



"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left

@flax.struct.dataclass
class 	__lowerCAmelCase							:


					'''simple docstring'''


					a__        =     None
					a__        =     None
					a__        =     None







					@classmethod
					def 		_a (      cls   ):




										"""simple docstring"""





										return cls()



@dataclass
class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     42
					a__        =     42
					a__        =     None



class 	__lowerCAmelCase							(__UpperCamelCase					,__UpperCamelCase  ):


					'''simple docstring'''


					@property
					def 		_a (      self   ):




										"""simple docstring"""





										return True







					@register_to_config
					def __init__(      self    ,       a = 20_00    ,       a = 0.15    ,       a = 0.01    ,       a = 1348.0    ,       a = 1e-5    ,       a = 1    ,       ):




										"""simple docstring"""





										pass







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						ScoreSdeVeSchedulerState.create()
										return self.set_sigmas(
										    a    ,       self.config.num_train_timesteps    ,       self.config.sigma_min    ,       self.config.sigma_max    ,       self.config.sampling_eps    ,       )







					def 		_a (      self    ,       a    ,       a    ,       a = ()    ,       a = None   ):




										"""simple docstring"""





										snake_case_						:Dict								=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

										snake_case_						:Optional[Any]								=						jnp.linspace(1    ,       a    ,       a   )
										return state.replace(timesteps=a   )







					def 		_a (      self    ,       a    ,       a    ,       a = None    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Any								=						sigma_min if sigma_min is not None else self.config.sigma_min
										snake_case_						:Any								=						sigma_max if sigma_max is not None else self.config.sigma_max
										snake_case_						:Optional[int]								=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if state.timesteps is None:
															snake_case_						:List[str]								=						self.set_timesteps(a    ,       a    ,       a   )

										snake_case_						:int								=						jnp.exp(jnp.linspace(jnp.log(a   )    ,       jnp.log(a   )    ,       a   )   )
										snake_case_						:int								=						jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

										return state.replace(discrete_sigmas=a    ,       sigmas=a   )







					def 		_a (      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										return jnp.where(timesteps == 0    ,       jnp.zeros_like(a   )    ,       state.discrete_sigmas[timesteps - 1]   )







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a = True    ,       ):




										"""simple docstring"""





										if state.timesteps is None:
															raise ValueError(
															    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

										snake_case_						:str								=						timestep * jnp.ones(
										    sample.shape[0]    ,       )
										snake_case_						:Dict								=						(timestep * (len(state.timesteps   ) - 1)).long()

										snake_case_						:int								=						state.discrete_sigmas[timesteps]
										snake_case_						:List[Any]								=						self.get_adjacent_sigma(a    ,       a    ,       a   )
										snake_case_						:int								=						jnp.zeros_like(a   )
										snake_case_						:Dict								=						(sigma**2 - adjacent_sigma**2) ** 0.5

										# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
										# also equation 47 shows the analog from SDE models to ancestral sampling methods
										snake_case_						:Union[str, Any]								=						diffusion.flatten()
										snake_case_						:Optional[Any]								=						broadcast_to_shape_from_left(a    ,       sample.shape   )
										snake_case_						:int								=						drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										snake_case_						:Optional[int]								=						random.split(a    ,       num=1   )
										snake_case_						:Dict								=						random.normal(key=a    ,       shape=sample.shape   )
										snake_case_						:Any								=						sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										snake_case_						:str								=						prev_sample_mean + diffusion * noise  # add impact of diffusion field g

										if not return_dict:
															return (prev_sample, prev_sample_mean, state)

										return FlaxSdeVeOutput(prev_sample=a    ,       prev_sample_mean=a    ,       state=a   )







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a    ,       a = True    ,       ):




										"""simple docstring"""





										if state.timesteps is None:
															raise ValueError(
															    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

										# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
										# sample noise for correction
										snake_case_						:Optional[Any]								=						random.split(a    ,       num=1   )
										snake_case_						:str								=						random.normal(key=a    ,       shape=sample.shape   )

