






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class     SCREAMING_SNAKE_CASE					(			SCREAMING_SNAKE_CASE_       ):





							"""simple docstring"""

							A_    	=       42
							A_    	=       42
							A_    	=       None





class     SCREAMING_SNAKE_CASE					(			SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_       ):





							"""simple docstring"""

							A_    	=       2

							@register_to_config
							def __init__(	self: Optional[int]		,  __A: float = 0.02		,  __A: float = 1_00		,  __A: float = 1.007		,  __A: float = 80		,  __A: float = 0.05		,  __A: float = 50		,  )     ->					List[str]:
												_A										=			sigma_max

												# setable values
												_A										=			None
												_A										=			None
												_A										=			None  # sigma(t_i)

							def    __A			(	self: Tuple		,  __A: torch.FloatTensor		,  __A: Optional[int] = None )     ->					Tuple:
												return sample

							def    __A			(	self: Union[str, Any]		,  __A: int		,  __A: Union[str, torch.device] = None )     ->					Any:
												_A										=			num_inference_steps
												_A										=			np.arange(0		,  self.num_inference_steps )[::-1].copy()
												_A										=			torch.from_numpy(__A ).to(__A )
												_A										=			[
												    (
												        self.config.sigma_max**2
												        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
												    )
												    for i in self.timesteps
												]
												_A										=			torch.tensor(__A		,  dtype=torch.floataa		,  device=__A )

							def    __A			(	self: int		,  __A: torch.FloatTensor		,  __A: float		,  __A: Optional[torch.Generator] = None )     ->					Optional[Any]:


												if self.config.s_min <= sigma <= self.config.s_max:
																	_A										=			min(self.config.s_churn / self.num_inference_steps		,  2**0.5 - 1 )
												else:
																	_A										=			0

												# sample eps ~ N(0, S_noise^2 * I)
												_A										=			self.config.s_noise * randn_tensor(sample.shape		,  generator=__A ).to(sample.device )
												_A										=			sigma + gamma * sigma
												_A										=			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat

							def    __A			(	self: Tuple		,  __A: torch.FloatTensor		,  __A: float		,  __A: float		,  __A: torch.FloatTensor		,  __A: bool = True		,  )     ->					Any:
												_A										=			sample_hat + sigma_hat * model_output
												_A										=			(sample_hat - pred_original_sample) / sigma_hat
												_A										=			sample_hat + (sigma_prev - sigma_hat) * derivative

												if not return_dict:
																	return (sample_prev, derivative)

												return KarrasVeOutput(
												    prev_sample=__A		,  derivative=__A		,  pred_original_sample=__A )

							def    __A			(	self: Optional[int]		,  __A: torch.FloatTensor		,  __A: float		,  __A: float		,  __A: torch.FloatTensor		,  __A: torch.FloatTensor		,  __A: torch.FloatTensor		,  __A: bool = True		,  )     ->					int:
												_A										=			sample_prev + sigma_prev * model_output
												_A										=			(sample_prev - pred_original_sample) / sigma_prev
												_A										=			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

												if not return_dict:
																	return (sample_prev, derivative)

												return KarrasVeOutput(
												    prev_sample=__A		,  derivative=__A		,  pred_original_sample=__A )



							def    __A			(	self: Optional[int]		,  __A: List[str]		,  __A: int		,  __A: Optional[int] )     ->					List[Any]:
												raise NotImplementedError()








from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp