




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 		A__				:
	lowerCamelCase__		: CommonSchedulerState

	# setable values
	lowerCamelCase__		: jnp.ndarray
	lowerCamelCase__		: jnp.ndarray
	lowerCamelCase__		: jnp.ndarray
	lowerCamelCase__		: Optional[int]				    =None

	# running values
	lowerCamelCase__		: Optional[jnp.ndarray]				    =None






	@classmethod
	def  lowercase      (	cls				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						return cls(common=lowerCamelCase				,     init_noise_sigma=lowerCamelCase				,     timesteps=lowerCamelCase				,     sigmas=lowerCamelCase						)





@dataclass
class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: LMSDiscreteSchedulerState





class 		A__				(   __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Optional[Any]				    =[e.name for e in FlaxKarrasDiffusionSchedulers]

	lowerCamelCase__		: jnp.dtype






	@property
	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""simple docstring"""






						return True






	@register_to_config
	def __init__(	self				,     lowerCamelCase = 1000				,     lowerCamelCase = 0.0_0_0_1				,     lowerCamelCase = 0.0_2				,     lowerCamelCase = "linear"				,     lowerCamelCase = None				,     lowerCamelCase = "epsilon"				,     lowerCamelCase = jnp.floataa				,     )      ->   int:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							dtype






	def  lowercase      (	self				,     lowerCamelCase = None						)      ->   LMSDiscreteSchedulerState:




						"""simple docstring"""






						if common is None:
											__magic_name__					:							Union[str, Any]		   =							CommonSchedulerState.create(self						)

						__magic_name__					:							int		   =							jnp.arange(0				,     self.config.num_train_timesteps						).round()[::-1]
						__magic_name__					:							List[str]		   =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						__magic_name__					:							Tuple		   =							sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=lowerCamelCase				,     init_noise_sigma=lowerCamelCase				,     timesteps=lowerCamelCase				,     sigmas=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   jnp.ndarray:




						"""simple docstring"""






						((__magic_name__)   , )					:							Optional[Any]		   =							jnp.where(state.timesteps == timestep				,     size=1						)
						__magic_name__					:							List[str]		   =							step_index[0]

						__magic_name__					:							Any		   =							state.sigmas[step_index]
						__magic_name__					:							List[Any]		   =							sample / ((sigma**2 + 1) ** 0.5)
						return sample






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						def lms_derivative(lowerCamelCase						):
											__magic_name__					:							List[Any]		   =							1.0
											for k in range(lowerCamelCase						):
																if current_order == k:
																					continue
																prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
											return prod

						__magic_name__					:							Any		   =							integrate.quad(lowerCamelCase				,     state.sigmas[t]				,     state.sigmas[t + 1]				,     epsrel=1e-4						)[0]

						return integrated_coeff






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase = ()						)      ->   LMSDiscreteSchedulerState:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							jnp.linspace(self.config.num_train_timesteps - 1				,     0				,     lowerCamelCase				,     dtype=self.dtype						)

						__magic_name__					:							Optional[Any]		   =							jnp.floor(lowerCamelCase						).astype(jnp.intaa						)
						__magic_name__					:							Any		   =							jnp.ceil(lowerCamelCase						).astype(jnp.intaa						)

						__magic_name__					:							Dict		   =							jnp.mod(lowerCamelCase				,     1.0						)

						__magic_name__					:							Union[str, Any]		   =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						__magic_name__					:							Optional[Any]		   =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						__magic_name__					:							List[str]		   =							jnp.concatenate([sigmas, jnp.array([0.0]				,     dtype=self.dtype						)]						)

						__magic_name__					:							Tuple		   =							timesteps.astype(jnp.intaa						)

						# initial running values
						__magic_name__					:							List[str]		   =							jnp.zeros((0,) + shape				,     dtype=self.dtype						)

						return state.replace(
						    timesteps=lowerCamelCase				,     sigmas=lowerCamelCase				,     num_inference_steps=lowerCamelCase				,     derivatives=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase = 4				,     lowerCamelCase = True				,     )      ->   Union[FlaxLMSSchedulerOutput, Tuple]:




						"""simple docstring"""






						if state.num_inference_steps is None:
											raise ValueError(
											    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

						__magic_name__					:							Tuple		   =							state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											__magic_name__					:							Optional[int]		   =							sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											__magic_name__					:							int		   =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						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
						__magic_name__					:							Tuple		   =							(sample - pred_original_sample) / sigma
						__magic_name__					:							Any		   =							state.replace(derivatives=jnp.append(state.derivatives				,     lowerCamelCase						)						)
						if len(state.derivatives						) > order:
											__magic_name__					:							List[str]		   =							state.replace(derivatives=jnp.delete(state.derivatives				,     0						)						)

						# 3. Compute linear multistep coefficients
						__magic_name__					:							int		   =							min(timestep + 1				,     lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							[self.get_lms_coefficient(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						) for curr_order in range(lowerCamelCase						)]

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

						if not return_dict:
											return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,     state=lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     )      ->   jnp.ndarray:




						"""simple docstring"""






						__magic_name__					:							int		   =							state.sigmas[timesteps].flatten()
						__magic_name__					:							Any		   =							broadcast_to_shape_from_left(lowerCamelCase				,     noise.shape						)

						__magic_name__					:							Optional[Any]		   =							original_samples + noise * sigma

						return noisy_samples






	def __len__(	self						)      ->   Any:




						"""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}""")
