
"""simple docstring"""


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     A_	(     __lowercase   ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	      =   42
							SCREAMING_SNAKE_CASE_	      =   42
							SCREAMING_SNAKE_CASE_	      =   None






class     A_	(     __lowercase      ,					__lowercase   ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	      =   2





							@register_to_config
							def __init__(  self  :Optional[Any]     ,							lowerCamelCase_  :float = 0.02     ,							lowerCamelCase_  :float = 100     ,							lowerCamelCase_  :float = 1.0_07     ,							lowerCamelCase_  :float = 80     ,							lowerCamelCase_  :float = 0.05     ,							lowerCamelCase_  :float = 50     ,							):





										"""simple docstring"""

										lowerCamelCase__	:     List[str]              =sigma_max

										# setable values
										lowerCamelCase__	:     Tuple              =None
										lowerCamelCase__	:     Optional[int]              =None
										lowerCamelCase__	:     int              =None  # sigma(t_i)





							def       UpperCAmelCase__							(  self  :Dict     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :Optional[int] = None       ):





										"""simple docstring"""

										return sample





							def       UpperCAmelCase__							(  self  :List[str]     ,							lowerCamelCase_  :int     ,							lowerCamelCase_  :Union[str, torch.device] = None       ):





										"""simple docstring"""

										lowerCamelCase__	:     List[Any]              =num_inference_steps
										lowerCamelCase__	:     List[str]              =np.arange(0     ,							self.num_inference_steps       )[::-1].copy()
										lowerCamelCase__	:     Optional[Any]              =torch.from_numpy(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
										lowerCamelCase__	:     str              =[
										    (
										        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
										]
										lowerCamelCase__	:     Union[str, Any]              =torch.tensor(SCREAMING_SNAKE_CASE__     ,							dtype=torch.floataa     ,							device=SCREAMING_SNAKE_CASE__       )





							def       UpperCAmelCase__							(  self  :Tuple     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :float     ,							lowerCamelCase_  :Optional[torch.Generator] = None       ):





										"""simple docstring"""

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

										# sample eps ~ N(0, S_noise^2 * I)
										lowerCamelCase__	:     Union[str, Any]              =self.config.s_noise * randn_tensor(sample.shape     ,							generator=SCREAMING_SNAKE_CASE__       ).to(sample.device       )
										lowerCamelCase__	:     Any              =sigma + gamma * sigma
										lowerCamelCase__	:     Tuple              =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

										return sample_hat, sigma_hat





							def       UpperCAmelCase__							(  self  :Union[str, Any]     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :float     ,							lowerCamelCase_  :float     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :bool = True     ,							):





										"""simple docstring"""

										lowerCamelCase__	:     str              =sample_hat + sigma_hat * model_output
										lowerCamelCase__	:     Optional[int]              =(sample_hat - pred_original_sample) / sigma_hat
										lowerCamelCase__	:     int              =sample_hat + (sigma_prev - sigma_hat) * derivative

										if not return_dict:
													return (sample_prev, derivative)

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





							def       UpperCAmelCase__							(  self  :Optional[Any]     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :float     ,							lowerCamelCase_  :float     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :torch.FloatTensor     ,							lowerCamelCase_  :bool = True     ,							):





										"""simple docstring"""

										lowerCamelCase__	:     List[Any]              =sample_prev + sigma_prev * model_output
										lowerCamelCase__	:     Tuple              =(sample_prev - pred_original_sample) / sigma_prev
										lowerCamelCase__	:     int              =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=SCREAMING_SNAKE_CASE__     ,							derivative=SCREAMING_SNAKE_CASE__     ,							pred_original_sample=SCREAMING_SNAKE_CASE__       )





							def       UpperCAmelCase__							(  self  :Optional[Any]     ,							lowerCamelCase_  :Optional[Any]     ,							lowerCamelCase_  :Optional[int]     ,							lowerCamelCase_  :List[Any]       ):





										"""simple docstring"""

										raise NotImplementedError()

import pprint

import requests

SCREAMING_SNAKE_CASE__   :			str												=    "https://zenquotes.io/api"




def  __magic_name__ (   )			->   list:
				return requests.get(API_ENDPOINT_URL + '''/today'''							).json()




def  __magic_name__ (   )			->   list:
				return requests.get(API_ENDPOINT_URL + '''/random'''							).json()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			int												=    random_quotes()
					pprint.pprint(response)



