

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








"""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_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = "swin"

 lowercase__			 = {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(    self:		Any,				a_:		List[str]=224,				a_:		List[Any]=4,				a_:		List[Any]=3,				a_:		Dict=96,				a_:		List[str]=[2, 2, 6, 2],				a_:		int=[3, 6, 12, 24],				a_:		int=7,				a_:		str=4.0,				a_:		Optional[Any]=True,				a_:		Dict=0.0,				a_:		List[Any]=0.0,				a_:		List[str]=0.1,				a_:		Union[str, Any]="gelu",				a_:		Dict=False,				a_:		Union[str, Any]=0.02,				a_:		Optional[int]=1E-5,				a_:		Optional[int]=32,				a_:		Tuple=None,				a_:		Union[str, Any]=None,				**a_:		Any,				):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		Any								=  image_size
   _snake_case							:		List[Any]								=  patch_size
   _snake_case							:		Tuple								=  num_channels
   _snake_case							:		str								=  embed_dim
   _snake_case							:		Union[str, Any]								=  depths
   _snake_case							:		int								=  len(a_		)
   _snake_case							:		Union[str, Any]								=  num_heads
   _snake_case							:		List[str]								=  window_size
   _snake_case							:		str								=  mlp_ratio
   _snake_case							:		Union[str, Any]								=  qkv_bias
   _snake_case							:		Dict								=  hidden_dropout_prob
   _snake_case							:		str								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  drop_path_rate
   _snake_case							:		Optional[int]								=  hidden_act
   _snake_case							:		str								=  use_absolute_embeddings
   _snake_case							:		Tuple								=  layer_norm_eps
   _snake_case							:		List[Any]								=  initializer_range
   _snake_case							:		Optional[Any]								=  encoder_stride
   # 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
   _snake_case							:		Any								=  int(embed_dim * 2 ** (len(a_		) - 1)		)
   _snake_case							:		Any								=  ["""stem"""] + [f"stage{idx}" for idx in range(1,				len(a_		) + 1		)]
   _snake_case		, _snake_case							:		List[str]								=  get_aligned_output_features_output_indices(
       out_features=a_,				out_indices=a_,				stage_names=self.stage_names		)







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = version.parse("1.11"			)


 @property
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]		)


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return 1E-4


