



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


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : List[Any]       					=  "beit"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=8192,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[3, 5, 7, 11],      SCREAMING_SNAKE_CASE_=[1, 2, 3, 6],      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=0.4,      SCREAMING_SNAKE_CASE_=256,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=255,      **SCREAMING_SNAKE_CASE_,      )					->		str:
          super().__init__(**_SCREAMING_SNAKE_CASE	)

          UpperCamelCase   :  Dict						     =     vocab_size
          UpperCamelCase   :  Any						     =     hidden_size
          UpperCamelCase   :  Tuple						     =     num_hidden_layers
          UpperCamelCase   :  Union[str, Any]						     =     num_attention_heads
          UpperCamelCase   :  List[Any]						     =     intermediate_size
          UpperCamelCase   :  List[Any]						     =     hidden_act
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Any						     =     attention_probs_dropout_prob
          UpperCamelCase   :  str						     =     initializer_range
          UpperCamelCase   :  Any						     =     layer_norm_eps

          UpperCamelCase   :  Tuple						     =     image_size
          UpperCamelCase   :  List[str]						     =     patch_size
          UpperCamelCase   :  Dict						     =     num_channels
          UpperCamelCase   :  Optional[Any]						     =     use_mask_token
          UpperCamelCase   :  str						     =     use_absolute_position_embeddings
          UpperCamelCase   :  List[str]						     =     use_relative_position_bias
          UpperCamelCase   :  Optional[int]						     =     use_shared_relative_position_bias
          UpperCamelCase   :  List[str]						     =     layer_scale_init_value
          UpperCamelCase   :  Union[str, Any]						     =     drop_path_rate
          UpperCamelCase   :  Union[str, Any]						     =     use_mean_pooling
          # decode head attributes (semantic segmentation)
          UpperCamelCase   :  Optional[Any]						     =     out_indices
          UpperCamelCase   :  Optional[Any]						     =     pool_scales
          # auxiliary head attributes (semantic segmentation)
          UpperCamelCase   :  Tuple						     =     use_auxiliary_head
          UpperCamelCase   :  str						     =     auxiliary_loss_weight
          UpperCamelCase   :  Union[str, Any]						     =     auxiliary_channels
          UpperCamelCase   :  List[Any]						     =     auxiliary_num_convs
          UpperCamelCase   :  Tuple						     =     auxiliary_concat_input
          UpperCamelCase   :  int						     =     semantic_loss_ignore_index






class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Tuple       					=  version.parse("1.11"							)
       @property
       def  snake_case_	(      self	)					->		Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ]	)






       @property
       def  snake_case_	(      self	)					->		float:
          return 1e-4








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



