
								lowerCAmelCase__  =   self.sigma_to_t(__A					)

								# setable values
								lowerCAmelCase__  =   None
								lowerCAmelCase__  =   torch.from_numpy(__A					)
								lowerCAmelCase__  =   torch.from_numpy(__A					)
								lowerCAmelCase__  =   False
								lowerCAmelCase__  =   False




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Union[str, Any]   ,		__A		:  Union[str, Any]=None					)     ->			Optional[Any]:




								'''simple docstring'''





								if schedule_timesteps is None:
															lowerCAmelCase__  =   self.timesteps

								lowerCAmelCase__  =   (schedule_timesteps == timestep).nonzero()
								return indices.item()




	def      lowercase__							(  self		:  Any   ,		__A		:  torch.FloatTensor   ,		__A		:  Union[float, torch.FloatTensor]					)     ->			torch.FloatTensor:




								'''simple docstring'''





								if isinstance(__A   ,		torch.Tensor					):
															lowerCAmelCase__  =   timestep.to(self.timesteps.device					)
								lowerCAmelCase__  =   self.index_for_timestep(__A					)
								lowerCAmelCase__  =   self.sigmas[step_idx]

								lowerCAmelCase__  =   sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								lowerCAmelCase__  =   True
								return sample




	def      lowercase__							(  self		:  List[str]   ,		__A		:  Union[float, np.ndarray]					)     ->			Any:




								'''simple docstring'''





								if not isinstance(__A   ,		np.ndarray					):
															lowerCAmelCase__  =   np.array(__A   ,		dtype=np.floataa					)

								lowerCAmelCase__  =   1000 * 0.2_5 * np.log(sigmas + 1E-44					)

								return timesteps




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Optional[int] = None   ,		__A		:  Union[str, torch.device] = None   ,		__A		:  Optional[List[int]] = None   ,		)     ->			Optional[Any]:




								'''simple docstring'''





								if num_inference_steps is None and timesteps is None:
															raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""					)

								if num_inference_steps is not None and timesteps is not None:
															raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""					)

								# Follow DDPMScheduler custom timesteps logic
								if timesteps is not None:
															for i in range(1   ,		len(__A					)					):
																						if timesteps[i] >= timesteps[i - 1]:
																													raise ValueError("""`timesteps` must be in descending order."""					)

															if timesteps[0] >= self.config.num_train_timesteps:
																						raise ValueError(
																						    f'''`timesteps` must start before `self.config.train_timesteps`:'''
																						    f''' {self.config.num_train_timesteps}.'''					)

															lowerCAmelCase__  =   np.array(__A   ,		dtype=np.intaa					)
															lowerCAmelCase__  =   True
								else:
															if num_inference_steps > self.config.num_train_timesteps:
																						raise ValueError(
																						    f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
																						    f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
																						    f''' maximal {self.config.num_train_timesteps} timesteps.'''					)

															lowerCAmelCase__  =   num_inference_steps

															lowerCAmelCase__  =   self.config.num_train_timesteps // self.num_inference_steps
															lowerCAmelCase__  =   (np.arange(0   ,		__A					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
															lowerCAmelCase__  =   False

								# Map timesteps to Karras sigmas directly for multistep sampling
								# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
								lowerCAmelCase__  =   self.config.num_train_timesteps
								lowerCAmelCase__  =   timesteps[::-1].copy()
								lowerCAmelCase__  =   ramp / (num_train_timesteps - 1)
								lowerCAmelCase__  =   self._convert_to_karras(__A					)
								lowerCAmelCase__  =   self.sigma_to_t(__A					)

								lowerCAmelCase__  =   np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
								lowerCAmelCase__  =   torch.from_numpy(__A					).to(device=__A					)

								if str(__A					).startswith("""mps"""					):
															# mps does not support float64
															lowerCAmelCase__  =   torch.from_numpy(__A					).to(__A   ,		dtype=torch.floataa					)
								else:
															lowerCAmelCase__  =   torch.from_numpy(__A					).to(device=__A					)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  Union[str, Any]					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.config.sigma_min
								lowerCAmelCase__  =   self.config.sigma_max

