


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__    :							Union[str, Any]            =					{'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Dict            =					['''FNetTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							List[str]            =					['''FNetTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FNetForMaskedLM''',
		    '''FNetForMultipleChoice''',
		    '''FNetForNextSentencePrediction''',
		    '''FNetForPreTraining''',
		    '''FNetForQuestionAnswering''',
		    '''FNetForSequenceClassification''',
		    '''FNetForTokenClassification''',
		    '''FNetLayer''',
		    '''FNetModel''',
		    '''FNetPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_fnet import FNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_fnet_fast import FNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fnet import (
				    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FNetForMaskedLM,
				    FNetForMultipleChoice,
				    FNetForNextSentencePrediction,
				    FNetForPreTraining,
				    FNetForQuestionAnswering,
				    FNetForSequenceClassification,
				    FNetForTokenClassification,
				    FNetLayer,
				    FNetModel,
				    FNetPreTrainedModel,
				)


else:
		import sys

		a__    :							List[str]            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""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}.")
