



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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
					import PIL

# soft dependency
if is_pytesseract_available():
					import pytesseract

snake_case_			:     int  	=  logging.get_logger(__name__)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						return [
						    int(1_0_0_0 * (box[0] / width)							),
						    int(1_0_0_0 * (box[1] / height)							),
						    int(1_0_0_0 * (box[2] / width)							),
						    int(1_0_0_0 * (box[3] / height)							),
						]






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_ = None							):
						_UpperCamelCase :		str			    =       tesseract_config if tesseract_config is not None else ''''''

						# apply OCR
						_UpperCamelCase :		Any			    =       to_pil_image(__lowerCAmelCase							)
						_UpperCamelCase :		Optional[Any]			    =       pil_image.size
						_UpperCamelCase :		Any			    =       pytesseract.image_to_data(__lowerCAmelCase  ,  lang=__lowerCAmelCase  ,  output_type='dict'  ,  config=__lowerCAmelCase							)
						_UpperCamelCase :		Optional[int]			    =       data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

						# filter empty words and corresponding coordinates
						_UpperCamelCase :		Dict			    =       [idx for idx, word in enumerate(__lowerCAmelCase							) if not word.strip()]
						_UpperCamelCase :		List[Any]			    =       [word for idx, word in enumerate(__lowerCAmelCase							) if idx not in irrelevant_indices]
						_UpperCamelCase :		str			    =       [coord for idx, coord in enumerate(__lowerCAmelCase							) if idx not in irrelevant_indices]
						_UpperCamelCase :		Optional[int]			    =       [coord for idx, coord in enumerate(__lowerCAmelCase							) if idx not in irrelevant_indices]
						_UpperCamelCase :		List[Any]			    =       [coord for idx, coord in enumerate(__lowerCAmelCase							) if idx not in irrelevant_indices]
						_UpperCamelCase :		Optional[int]			    =       [coord for idx, coord in enumerate(__lowerCAmelCase							) if idx not in irrelevant_indices]

						# turn coordinates into (left, top, left+width, top+height) format
						_UpperCamelCase :		Any			    =       []
						for x, y, w, h in zip(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase							):
												_UpperCamelCase :		List[Any]			    =       [x, y, x + w, y + h]
												actual_boxes.append(__lowerCAmelCase							)

						# finally, normalize the bounding boxes
						_UpperCamelCase :		int			    =       []
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase							)							)

						assert len(__lowerCAmelCase							) == len(__lowerCAmelCase							), "Not as many words as there are bounding boxes"

						return words, normalized_boxes






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





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    Dict[str, int] = None			,lowerCamelCase__ :    PILImageResampling = PILImageResampling.BILINEAR			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    Optional[str] = None			,lowerCamelCase__ :    Optional[str] = ""			,**lowerCamelCase__ :    Any			,):







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

							_UpperCamelCase :		Union[str, Any]			    =       do_resize
							_UpperCamelCase :		Union[str, Any]			    =       size
							_UpperCamelCase :		Dict			    =       resample
							_UpperCamelCase :		Optional[Any]			    =       apply_ocr
							_UpperCamelCase :		Optional[int]			    =       ocr_lang
							_UpperCamelCase :		int			    =       tesseract_config





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







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





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







							'''simple docstring'''
							_UpperCamelCase :		str			    =       do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase :		Dict			    =       size if size is not None else self.size
							_UpperCamelCase :		str			    =       get_size_dict(__lowercase			)
							_UpperCamelCase :		Optional[Any]			    =       resample if resample is not None else self.resample
							_UpperCamelCase :		str			    =       apply_ocr if apply_ocr is not None else self.apply_ocr
							_UpperCamelCase :		Optional[Any]			    =       ocr_lang if ocr_lang is not None else self.ocr_lang
							_UpperCamelCase :		Any			    =       tesseract_config if tesseract_config is not None else self.tesseract_config

							_UpperCamelCase :		Tuple			    =       make_list_of_images(__lowercase			)

							if not valid_images(__lowercase			):
													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.'			)

							# All transformations expect numpy arrays.
							_UpperCamelCase :		Dict			    =       [to_numpy_array(__lowercase			) for image in images]

							if apply_ocr:
													requires_backends(self			,'pytesseract'			)
													_UpperCamelCase :		Optional[int]			    =       []
													_UpperCamelCase :		List[str]			    =       []
													for image in images:
																			_UpperCamelCase :		Dict			    =       apply_tesseract(__lowercase			,__lowercase			,__lowercase			)
																			words_batch.append(__lowercase			)
																			boxes_batch.append(__lowercase			)

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							_UpperCamelCase :		str			    =       [flip_channel_order(__lowercase			) for image in images]
							_UpperCamelCase :		List[Any]			    =       [to_channel_dimension_format(__lowercase			,__lowercase			) for image in images]

							_UpperCamelCase :		str			    =       BatchFeature(data={'pixel_values': images}			,tensor_type=__lowercase			)

							if apply_ocr:
													_UpperCamelCase :		Dict			    =       words_batch
													_UpperCamelCase :		Any			    =       boxes_batch
							return data







'''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}.""")