								lowerCAmelCase__  =   self.config.rho
								lowerCAmelCase__  =   sigma_min ** (1 / rho)
								lowerCAmelCase__  =   sigma_max ** (1 / rho)
								lowerCAmelCase__  =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.config.sigma_data

								lowerCAmelCase__  =   sigma_data**2 / (sigma**2 + sigma_data**2)
								lowerCAmelCase__  =   sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.config.sigma_min
								lowerCAmelCase__  =   self.config.sigma_data

								lowerCAmelCase__  =   sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								lowerCAmelCase__  =   (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out




	def      lowercase__							(  self		:  str   ,		__A		:  torch.FloatTensor   ,		__A		:  Union[float, torch.FloatTensor]   ,		__A		:  torch.FloatTensor   ,		__A		:  Optional[torch.Generator] = None   ,		__A		:  bool = True   ,		)     ->			Union[CMStochasticIterativeSchedulerOutput, Tuple]:




								'''simple docstring'''





								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"""
															        f''' `{self.__class__}.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					):
															lowerCAmelCase__  =   timestep.to(self.timesteps.device					)

								lowerCAmelCase__  =   self.config.sigma_min
								lowerCAmelCase__  =   self.config.sigma_max

								lowerCAmelCase__  =   self.index_for_timestep(__A					)

								# sigma_next corresponds to next_t in original implementation
								lowerCAmelCase__  =   self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
															lowerCAmelCase__  =   self.sigmas[step_index + 1]
								else:
															# Set sigma_next to sigma_min
															lowerCAmelCase__  =   self.sigmas[-1]

								# Get scalings for boundary conditions
								lowerCAmelCase__  ,lowerCAmelCase__  =   self.get_scalings_for_boundary_condition(__A					)

								# 1. Denoise model output using boundary conditions
								lowerCAmelCase__  =   c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
															lowerCAmelCase__  =   denoised.clamp(-1   ,		1					)

								# 2. Sample z ~ N(0, s_noise^2 * I)
								# Noise is not used for onestep sampling.
								if len(self.timesteps					) > 1:
															lowerCAmelCase__  =   randn_tensor(
															    model_output.shape   ,		dtype=model_output.dtype   ,		device=model_output.device   ,		generator=__A					)
								else:
															lowerCAmelCase__  =   torch.zeros_like(__A					)
								lowerCAmelCase__  =   noise * self.config.s_noise

								lowerCAmelCase__  =   sigma_next.clamp(min=__A   ,		max=__A					)

								# 3. Return noisy sample
								# tau = sigma_hat, eps = sigma_min
								lowerCAmelCase__  =   denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

								if not return_dict:
															return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__A					)




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




								'''simple docstring'''





								lowerCAmelCase__  =   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__  =   self.timesteps.to(original_samples.device   ,		dtype=torch.floataa					)
															lowerCAmelCase__  =   timesteps.to(original_samples.device   ,		dtype=torch.floataa					)
								else:
															lowerCAmelCase__  =   self.timesteps.to(original_samples.device					)
															lowerCAmelCase__  =   timesteps.to(original_samples.device					)

								lowerCAmelCase__  =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

								lowerCAmelCase__  =   original_samples + noise * sigma
								return noisy_samples




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




								'''simple docstring'''





								return self.config.num_train_timesteps










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str						) ->							str:
							lowerCAmelCase__  =   word.upper()
							lowerCAmelCase__  =   0
							lowerCAmelCase__  =   {}
							lowerCAmelCase__  =   []

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


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

		_UpperCamelCase  						=			time.time()
		with open("""dictionary.txt""") as in_file:
				_UpperCamelCase  						=			in_file.read().splitlines()

		_UpperCamelCase  						=			{}
		for word in word_list:
				_UpperCamelCase  						=			get_word_pattern(word)
				if pattern in all_patterns:
						all_patterns[pattern].append(word)
				else:
						_UpperCamelCase  						=			[word]

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

		_UpperCamelCase  						=			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.





