





"""simple docstring"""




import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Dict      					=["""image_processor""", """tokenizer"""]
							lowerCamelCase   :					Optional[int]      					="""BlipImageProcessor"""
							lowerCamelCase   :					List[Any]      					="""AutoTokenizer"""






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> str:
								super().__init__(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# add QFormer tokenizer
								a      :		Any	=				qformer_tokenizer






							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> BatchFeature:
								if images is None and text is None:
									raise ValueError("You have to specify at least images or text."      )

								a      :		Dict	=				BatchFeature()

								if text is not None:
									a      :		Dict	=				self.tokenizer(
									    text=lowerCAmelCase__      ,					add_special_tokens=lowerCAmelCase__      ,					padding=lowerCAmelCase__      ,					truncation=lowerCAmelCase__      ,					max_length=lowerCAmelCase__      ,					stride=lowerCAmelCase__      ,					pad_to_multiple_of=lowerCAmelCase__      ,					return_attention_mask=lowerCAmelCase__      ,					return_overflowing_tokens=lowerCAmelCase__      ,					return_special_tokens_mask=lowerCAmelCase__      ,					return_offsets_mapping=lowerCAmelCase__      ,					return_token_type_ids=lowerCAmelCase__      ,					return_length=lowerCAmelCase__      ,					verbose=lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)
									encoding.update(lowerCAmelCase__      )
									a      :		Optional[int]	=				self.qformer_tokenizer(
									    text=lowerCAmelCase__      ,					add_special_tokens=lowerCAmelCase__      ,					padding=lowerCAmelCase__      ,					truncation=lowerCAmelCase__      ,					max_length=lowerCAmelCase__      ,					stride=lowerCAmelCase__      ,					pad_to_multiple_of=lowerCAmelCase__      ,					return_attention_mask=lowerCAmelCase__      ,					return_overflowing_tokens=lowerCAmelCase__      ,					return_special_tokens_mask=lowerCAmelCase__      ,					return_offsets_mapping=lowerCAmelCase__      ,					return_token_type_ids=lowerCAmelCase__      ,					return_length=lowerCAmelCase__      ,					verbose=lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)
									a      :		str	=				qformer_text_encoding.pop("input_ids"      )
									a      :		Union[str, Any]	=				qformer_text_encoding.pop("attention_mask"      )

								if images is not None:
									a      :		List[Any]	=				self.image_processor(lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      )
									encoding.update(lowerCAmelCase__      )

								return encoding






							def     __a   (   self      ,					*lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> Dict:
								return self.tokenizer.batch_decode(*lowerCAmelCase__      ,					**lowerCAmelCase__      )






							def     __a   (   self      ,					*lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> Optional[int]:
								return self.tokenizer.decode(*lowerCAmelCase__      ,					**lowerCAmelCase__      )






							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def     __a   (   self      )	-> Dict:
								a      :		Union[str, Any]	=				self.tokenizer.model_input_names
								a      :		List[Any]	=				self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> Union[str, Any]:
								if os.path.isfile(lowerCAmelCase__      ):
									raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""      )
								os.makedirs(lowerCAmelCase__      ,					exist_ok=lowerCAmelCase__      )
								a      :		Optional[Any]	=				os.path.join(lowerCAmelCase__      ,					"qformer_tokenizer"      )
								self.qformer_tokenizer.save_pretrained(lowerCAmelCase__      )
								return super().save_pretrained(lowerCAmelCase__      ,					**lowerCAmelCase__      )






							@classmethod
							def     __a   (   cls      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> List[str]:
								a      :		Optional[Any]	=				AutoTokenizer.from_pretrained(lowerCAmelCase__      ,					subfolder="qformer_tokenizer"      )
								a      :		Optional[int]	=				cls._get_arguments_from_pretrained(lowerCAmelCase__      ,					**lowerCAmelCase__      )
								args.append(lowerCAmelCase__      )
								return cls(*lowerCAmelCase__      )









"""simple docstring"""




from datetime import datetime

import requests






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->bytes:






	'''simple docstring'''






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


if __name__ == "__main__":
		a						: str     	=			input('''Enter Video/IGTV url: ''').strip()
		a						: 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}.''')


