



'''simple docstring'''
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
snake_case_				:			List[str]						 =						'src/transformers'

# Matches is_xxx_available()
snake_case_				:			List[Any]						 =						re.compile(r'is\_([a-z_]*)_available()')
# Matches from xxx import bla
snake_case_				:			Any						 =						re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
snake_case_				:			List[Any]						 =						re.compile(r'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


snake_case_				:			Optional[Any]						 =						'\n{0} = None\n'

snake_case_				:			Any						 =						'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


snake_case_				:			List[Any]						 =						'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def A__					(  UpperCAmelCase_							):
						if _re_test_backend.search(UpperCAmelCase_							) is None:
												return None
						_UpperCamelCase      :	Any				       =     [b[0] for b in _re_backend.findall(UpperCAmelCase_							)]
						backends.sort()
						return "_and_".join(UpperCAmelCase_							)






def A__					(  ):
						with open(os.path.join(UpperCAmelCase_						,						'__init__.py'							)						,						'r'						,						encoding='utf-8'						,						newline='\n'							) as f:
												_UpperCamelCase      :	List[str]				       =     f.readlines()

						# Get to the point we do the actual imports for type checking
						_UpperCamelCase      :	Tuple				       =     0
						while not lines[line_index].startswith('if TYPE_CHECKING'							):
												line_index += 1

						_UpperCamelCase      :	Dict				       =     {}
						# Go through the end of the file
						while line_index < len(UpperCAmelCase_							):
												# If the line is an if is_backend_available, we grab all objects associated.
												_UpperCamelCase      :	Tuple				       =     find_backend(lines[line_index]							)
												if backend is not None:
																		while not lines[line_index].startswith('    else:'							):
																								line_index += 1
																		line_index += 1

																		_UpperCamelCase      :	Optional[Any]				       =     []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]							) <= 1 or lines[line_index].startswith(' ' * 8							):
																								_UpperCamelCase      :	Dict				       =     lines[line_index]
																								_UpperCamelCase      :	List[str]				       =     _re_single_line_import.search(UpperCAmelCase_							)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(', '							)							)
																								elif line.startswith(' ' * 1_2							):
																														objects.append(line[1_2:-2]							)
																								line_index += 1

																		_UpperCamelCase      :	Tuple				       =     objects
												else:
																		line_index += 1

						return backend_specific_objects






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_							):
						if name.isupper():
												return DUMMY_CONSTANT.format(UpperCAmelCase_							)
						elif name.islower():
												return DUMMY_FUNCTION.format(UpperCAmelCase_						,						UpperCAmelCase_							)
						else:
												return DUMMY_CLASS.format(UpperCAmelCase_						,						UpperCAmelCase_							)






def A__					(  UpperCAmelCase_=None							):
						if backend_specific_objects is None:
												_UpperCamelCase      :	Dict				       =     read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						_UpperCamelCase      :	Union[str, Any]				       =     {}

						for backend, objects in backend_specific_objects.items():
												_UpperCamelCase      :	Optional[int]				       =     '[' + ', '.join(f'"{b}"' for b in backend.split('_and_'							)							) + ']'
												_UpperCamelCase      :	List[str]				       =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(UpperCAmelCase_						,						UpperCAmelCase_							) for o in objects]							)
												_UpperCamelCase      :	str				       =     dummy_file

						return dummy_files






def A__					(  UpperCAmelCase_=False							):
						_UpperCamelCase      :	int				       =     create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						_UpperCamelCase      :	List[Any]				       =     {'torch': 'pt'}

						# Locate actual dummy modules and read their content.
						_UpperCamelCase      :	List[Any]				       =     os.path.join(UpperCAmelCase_						,						'utils'							)
						_UpperCamelCase      :	Optional[Any]				       =     {
						    backend: os.path.join(UpperCAmelCase_						,						f'dummy_{short_names.get(UpperCAmelCase_						,						UpperCAmelCase_							)}_objects.py'							)
						    for backend in dummy_files.keys()
						}

						_UpperCamelCase      :	Optional[int]				       =     {}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(UpperCAmelCase_							):
																		with open(UpperCAmelCase_						,						'r'						,						encoding='utf-8'						,						newline='\n'							) as f:
																								_UpperCamelCase      :	Dict				       =     f.read()
												else:
																		_UpperCamelCase      :	Any				       =     ''

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f'Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase_						,						UpperCAmelCase_							)}_objects.py as the main '
																								    '__init__ has new objects.'							)
																								with open(dummy_file_paths[backend]						,						'w'						,						encoding='utf-8'						,						newline='\n'							) as f:
																														f.write(dummy_files[backend]							)
																		else:
																								raise ValueError(
																								    'The main __init__ has objects that are not present in '
																								    f'transformers.utils.dummy_{short_names.get(UpperCAmelCase_						,						UpperCAmelCase_							)}_objects.py. Run `make fix-copies` '
																								    'to fix this.'							)


if __name__ == "__main__":
					snake_case_				:			Optional[int]						 =						argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					snake_case_				:			Optional[Any]						 =						parser.parse_args()

					check_dummies(args.fix_and_overwrite)







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def A__					(  *UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     torch.cuda.device_count()
						print(f'Device count: {num_processes}'							)
						with patch_environment(
						    world_size=UpperCAmelCase_						,						master_addr='127.0.01'						,						master_port='29500'						,						mixed_precision=args[1].mixed_precision							):
												_UpperCamelCase      :	Optional[int]				       =     PrepareForLaunch(UpperCAmelCase_						,						distributed_type='MULTI_GPU'							)
												torch.multiprocessing.start_processes(UpperCAmelCase_						,						args=UpperCAmelCase_						,						nprocs=UpperCAmelCase_						,						start_method='spawn'							)


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					snake_case_				:			Optional[Any]						 =						rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					snake_case_				:			List[Any]						 =						[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					snake_case_				:			Union[str, Any]						 =						rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					snake_case_				:			List[str]						 =						argparse.Namespace(cpu=False, mixed_precision='fp16')
					snake_case_				:			List[str]						 =						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])


