

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_	:			str  	=      logging.get_logger(__name__)

A_	:			List[str]  	=      {
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	'''donut-swin'''

					UpperCAmelCase__:       Tuple  							=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(			self   ,	A__=224   ,	A__=4   ,	A__=3   ,	A__=96   ,	A__=[2, 2, 6, 2]   ,	A__=[3, 6, 12, 24]   ,	A__=7   ,	A__=4.0   ,	A__=True   ,	A__=0.0   ,	A__=0.0   ,	A__=0.1   ,	A__="gelu"   ,	A__=False   ,	A__=0.0_2   ,	A__=1e-5   ,	**A__   ,	):
						super().__init__(**A__      )

						A__							:   Any					       =		image_size
						A__							:   Optional[Any]					       =		patch_size
						A__							:   List[Any]					       =		num_channels
						A__							:   Optional[Any]					       =		embed_dim
						A__							:   str					       =		depths
						A__							:   Tuple					       =		len(A__      )
						A__							:   Optional[int]					       =		num_heads
						A__							:   Union[str, Any]					       =		window_size
						A__							:   Optional[Any]					       =		mlp_ratio
						A__							:   int					       =		qkv_bias
						A__							:   List[Any]					       =		hidden_dropout_prob
						A__							:   Dict					       =		attention_probs_dropout_prob
						A__							:   Dict					       =		drop_path_rate
						A__							:   Dict					       =		hidden_act
						A__							:   Dict					       =		use_absolute_embeddings
						A__							:   int					       =		layer_norm_eps
						A__							:   Any					       =		initializer_range
						# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						A__							:   Union[str, Any]					       =		int(embed_dim * 2 ** (len(A__      ) - 1)      )






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')))



