

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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin



@flax.struct.dataclass
class   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[int]      							=					None
			_UpperCAmelCase       :							Optional[jnp.ndarray]      							=					None
			_UpperCAmelCase       :							Optional[jnp.ndarray]      							=					None  # sigma(t_i)



			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					List[str]):
									return cls()







@dataclass
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							jnp.ndarray
			_UpperCAmelCase       :							jnp.ndarray
			_UpperCAmelCase       :							KarrasVeSchedulerState







class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_     ):




			"""simple docstring"""







			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									return True



			@register_to_config
			def __init__(			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					float = 0.02       ,						lowerCAmelCase__  :					float = 100       ,						lowerCAmelCase__  :					float = 1.007       ,						lowerCAmelCase__  :					float = 80       ,						lowerCAmelCase__  :					float = 0.05       ,						lowerCAmelCase__  :					float = 50       ,						):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									return KarrasVeSchedulerState.create()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					KarrasVeSchedulerState       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					Tuple = ()):
									SCREAMING_SNAKE_CASE_:			Dict  		=	jnp.arange(0       ,						lowerCAmelCase__)[::-1].copy()
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=lowerCAmelCase__       ,						schedule=jnp.array(lowerCAmelCase__       ,						dtype=jnp.floataa)       ,						timesteps=lowerCAmelCase__       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					KarrasVeSchedulerState       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					random.KeyArray       ,						):
									if self.config.s_min <= sigma <= self.config.s_max:
															SCREAMING_SNAKE_CASE_:			Dict  		=	min(self.config.s_churn / state.num_inference_steps       ,						2**0.5 - 1)
									else:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	0

									# sample eps ~ N(0, S_noise^2 * I)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	random.split(lowerCAmelCase__       ,						num=1)
									SCREAMING_SNAKE_CASE_:			str  		=	self.config.s_noise * random.normal(key=lowerCAmelCase__       ,						shape=sample.shape)
									SCREAMING_SNAKE_CASE_:			Any  		=	sigma + gamma * sigma
									SCREAMING_SNAKE_CASE_:			Any  		=	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					KarrasVeSchedulerState       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					bool = True       ,						):
									SCREAMING_SNAKE_CASE_:			int  		=	sample_hat + sigma_hat * model_output
									SCREAMING_SNAKE_CASE_:			Tuple  		=	(sample_hat - pred_original_sample) / sigma_hat
									SCREAMING_SNAKE_CASE_:			int  		=	sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
															return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase__       ,						derivative=lowerCAmelCase__       ,						state=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					KarrasVeSchedulerState       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					jnp.ndarray       ,						lowerCAmelCase__  :					bool = True       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE_:			str  		=	(sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE_:			Tuple  		=	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
															return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase__       ,						derivative=lowerCAmelCase__       ,						state=lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					KarrasVeSchedulerState       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					str):
									raise NotImplementedError()









import doctest
from collections import deque

import numpy as np



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[1, 2, 3, 4]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Any  		=	len(self.first_signal)
									SCREAMING_SNAKE_CASE_:			Dict  		=	len(self.second_signal)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max(lowerCAmelCase__       ,						lowerCAmelCase__)

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[[0] * max_length for i in range(lowerCAmelCase__)]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
															self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
															self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	deque(self.second_signal)
															rotated_signal.rotate(lowerCAmelCase__)
															for j, item in enumerate(lowerCAmelCase__):
																					matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.matmul(np.transpose(lowerCAmelCase__)       ,						np.transpose(self.first_signal))

									# rounding-off to two decimal places
									return [round(lowerCAmelCase__       ,						2) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()






