


from datetime import datetime

import requests







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str    )     ->		bytes:

       """simple docstring"""

       a_ :		Tuple							=  'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
       a_ :		Optional[int]							=  requests.get(base_url + url    ).json()[0]['urls'][0]['src']
       return requests.get(__A    ).content


if __name__ == "__main__":
   UpperCAmelCase_				:			Dict   =	input('Enter Video/IGTV url: ').strip()
   UpperCAmelCase_				:			Tuple   =	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}.')






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_				:			Union[str, Any]   =	{
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Optional[Any]   =	['WhisperTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'WhisperForConditionalGeneration',
       'WhisperModel',
       'WhisperPreTrainedModel',
       'WhisperForAudioClassification',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFWhisperForConditionalGeneration',
       'TFWhisperModel',
       'TFWhisperPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Any   =	[
       'FlaxWhisperForConditionalGeneration',
       'FlaxWhisperModel',
       'FlaxWhisperPreTrainedModel',
       'FlaxWhisperForAudioClassification',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_				:			int   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


