






"""simple docstring"""

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''git_vision_model'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						Optional[Any]=7_6_8						,    __lowerCamelCase							:						Dict=3_0_7_2						,    __lowerCamelCase							:						Any=1_2						,    __lowerCamelCase							:						Optional[int]=1_2						,    __lowerCamelCase							:						Union[str, Any]=3						,    __lowerCamelCase							:						Any=2_2_4						,    __lowerCamelCase							:						Tuple=1_6						,    __lowerCamelCase							:						Optional[int]="quick_gelu"						,    __lowerCamelCase							:						Dict=1e-5						,    __lowerCamelCase							:						Tuple=0.0						,    __lowerCamelCase							:						int=0.02						,    **__lowerCamelCase							:						Optional[int]						,    )				->					Any:
										super().__init__(**__lowerCamelCase	)

										__magic_name__            =     hidden_size
										__magic_name__            =     intermediate_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     num_channels
										__magic_name__            =     patch_size
										__magic_name__            =     image_size
										__magic_name__            =     initializer_range
										__magic_name__            =     attention_dropout
										__magic_name__            =     layer_norm_eps
										__magic_name__            =     hidden_act







				@classmethod
				def     _snake_case					(							cls							:						List[str]						,    __lowerCamelCase							:						Union[str, os.PathLike]						,    **__lowerCamelCase							:						Optional[Any]	)				->					"PretrainedConfig":
										cls._set_token_in_kwargs(__lowerCamelCase	)

										__magic_name__      ,    __magic_name__            =     cls.get_config_dict(__lowerCamelCase						,    **__lowerCamelCase	)

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get("model_type"	) == "git":
																__magic_name__            =     config_dict["vision_config"]

										if "model_type" in config_dict and hasattr(cls						,    "model_type"	) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
																    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''	)

										return cls.from_dict(__lowerCamelCase						,    **__lowerCamelCase	)





class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''git'''







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						Tuple=None						,    __lowerCamelCase							:						Dict=3_0_5_2_2						,    __lowerCamelCase							:						Any=7_6_8						,    __lowerCamelCase							:						Tuple=6						,    __lowerCamelCase							:						Union[str, Any]=1_2						,    __lowerCamelCase							:						Optional[Any]=3_0_7_2						,    __lowerCamelCase							:						Any="gelu"						,    __lowerCamelCase							:						int=0.1						,    __lowerCamelCase							:						List[str]=0.1						,    __lowerCamelCase							:						Optional[int]=1_0_2_4						,    __lowerCamelCase							:						int=0.02						,    __lowerCamelCase							:						str=1e-12						,    __lowerCamelCase							:						Tuple=0						,    __lowerCamelCase							:						Optional[int]="absolute"						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Union[str, Any]=False						,    __lowerCamelCase							:						str=1_0_1						,    __lowerCamelCase							:						Optional[Any]=1_0_2						,    __lowerCamelCase							:						str=None						,    **__lowerCamelCase							:						Dict						,    )				->					int:
										super().__init__(bos_token_id=__lowerCamelCase						,    eos_token_id=__lowerCamelCase						,    pad_token_id=__lowerCamelCase						,    **__lowerCamelCase	)

										if vision_config is None:
																__magic_name__            =     {}
																logger.info("vision_config is None. initializing the GitVisionConfig with default values."	)

										__magic_name__            =     GitVisionConfig(**__lowerCamelCase	)
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     hidden_act
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     initializer_range
										__magic_name__            =     layer_norm_eps
										__magic_name__            =     position_embedding_type
										__magic_name__            =     use_cache
										__magic_name__            =     tie_word_embeddings
										__magic_name__            =     num_image_with_embedding

										__magic_name__            =     bos_token_id
										__magic_name__            =     eos_token_id







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										__magic_name__            =     self.vision_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)



