



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 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		torch.FloatTensor
						UpperCAmelCase			:		torch.FloatTensor
						UpperCAmelCase			:		Optional[torch.FloatTensor]		=		None


class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Tuple		=		2







						@register_to_config
						def __init__(  self    :				Optional[int]       ,	snake_case    :				float = 0.02       ,	snake_case    :				float = 100       ,	snake_case    :				float = 1.007       ,	snake_case    :				float = 80       ,	snake_case    :				float = 0.05       ,	snake_case    :				float = 50       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				sigma_max

											# setable values
											SCREAMING_SNAKE_CASE							:     int						=				None
											SCREAMING_SNAKE_CASE							:     np.IntTensor						=				None
											SCREAMING_SNAKE_CASE							:     torch.FloatTensor						=				None  # sigma(t_i)







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



											'''simple docstring'''


											return sample







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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				num_inference_steps
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.arange(0       ,	self.num_inference_steps	)[::-1].copy()
											SCREAMING_SNAKE_CASE							:     Any						=				torch.from_numpy(snake_case	).to(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[
											    (
											        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
											]
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.tensor(snake_case       ,	dtype=torch.floataa       ,	device=snake_case	)







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



											'''simple docstring'''


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

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE							:     Any						=				self.config.s_noise * randn_tensor(sample.shape       ,	generator=snake_case	).to(sample.device	)
											SCREAMING_SNAKE_CASE							:     int						=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				torch.FloatTensor       ,	snake_case    :				float       ,	snake_case    :				float       ,	snake_case    :				torch.FloatTensor       ,	snake_case    :				bool = True       ,	):



											'''simple docstring'''


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

											if not return_dict:
																return (sample_prev, derivative)

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







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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE							:     str						=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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=snake_case       ,	derivative=snake_case       ,	pred_original_sample=snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				List[str]       ,	snake_case    :				int       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											raise NotImplementedError()




from datetime import datetime

import requests

def        __a			(   __lowerCAmelCase   )     ->     bytes:
					SCREAMING_SNAKE_CASE							:     int						=				'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
					SCREAMING_SNAKE_CASE							:     Any						=				requests.get(base_url + url   ).json()[0]['urls'][0]['src']
					return requests.get(__lowerCAmelCase   ).content


if __name__ == "__main__":
			_lowerCamelCase    :		List[Any]		      =    input("""Enter Video/IGTV url: """).strip()
			_lowerCamelCase    :		int		      =    f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
			with open(file_name, """wb""") as fp:
						fp.write(download_video(url))
			print(f"""Done. Video saved to disk as {file_name}.""")