






"""simple docstring"""

import copy

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


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       {
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''deformable_detr'''
				UpperCAmelCase__        =				{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, Any]=True						,    __lowerCamelCase							:						str=None						,    __lowerCamelCase							:						List[Any]=3						,    __lowerCamelCase							:						Any=3_0_0						,    __lowerCamelCase							:						Union[str, Any]=1_0_2_4						,    __lowerCamelCase							:						int=6						,    __lowerCamelCase							:						List[str]=1_0_2_4						,    __lowerCamelCase							:						Tuple=8						,    __lowerCamelCase							:						Tuple=6						,    __lowerCamelCase							:						Tuple=1_0_2_4						,    __lowerCamelCase							:						Optional[Any]=8						,    __lowerCamelCase							:						Any=0.0						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						Union[str, Any]="relu"						,    __lowerCamelCase							:						Optional[int]=2_5_6						,    __lowerCamelCase							:						Dict=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.0						,    __lowerCamelCase							:						Optional[int]=0.0						,    __lowerCamelCase							:						str=0.02						,    __lowerCamelCase							:						Dict=1.0						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Union[str, Any]=False						,    __lowerCamelCase							:						Union[str, Any]="sine"						,    __lowerCamelCase							:						str="resnet50"						,    __lowerCamelCase							:						List[Any]=True						,    __lowerCamelCase							:						Any=False						,    __lowerCamelCase							:						Any=4						,    __lowerCamelCase							:						List[str]=4						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						List[str]=False						,    __lowerCamelCase							:						Tuple=3_0_0						,    __lowerCamelCase							:						Union[str, Any]=False						,    __lowerCamelCase							:						Any=1						,    __lowerCamelCase							:						Optional[int]=5						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						int=1						,    __lowerCamelCase							:						Union[str, Any]=1						,    __lowerCamelCase							:						Dict=5						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						int=0.1						,    __lowerCamelCase							:						Optional[Any]=0.25						,    __lowerCamelCase							:						Union[str, Any]=False						,    **__lowerCamelCase							:						int						,    )				->					Optional[int]:
										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."	)
																						__magic_name__            =     CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
																elif isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																						__magic_name__            =     backbone_config.get("model_type"	)
																						__magic_name__            =     CONFIG_MAPPING[backbone_model_type]
																						__magic_name__            =     config_class.from_dict(__lowerCamelCase	)
										__magic_name__            =     use_timm_backbone
										__magic_name__            =     backbone_config
										__magic_name__            =     num_channels
										__magic_name__            =     num_queries
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     d_model
										__magic_name__            =     encoder_ffn_dim
										__magic_name__            =     encoder_layers
										__magic_name__            =     encoder_attention_heads
										__magic_name__            =     decoder_ffn_dim
										__magic_name__            =     decoder_layers
										__magic_name__            =     decoder_attention_heads
										__magic_name__            =     dropout
										__magic_name__            =     attention_dropout
										__magic_name__            =     activation_dropout
										__magic_name__            =     activation_function
										__magic_name__            =     init_std
										__magic_name__            =     init_xavier_std
										__magic_name__            =     encoder_layerdrop
										__magic_name__            =     auxiliary_loss
										__magic_name__            =     position_embedding_type
										__magic_name__            =     backbone
										__magic_name__            =     use_pretrained_backbone
										__magic_name__            =     dilation
										# deformable attributes
										__magic_name__            =     num_feature_levels
										__magic_name__            =     encoder_n_points
										__magic_name__            =     decoder_n_points
										__magic_name__            =     two_stage
										__magic_name__            =     two_stage_num_proposals
										__magic_name__            =     with_box_refine
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True."	)
										# Hungarian matcher
										__magic_name__            =     class_cost
										__magic_name__            =     bbox_cost
										__magic_name__            =     giou_cost
										# Loss coefficients
										__magic_name__            =     mask_loss_coefficient
										__magic_name__            =     dice_loss_coefficient
										__magic_name__            =     bbox_loss_coefficient
										__magic_name__            =     giou_loss_coefficient
										__magic_name__            =     eos_coefficient
										__magic_name__            =     focal_alpha
										__magic_name__            =     disable_custom_kernels
										super().__init__(is_encoder_decoder=__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				def     _snake_case					(							self							:						List[Any]	)				->					int:
										return self.encoder_attention_heads







				@property
				def     _snake_case					(							self							:						str	)				->					int:
										return self.d_model







				def     _snake_case					(							self							:						List[str]	)				->					int:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										if self.backbone_config is not None:
																__magic_name__            =     self.backbone_config.to_dict()
										__magic_name__            =     self.__class__.model_type
										return output














"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowercase			,      lowercase    			=       parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowercase    			=       args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



