

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_	:			Optional[Any]  	=      logging.get_logger(__name__)

A_	:			Optional[int]  	=      {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	'''conditional_detr'''
					UpperCAmelCase__:       Union[str, Any]  							=	['''past_key_values''']
					UpperCAmelCase__:       Dict  							=	{
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}






					def __init__(			self   ,	A__=True   ,	A__=None   ,	A__=3   ,	A__=300   ,	A__=6   ,	A__=2048   ,	A__=8   ,	A__=6   ,	A__=2048   ,	A__=8   ,	A__=0.0   ,	A__=0.0   ,	A__=True   ,	A__="relu"   ,	A__=256   ,	A__=0.1   ,	A__=0.0   ,	A__=0.0   ,	A__=0.0_2   ,	A__=1.0   ,	A__=False   ,	A__="sine"   ,	A__="resnet50"   ,	A__=True   ,	A__=False   ,	A__=2   ,	A__=5   ,	A__=2   ,	A__=1   ,	A__=1   ,	A__=2   ,	A__=5   ,	A__=2   ,	A__=0.2_5   ,	**A__   ,	):
						if backbone_config is not None and use_timm_backbone:
							raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""      )

						if not use_timm_backbone:
							if backbone_config is None:
								logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""      )
								A__							:   Optional[Any]					       =		CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
							elif isinstance(A__   ,	A__      ):
								A__							:   Optional[int]					       =		backbone_config.get("""model_type"""      )
								A__							:   Any					       =		CONFIG_MAPPING[backbone_model_type]
								A__							:   List[Any]					       =		config_class.from_dict(A__      )

						A__							:   Dict					       =		use_timm_backbone
						A__							:   str					       =		backbone_config
						A__							:   List[str]					       =		num_channels
						A__							:   Optional[int]					       =		num_queries
						A__							:   Union[str, Any]					       =		d_model
						A__							:   str					       =		encoder_ffn_dim
						A__							:   Tuple					       =		encoder_layers
						A__							:   Optional[Any]					       =		encoder_attention_heads
						A__							:   Any					       =		decoder_ffn_dim
						A__							:   Tuple					       =		decoder_layers
						A__							:   Optional[Any]					       =		decoder_attention_heads
						A__							:   List[str]					       =		dropout
						A__							:   List[Any]					       =		attention_dropout
						A__							:   Optional[Any]					       =		activation_dropout
						A__							:   Union[str, Any]					       =		activation_function
						A__							:   int					       =		init_std
						A__							:   int					       =		init_xavier_std
						A__							:   Tuple					       =		encoder_layerdrop
						A__							:   Optional[Any]					       =		decoder_layerdrop
						A__							:   str					       =		encoder_layers
						A__							:   List[Any]					       =		auxiliary_loss
						A__							:   str					       =		position_embedding_type
						A__							:   int					       =		backbone
						A__							:   List[str]					       =		use_pretrained_backbone
						A__							:   Tuple					       =		dilation
						# Hungarian matcher
						A__							:   Dict					       =		class_cost
						A__							:   Union[str, Any]					       =		bbox_cost
						A__							:   Any					       =		giou_cost
						# Loss coefficients
						A__							:   List[Any]					       =		mask_loss_coefficient
						A__							:   Union[str, Any]					       =		dice_loss_coefficient
						A__							:   List[Any]					       =		cls_loss_coefficient
						A__							:   int					       =		bbox_loss_coefficient
						A__							:   Any					       =		giou_loss_coefficient
						A__							:   Union[str, Any]					       =		focal_alpha
						super().__init__(is_encoder_decoder=A__   ,	**A__      )






					@property
					def   __A      (			self      ):
						return self.encoder_attention_heads






					@property
					def   __A      (			self      ):
						return self.d_model






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		copy.deepcopy(self.__dict__      )
						if self.backbone_config is not None:
							A__							:   Any					       =		self.backbone_config.to_dict()
						A__							:   List[Any]					       =		self.__class__.model_type
						return output



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Union[str, Any]  							=	version.parse('''1.11'''    )






					@property
					def   __A      (			self      ):
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						        ("""pixel_mask""", {0: """batch"""}),
						    ]      )






					@property
					def   __A      (			self      ):
						return 1e-5






					@property
					def   __A      (			self      ):
						return 12






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')))



