



'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


snake_case_			:     Optional[int]  	=  logging.get_logger(__name__)






class 							lowercase__     (							lowercase       ):
	lowercase__      =      ["""pixel_values"""]





	def __init__(			self :    int			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    Dict[str, int] = None			,lowerCamelCase__ :    PILImageResampling = PIL.Image.BICUBIC			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    Dict[str, int] = None			,lowerCamelCase__ :    Union[int, float] = 1 / 255			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    Optional[Union[float, List[float]]] = None			,lowerCamelCase__ :    Optional[Union[float, List[float]]] = None			,**lowerCamelCase__ :    Union[str, Any]			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)
							_UpperCamelCase :		Dict			    =       size if size is not None else {'height': 256, 'width': 256}
							_UpperCamelCase :		int			    =       get_size_dict(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase :		int			    =       get_size_dict(lowerCamelCase__			,param_name='crop_size'			)

							_UpperCamelCase :		List[Any]			    =       do_resize
							_UpperCamelCase :		Any			    =       size
							_UpperCamelCase :		Optional[Any]			    =       resample
							_UpperCamelCase :		Tuple			    =       do_center_crop
							_UpperCamelCase :		str			    =       crop_size
							_UpperCamelCase :		Tuple			    =       do_rescale
							_UpperCamelCase :		Dict			    =       rescale_factor
							_UpperCamelCase :		str			    =       do_normalize
							_UpperCamelCase :		str			    =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_UpperCamelCase :		Dict			    =       image_std if image_std is not None else IMAGENET_STANDARD_STD





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    np.ndarray			,lowerCamelCase__ :    Dict[str, int]			,lowerCamelCase__ :    PILImageResampling = PIL.Image.BICUBIC			,lowerCamelCase__ :    Optional[Union[str, ChannelDimension]] = None			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       get_size_dict(lowerCamelCase__			)
							if "height" not in size or "width" not in size:
													raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
							return resize(
							    lowerCamelCase__			,size=(size['height'], size['width'])			,resample=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    np.ndarray			,lowerCamelCase__ :    Dict[str, int]			,lowerCamelCase__ :    Optional[Union[str, ChannelDimension]] = None			,**lowerCamelCase__ :    str			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       get_size_dict(lowerCamelCase__			)
							if "height" not in size or "width" not in size:
													raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
							return center_crop(lowerCamelCase__			,size=(size['height'], size['width'])			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    np.ndarray			,lowerCamelCase__ :    Union[int, float]			,lowerCamelCase__ :    Optional[Union[str, ChannelDimension]] = None			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							return rescale(lowerCamelCase__			,scale=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    np.ndarray			,lowerCamelCase__ :    Union[float, List[float]]			,lowerCamelCase__ :    Union[float, List[float]]			,lowerCamelCase__ :    Optional[Union[str, ChannelDimension]] = None			,**lowerCamelCase__ :    List[str]			,):







							'''simple docstring'''
							return normalize(lowerCamelCase__			,mean=lowerCamelCase__			,std=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    ImageInput			,lowerCamelCase__ :    bool = None			,lowerCamelCase__ :    Dict[str, int] = None			,lowerCamelCase__ :    Union[str, Any]=None			,lowerCamelCase__ :    bool = None			,lowerCamelCase__ :    Dict[str, int] = None			,lowerCamelCase__ :    bool = None			,lowerCamelCase__ :    float = None			,lowerCamelCase__ :    bool = None			,lowerCamelCase__ :    Optional[Union[float, List[float]]] = None			,lowerCamelCase__ :    Optional[Union[float, List[float]]] = None			,lowerCamelCase__ :    Optional[Union[str, TensorType]] = None			,lowerCamelCase__ :    ChannelDimension = ChannelDimension.FIRST			,**lowerCamelCase__ :    Dict			,):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase :		Dict			    =       resample if resample is not None else self.resample
							_UpperCamelCase :		str			    =       do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase :		Tuple			    =       do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase :		Optional[Any]			    =       rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase :		List[Any]			    =       do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase :		Optional[int]			    =       image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase :		Any			    =       image_std if image_std is not None else self.image_std

							_UpperCamelCase :		Tuple			    =       size if size is not None else self.size
							_UpperCamelCase :		Union[str, Any]			    =       get_size_dict(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase :		int			    =       get_size_dict(lowerCamelCase__			,param_name='crop_size'			)

							_UpperCamelCase :		Optional[int]			    =       make_list_of_images(lowerCamelCase__			)

							if not valid_images(lowerCamelCase__			):
													raise ValueError(
													    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
													    'torch.Tensor, tf.Tensor or jax.ndarray.'			)

							if do_resize and size is None or resample is None:
													raise ValueError('Size and resample must be specified if do_resize is True.'			)

							if do_center_crop and crop_size is None:
													raise ValueError('Crop size must be specified if do_center_crop is True.'			)

							if do_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

							# All transformations expect numpy arrays.
							_UpperCamelCase :		Optional[Any]			    =       [to_numpy_array(lowerCamelCase__			) for image in images]

							if do_resize:
													_UpperCamelCase :		List[Any]			    =       [self.resize(image=lowerCamelCase__			,size=lowerCamelCase__			,resample=lowerCamelCase__			) for image in images]

							if do_center_crop:
													_UpperCamelCase :		Optional[int]			    =       [self.center_crop(image=lowerCamelCase__			,size=lowerCamelCase__			) for image in images]

							if do_rescale:
													_UpperCamelCase :		Optional[Any]			    =       [self.rescale(image=lowerCamelCase__			,scale=lowerCamelCase__			) for image in images]

							if do_normalize:
													_UpperCamelCase :		List[str]			    =       [self.normalize(image=lowerCamelCase__			,mean=lowerCamelCase__			,std=lowerCamelCase__			) for image in images]

							_UpperCamelCase :		Dict			    =       [to_channel_dimension_format(lowerCamelCase__			,lowerCamelCase__			) for image in images]

							_UpperCamelCase :		Optional[int]			    =       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__			,tensor_type=lowerCamelCase__			)







'''simple docstring'''
from datetime import datetime

import requests






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[Any]			    =       'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
						_UpperCamelCase :		Optional[int]			    =       requests.get(base_url + url							).json()[0]['urls'][0]['src']
						return requests.get(UpperCAmelCase_							).content


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  input('Enter Video/IGTV url: ').strip()
					snake_case_			:     str  	=  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}.""")