										# compute step size from the model_output, the noise, and the snr
										snake_case_						:Union[str, Any]								=						jnp.linalg.norm(a   )
										snake_case_						:Tuple								=						jnp.linalg.norm(a   )
										snake_case_						:Union[str, Any]								=						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
										snake_case_						:List[Any]								=						step_size * jnp.ones(sample.shape[0]   )

										# compute corrected sample: model_output term and noise term
										snake_case_						:Dict								=						step_size.flatten()
										snake_case_						:int								=						broadcast_to_shape_from_left(a    ,       sample.shape   )
										snake_case_						:Tuple								=						sample + step_size * model_output
										snake_case_						:str								=						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
															return (prev_sample, state)

										return FlaxSdeVeOutput(prev_sample=a    ,       state=a   )







					def __len__(      self   ):




										"""simple docstring"""





										return self.config.num_train_timesteps






"""simple docstring"""






def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     ):




					"""simple docstring"""
					snake_case_						:List[Any]								=						list(_A							)
					snake_case_						:int								=						list(_A							)
					snake_case_						:List[Any]								=						len(_A							)
					snake_case_						:List[Any]								=						len(_A							)

					snake_case_						:Union[str, Any]								=						[
					    [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]
					snake_case_						:int								=						[
					    ["0" for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]

					for i in range(1,     len_source_seq + 1							):
										snake_case_						:List[str]								=						i * delete_cost
										snake_case_						:str								=						F'''D{source_seq[i - 1]:c}'''

					for i in range(1,     len_destination_seq + 1							):
										snake_case_						:int								=						i * insert_cost
										snake_case_						:Dict								=						F'''I{destination_seq[i - 1]:c}'''

					for i in range(1,     len_source_seq + 1							):
										for j in range(1,     len_destination_seq + 1							):
															if source_seq[i - 1] == destination_seq[j - 1]:
																				snake_case_						:List[Any]								=						costs[i - 1][j - 1] + copy_cost
																				snake_case_						:str								=						F'''C{source_seq[i - 1]:c}'''
															else:
																				snake_case_						:Any								=						costs[i - 1][j - 1] + replace_cost
																				snake_case_						:Any								=						F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

															if costs[i - 1][j] + delete_cost < costs[i][j]:
																				snake_case_						:str								=						costs[i - 1][j] + delete_cost
																				snake_case_						:Tuple								=						F'''D{source_seq[i - 1]:c}'''

															if costs[i][j - 1] + insert_cost < costs[i][j]:
																				snake_case_						:int								=						costs[i][j - 1] + insert_cost
																				snake_case_						:int								=						F'''I{destination_seq[j - 1]:c}'''

					return costs, ops
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					if i == 0 and j == 0:
										return []
					else:
										if ops[i][j][0] == "C" or ops[i][j][0] == "R":
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     i - 1,     j - 1							)
															seq.append(ops[i][j]							)
															return seq
										elif ops[i][j][0] == "D":
															snake_case_						:Union[str, Any]								=						assemble_transformation(_A,     i - 1,     _A							)
															seq.append(ops[i][j]							)
															return seq
										else:
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     _A,     j - 1							)
															seq.append(ops[i][j]							)
															return seq


if __name__ == "__main__":
				__UpperCAmelCase					,	__UpperCAmelCase      :      List[Any]   					=					compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				__UpperCAmelCase      :      List[Any]   					=					len(operations)
				__UpperCAmelCase      :      Dict   					=					len(operations[0])
				__UpperCAmelCase      :      List[str]   					=					assemble_transformation(operations, m - 1, n - 1)

				__UpperCAmelCase      :      Any   					=					list('Python')
				__UpperCAmelCase      :      Tuple   					=					0
				__UpperCAmelCase      :      Optional[int]   					=					0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																__UpperCAmelCase      :      Any   					=					op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))

