




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 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = 42


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase  ):
					_lowerCamelCase		  = 1

					@register_to_config
					def __init__(				self ,       __lowercase = 40 ,       __lowercase = 0.0_02 ,       __lowercase = 80.0 ,       __lowercase = 0.5 ,       __lowercase = 1.0 ,       __lowercase = 7.0 ,       __lowercase = True ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							sigma_max

						lowerCamelCase_							:		Dict							   =							np.linspace(0 ,       1 ,       __lowercase					)
						lowerCamelCase_							:		List[Any]							   =							self._convert_to_karras(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							self.sigma_to_t(__lowercase					)

						# setable values
						lowerCamelCase_							:		Optional[int]							   =							None
						lowerCamelCase_							:		Dict							   =							torch.from_numpy(__lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							torch.from_numpy(__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							False
						lowerCamelCase_							:		List[str]							   =							False

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase=None					):







						'''simple docstring'''




						if schedule_timesteps is None:
							lowerCamelCase_							:		Optional[Any]							   =							self.timesteps

						lowerCamelCase_							:		Optional[int]							   =							(schedule_timesteps == timestep).nonzero()
						return indices.item()

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if isinstance(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		List[Any]							   =							timestep.to(self.timesteps.device					)
						lowerCamelCase_							:		List[str]							   =							self.index_for_timestep(__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							self.sigmas[step_idx]

						lowerCamelCase_							:		int							   =							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

						lowerCamelCase_							:		int							   =							True
						return sample

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						if not isinstance(__lowercase ,       np.ndarray					):
							lowerCamelCase_							:		str							   =							np.array(__lowercase ,       dtype=np.floataa					)

						lowerCamelCase_							:		Optional[Any]							   =							1_000 * 0.25 * np.log(sigmas + 1E-44					)

						return timesteps

					def 	lowercase_							(				self ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       ):







						'''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(__lowercase					)					):
								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_							:		Optional[int]							   =							np.array(__lowercase ,       dtype=np.intaa					)
							lowerCamelCase_							:		Union[str, Any]							   =							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_							:		Tuple							   =							num_inference_steps

							lowerCamelCase_							:		int							   =							self.config.num_train_timesteps // self.num_inference_steps
							lowerCamelCase_							:		str							   =							(np.arange(0 ,       __lowercase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
							lowerCamelCase_							:		Optional[int]							   =							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_							:		Optional[Any]							   =							self.config.num_train_timesteps
						lowerCamelCase_							:		str							   =							timesteps[::-1].copy()
						lowerCamelCase_							:		Optional[int]							   =							ramp / (num_train_timesteps - 1)
						lowerCamelCase_							:		int							   =							self._convert_to_karras(__lowercase					)
						lowerCamelCase_							:		int							   =							self.sigma_to_t(__lowercase					)

						lowerCamelCase_							:		List[str]							   =							np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
						lowerCamelCase_							:		Dict							   =							torch.from_numpy(__lowercase					).to(device=__lowercase					)

						if str(__lowercase					).startswith('mps'					):
							# mps does not support float64
							lowerCamelCase_							:		List[str]							   =							torch.from_numpy(__lowercase					).to(__lowercase ,       dtype=torch.floataa					)
						else:
							lowerCamelCase_							:		Dict							   =							torch.from_numpy(__lowercase					).to(device=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		float							   =							self.config.sigma_min
						lowerCamelCase_							:		float							   =							self.config.sigma_max

						lowerCamelCase_							:		Union[str, Any]							   =							self.config.rho
						lowerCamelCase_							:		Any							   =							sigma_min ** (1 / rho)
						lowerCamelCase_							:		Any							   =							sigma_max ** (1 / rho)
						lowerCamelCase_							:		List[str]							   =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.config.sigma_data

						lowerCamelCase_							:		str							   =							sigma_data**2 / (sigma**2 + sigma_data**2)
						lowerCamelCase_							:		Dict							   =							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
						return c_skip, c_out

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							self.config.sigma_min
						lowerCamelCase_							:		str							   =							self.config.sigma_data

						lowerCamelCase_							:		List[Any]							   =							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
						lowerCamelCase_							:		Any							   =							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
						return c_skip, c_out

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       __lowercase = True ,       ):







						'''simple docstring'''




						if (
						    isinstance(__lowercase ,       __lowercase					)
						    or isinstance(__lowercase ,       torch.IntTensor					)
						    or isinstance(__lowercase ,       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(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		Union[str, Any]							   =							timestep.to(self.timesteps.device					)

						lowerCamelCase_							:		List[Any]							   =							self.config.sigma_min
						lowerCamelCase_							:		Union[str, Any]							   =							self.config.sigma_max

						lowerCamelCase_							:		Tuple							   =							self.index_for_timestep(__lowercase					)

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

						# Get scalings for boundary conditions
						lowerCamelCase_   ,			lowerCamelCase_							:		Optional[Any]							   =							self.get_scalings_for_boundary_condition(__lowercase					)

						# 1. Denoise model output using boundary conditions
						lowerCamelCase_							:		Any							   =							c_out * model_output + c_skip * sample
						if self.config.clip_denoised:
							lowerCamelCase_							:		Dict							   =							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_							:		List[Any]							   =							randn_tensor(
							    model_output.shape ,       dtype=model_output.dtype ,       device=model_output.device ,       generator=__lowercase					)
						else:
							lowerCamelCase_							:		Tuple							   =							torch.zeros_like(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							noise * self.config.s_noise

						lowerCamelCase_							:		Union[str, Any]							   =							sigma_next.clamp(min=__lowercase ,       max=__lowercase					)

						# 3. Return noisy sample
						# tau = sigma_hat, eps = sigma_min
						lowerCamelCase_							:		List[Any]							   =							denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

						if not return_dict:
							return (prev_sample,)

						return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							   =							self.sigmas.to(device=original_samples.device ,       dtype=original_samples.dtype					)
						if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase					):
							# 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_							:		str							   =							self.timesteps.to(original_samples.device					)
							lowerCamelCase_							:		Optional[int]							   =							timesteps.to(original_samples.device					)

						lowerCamelCase_							:		str							   =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

						lowerCamelCase_							:		Optional[int]							   =							original_samples + noise * sigma
						return noisy_samples





					def __len__(				self					):







						'''simple docstring'''




						return self.config.num_train_timesteps





"""simple docstring"""
def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							str							)		->				int:
				# The longer word should come first
				if len(lowercase_							) < len(lowercase_							):
								return levenshtein_distance(lowercase_  ,      lowercase_							)

				if len(lowercase_							) == 0:
								return len(lowercase_							)

				_lowerCamelCase					     = list(range(len(lowercase_							) + 1							)							)

				for i, ca in enumerate(lowercase_							):
								_lowerCamelCase					     = [i + 1]

								for j, ca in enumerate(lowercase_							):
												# Calculate insertions, deletions and substitutions
												_lowerCamelCase					     = previous_row[j + 1] + 1
												_lowerCamelCase					     = current_row[j] + 1
												_lowerCamelCase					     = previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(lowercase_  ,      lowercase_  ,      lowercase_							)							)

								# Store the previous row
								_lowerCamelCase					     = current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  int										=					input('''Enter the first word:\n''').strip()
			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					input('''Enter the second word:\n''').strip()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					levenshtein_distance(first_word, second_word)
			print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")
