

from ...processing_utils import ProcessorMixin



class    _a						(lowerCamelCase_    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	['''image_processor''', '''feature_extractor''']
					UpperCAmelCase__:       Any  							=	'''TvltImageProcessor'''
					UpperCAmelCase__:       Union[str, Any]  							=	'''TvltFeatureExtractor'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(image_processor=__snake_case   ,	feature_extractor=__snake_case      )

						A__							:   Optional[Any]					       =		image_processor
						A__							:   Optional[int]					       =		feature_extractor






					def __call__(			self   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__=False   ,	A__=False   ,	*A__   ,	**A__   ,	):

						if images is None and audio is None:
							raise ValueError("""You need to specify either an `images` or `audio` input to process."""      )

						A__							:   int					       =		None
						if images is not None:
							A__							:   Union[str, Any]					       =		self.image_processor(__snake_case   ,	mask_pixel=__snake_case   ,	*__snake_case   ,	**__snake_case      )
						if images_mixed is not None:
							A__							:   Tuple					       =		self.image_processor(__snake_case   ,	is_mixed=__snake_case   ,	*__snake_case   ,	**__snake_case      )
						if audio is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor(
							    __snake_case   ,	*__snake_case   ,	sampling_rate=__snake_case   ,	mask_audio=__snake_case   ,	**__snake_case      )

						A__							:   Any					       =		{}
						if audio is not None:
							output_dict.update(__snake_case      )
						if images is not None:
							output_dict.update(__snake_case      )
						if images_mixed_dict is not None:
							output_dict.update(__snake_case      )
						return output_dict






					@property
					def   __A      (			self      ):
						A__							:   Dict					       =		self.image_processor.model_input_names
						A__							:   int					       =		self.feature_extractor.model_input_names
						return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )






import requests

A_	:			List[Any]  	=      'YOUR API KEY'

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str = giphy_api_key )					->				list:
	A__							:   Dict					       =		"""+""".join(query.split() )
	A__							:   Optional[int]					       =		f"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
	A__							:   Any					       =		requests.get(lowercase_ ).json()["""data"""]
	return [gif["url"] for gif in gifs]


if __name__ == "__main__":
	print('\n'.join(get_gifs('space ship')))



