






"""simple docstring"""
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCamelCase_       			=   {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCamelCase_       			=   logging.get_logger(__name__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''maskformer'''
	__magic_name__			   =       {'''hidden_size''': '''mask_feature_size'''}
	__magic_name__			   =       ['''resnet''', '''swin''']
	__magic_name__			   =       ['''detr''']





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int = 256			,			lowerCAmelCase_      :				int = 256			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				bool = False			,			lowerCAmelCase_      :				Optional[Dict] = None			,			lowerCAmelCase_      :				Optional[Dict] = None			,			lowerCAmelCase_      :				float = 0.0_2			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 20.0			,			lowerCAmelCase_      :				Optional[bool] = None			,			**lowerCAmelCase_      :				Optional[Any]			,			)		->    Optional[int]:
				if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							UpperCAmelCase_  :				int      =      SwinConfig(
							    image_size=384			,			in_channels=3			,			patch_size=4			,			embed_dim=128			,			depths=[2, 2, 18, 2]			,			num_heads=[4, 8, 16, 32]			,			window_size=12			,			drop_path_rate=0.3			,			out_features=["stage1", "stage2", "stage3", "stage4"]			,			)

				if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				Tuple      =      backbone_config.pop("model_type"		)
							UpperCAmelCase_  :				Any      =      CONFIG_MAPPING[backbone_model_type]
							UpperCAmelCase_  :				Dict      =      config_class.from_dict(lowerCAmelCase_		)

				# verify that the backbone is supported
				if backbone_config.model_type not in self.backbones_supported:
							logger.warning_once(
							    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
							    f"""Supported model types: {','.join(self.backbones_supported		)}"""		)

				if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							UpperCAmelCase_  :				List[str]      =      DetrConfig()
				else:
							# verify that the decoder is supported
							UpperCAmelCase_  :				Any      =      (
							    decoder_config.pop("model_type"		) if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
										raise ValueError(
										    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
										    f""" {','.join(self.decoders_supported		)}"""		)
							if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
										UpperCAmelCase_  :				Tuple      =      CONFIG_MAPPING[decoder_type]
										UpperCAmelCase_  :				Any      =      config_class.from_dict(lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      backbone_config
				UpperCAmelCase_  :				Any      =      decoder_config
				# main feature dimension for the model
				UpperCAmelCase_  :				List[Any]      =      fpn_feature_size
				UpperCAmelCase_  :				Union[str, Any]      =      mask_feature_size
				# initializer
				UpperCAmelCase_  :				Dict      =      init_std
				UpperCAmelCase_  :				Optional[Any]      =      init_xavier_std
				# Hungarian matcher && loss
				UpperCAmelCase_  :				int      =      cross_entropy_weight
				UpperCAmelCase_  :				Any      =      dice_weight
				UpperCAmelCase_  :				Any      =      mask_weight
				UpperCAmelCase_  :				List[str]      =      use_auxiliary_loss
				UpperCAmelCase_  :				str      =      no_object_weight
				UpperCAmelCase_  :				Optional[Any]      =      output_auxiliary_logits

				UpperCAmelCase_  :				Any      =      self.decoder_config.encoder_attention_heads
				UpperCAmelCase_  :				Optional[int]      =      self.decoder_config.num_hidden_layers
				super().__init__(**lowerCAmelCase_		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Optional[Any]			,			lowerCAmelCase_      :				PretrainedConfig			,			lowerCAmelCase_      :				PretrainedConfig			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    List[str]:
				return cls(
				    backbone_config=lowerCAmelCase_			,			decoder_config=lowerCAmelCase_			,			**lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Dict[str, any]:
				UpperCAmelCase_  :				Union[str, Any]      =      copy.deepcopy(self.__dict__		)
				UpperCAmelCase_  :				Dict      =      self.backbone_config.to_dict()
				UpperCAmelCase_  :				Optional[int]      =      self.decoder_config.to_dict()
				UpperCAmelCase_  :				Dict      =      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
			lowerCamelCase_       			=   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''')
			lowerCamelCase_			,	lowerCamelCase_       			=   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''')
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowerCamelCase_       			=   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)



