






"""simple docstring"""



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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A		:       List[Any]  				=		logging.get_logger(__name__)

__A		:       List[Any]  				=		{
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}





class 					_UpperCAmelCase       ( _A   ,						_A							):
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   "resnet"
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   ["basic", "bottleneck"]




					def __init__(				self     :						Any    ,						A     :						Tuple=3    ,						A     :						str=64    ,						A     :						Tuple=[2_56, 5_12, 10_24, 20_48]    ,						A     :						List[Any]=[3, 4, 6, 3]    ,						A     :						Union[str, Any]="bottleneck"    ,						A     :						int="relu"    ,						A     :						List[Any]=False    ,						A     :						Tuple=None    ,						A     :						int=None    ,						**A     :						List[str]    ,						)  ->  List[Any]:
							super().__init__(**A   )
							if layer_type not in self.layer_types:
									raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types   )}'''   )
							lowercase_ : List[Any]							       =   num_channels
							lowercase_ : Tuple							       =   embedding_size
							lowercase_ : Dict							       =   hidden_sizes
							lowercase_ : Tuple							       =   depths
							lowercase_ : Optional[int]							       =   layer_type
							lowercase_ : str							       =   hidden_act
							lowercase_ : Dict							       =   downsample_in_first_stage
							lowercase_ : str							       =   ['''stem'''] + [F'''stage{idx}''' for idx in range(1    ,						len(A   ) + 1   )]
							lowercase_       ,					lowercase_ : List[str]							       =   get_aligned_output_features_output_indices(
							    out_features=A    ,						out_indices=A    ,						stage_names=self.stage_names   )





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   version.parse("1.11"							)




					@property
					def   A  (				self     :						Any   )  ->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]   )






					@property
					def   A  (				self     :						Union[str, Any]   )  ->  float:
							return 1e-3






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
       snake_case_			  = 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''')
       snake_case_  ,		snake_case_			  = 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''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = 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)

