


"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a__    :							Tuple            =					logging.get_logger(__name__)


if is_vision_available():
		import PIL




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Tuple    =      ["pixel_values"]




	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BICUBIC      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Union[int, float] = 1 / 2_5_5      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    bool = True      ,      **UpperCAmelCase__      :    str      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__      ,      param_name="crop_size"       )

						__SCREAMING_SNAKE_CASE						       =    do_resize
						__SCREAMING_SNAKE_CASE						       =    size
						__SCREAMING_SNAKE_CASE						       =    resample
						__SCREAMING_SNAKE_CASE						       =    do_center_crop
						__SCREAMING_SNAKE_CASE						       =    crop_size
						__SCREAMING_SNAKE_CASE						       =    do_rescale
						__SCREAMING_SNAKE_CASE						       =    rescale_factor
						__SCREAMING_SNAKE_CASE						       =    do_normalize
						__SCREAMING_SNAKE_CASE						       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						__SCREAMING_SNAKE_CASE						       =    image_std if image_std is not None else OPENAI_CLIP_STD
						__SCREAMING_SNAKE_CASE						       =    do_convert_rgb




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BICUBIC      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    str      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						if "shortest_edge" not in size:
											raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
						__SCREAMING_SNAKE_CASE						       =    get_resize_output_image_size(UpperCAmelCase__      ,      size=size["shortest_edge"]      ,      default_to_square=UpperCAmelCase__       )
						return resize(UpperCAmelCase__      ,      size=UpperCAmelCase__      ,      resample=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Optional[int]      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
						if "height" not in size or "width" not in size:
											raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
						return center_crop(UpperCAmelCase__      ,      size=(size["height"], size["width"])      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[int, float]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    List[Any]      ,      )	->     Dict:
						return rescale(UpperCAmelCase__      ,      scale=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[float, List[float]]      ,      UpperCAmelCase__      :    Union[float, List[float]]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Any      ,      )	->     np.ndarray:
						return normalize(UpperCAmelCase__      ,      mean=UpperCAmelCase__      ,      std=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    ImageInput      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    int = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    float = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[Union[str, TensorType]] = None      ,      UpperCAmelCase__      :    Optional[ChannelDimension] = ChannelDimension.FIRST      ,      **UpperCAmelCase__      :    str      ,      )	->     PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						       =    do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						       =    size if size is not None else self.size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="size"      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						       =    do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="crop_size"      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE						       =    do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE						       =    image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE						       =    image_std if image_std is not None else self.image_std
						__SCREAMING_SNAKE_CASE						       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						__SCREAMING_SNAKE_CASE						       =    make_list_of_images(UpperCAmelCase__       )

						if not valid_images(UpperCAmelCase__       ):
											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:
											raise ValueError("Size 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."       )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
											__SCREAMING_SNAKE_CASE						       =    [convert_to_rgb(UpperCAmelCase__       ) for image in images]

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE						       =    [to_numpy_array(UpperCAmelCase__       ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE						       =    [self.resize(image=UpperCAmelCase__      ,      size=UpperCAmelCase__      ,      resample=UpperCAmelCase__       ) for image in images]

						if do_center_crop:
											__SCREAMING_SNAKE_CASE						       =    [self.center_crop(image=UpperCAmelCase__      ,      size=UpperCAmelCase__       ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE						       =    [self.rescale(image=UpperCAmelCase__      ,      scale=UpperCAmelCase__       ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE						       =    [self.normalize(image=UpperCAmelCase__      ,      mean=UpperCAmelCase__      ,      std=UpperCAmelCase__       ) for image in images]

						__SCREAMING_SNAKE_CASE						       =    [to_channel_dimension_format(UpperCAmelCase__      ,      UpperCAmelCase__       ) for image in images]

						__SCREAMING_SNAKE_CASE						       =    {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__      ,      tensor_type=UpperCAmelCase__       )




"""simple docstring"""







from datetime import datetime

import requests




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
					__SCREAMING_SNAKE_CASE						       =    requests.get(base_url + url       ).json()[0]["urls"][0]["src"]
					return requests.get(lowerCAmelCase_       ).content


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