



'''simple docstring'''
import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


snake_case_				:			Union[str, Any]						 =						logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
snake_case_				:			Optional[Any]						 =						direct_transformers_import(Path(__file__).parent)


snake_case_				:			Union[str, Any]						 =						{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}






class 							lowercase__			(							lowercase__     ):
	lowercase__						       =    ["""feature_extractor""", """tokenizer"""]
	# Names need to be attr_class for attr in attributes
	lowercase__						       =    None
	lowercase__						       =    None
	lowercase__						       =    None





	def __init__(				self      :       Dict				,*lowerCamelCase__      :       int				,**lowerCamelCase__      :       Union[str, Any]				):







							'''simple docstring'''
							# Sanitize args and kwargs
							for key in kwargs:
													if key not in self.attributes:
																			raise TypeError(F'Unexpected keyword argument {key}.'				)
							for arg, attribute_name in zip(_A				,self.attributes				):
													if attribute_name in kwargs:
																			raise TypeError(F'Got multiple values for argument {attribute_name}.'				)
													else:
																			_UpperCamelCase      :	Any				       =     arg

							if len(_A				) != len(self.attributes				):
													raise ValueError(
													    F'This processor requires {len(self.attributes				)} arguments: {", ".join(self.attributes				)}. Got '
													    F'{len(_A				)} arguments instead.'				)

							# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
							for attribute_name, arg in kwargs.items():
													_UpperCamelCase      :	List[Any]				       =     getattr(self				,F'{attribute_name}_class'				)
													# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
													_UpperCamelCase      :	int				       =     AUTO_TO_BASE_CLASS_MAPPING.get(_A				,_A				)
													if isinstance(_A				,_A				):
																			_UpperCamelCase      :	List[Any]				       =     tuple(getattr(_A				,_A				) for n in class_name if n is not None				)
													else:
																			_UpperCamelCase      :	Optional[int]				       =     getattr(_A				,_A				)

													if not isinstance(_A				,_A				):
																			raise ValueError(
																			    F'Received a {type(_A				).__name__} for argument {attribute_name}, but a {class_name} was expected.'				)

													setattr(self				,_A				,_A				)





	def __repr__(				self      :       Union[str, Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     [F'- {name}: {repr(getattr(self				,_A				)				)}' for name in self.attributes]
							_UpperCamelCase      :	Union[str, Any]				       =     '\n'.join(_A				)
							return F'{self.__class__.__name__}:\n{attributes_repr}'





	def 			UpperCamelCase_						(				self      :       List[Any]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       bool = False				,**lowerCamelCase__      :       List[str]				):







							'''simple docstring'''
							os.makedirs(_A				,exist_ok=_A				)

							if push_to_hub:
													_UpperCamelCase      :	Dict				       =     kwargs.pop('commit_message'				,_A				)
													_UpperCamelCase      :	Optional[Any]				       =     kwargs.pop('repo_id'				,save_directory.split(os.path.sep				)[-1]				)
													_UpperCamelCase      :	List[str]				       =     self._create_repo(_A				,**_A				)
													_UpperCamelCase      :	Optional[Any]				       =     self._get_files_timestamps(_A				)
							# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
							# loaded from the Hub.
							if self._auto_class is not None:
													_UpperCamelCase      :	Optional[Any]				       =     [getattr(self				,_A				) for attribute_name in self.attributes]
													_UpperCamelCase      :	Dict				       =     [(a.init_kwargs if isinstance(_A				,_A				) else a) for a in attrs]
													custom_object_save(self				,_A				,config=_A				)

							for attribute_name in self.attributes:
													_UpperCamelCase      :	Union[str, Any]				       =     getattr(self				,_A				)
													# Include the processor class in the attribute config so this processor can then be reloaded with the
													# `AutoProcessor` API.
													if hasattr(_A				,'_set_processor_class'				):
																			attribute._set_processor_class(self.__class__.__name__				)
													attribute.save_pretrained(_A				)

							if self._auto_class is not None:
													# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
													for attribute_name in self.attributes:
																			_UpperCamelCase      :	Optional[int]				       =     getattr(self				,_A				)
																			if isinstance(_A				,_A				):
																									del attribute.init_kwargs["auto_map"]

							if push_to_hub:
													self._upload_modified_files(
													    _A				,_A				,_A				,commit_message=_A				,token=kwargs.get('use_auth_token'				)				,)





	@classmethod
	def 			UpperCamelCase_						(				cls      :       str				,lowerCamelCase__      :       Union[str, os.PathLike]				,lowerCamelCase__      :       Optional[Union[str, os.PathLike]] = None				,lowerCamelCase__      :       bool = False				,lowerCamelCase__      :       bool = False				,lowerCamelCase__      :       Optional[Union[str, bool]] = None				,lowerCamelCase__      :       str = "main"				,**lowerCamelCase__      :       Any				,):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     cache_dir
							_UpperCamelCase      :	Any				       =     force_download
							_UpperCamelCase      :	List[str]				       =     local_files_only
							_UpperCamelCase      :	Optional[int]				       =     revision

							_UpperCamelCase      :	Optional[Any]				       =     kwargs.pop('use_auth_token'				,_A				)
							if use_auth_token is not None:
													warnings.warn(
													    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'				,_A				)
													if token is not None:
																			raise ValueError(
																			    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'				)
													_UpperCamelCase      :	str				       =     use_auth_token

							if token is not None:
													# change to `token` in a follow-up PR
													_UpperCamelCase      :	Any				       =     token

							_UpperCamelCase      :	Dict				       =     cls._get_arguments_from_pretrained(_A				,**_A				)
							return cls(*_A				)





	@classmethod
	def 			UpperCamelCase_						(				cls      :       str				,lowerCamelCase__      :       Optional[Any]="AutoProcessor"				):







							'''simple docstring'''
							if not isinstance(_A				,_A				):
													_UpperCamelCase      :	Dict				       =     auto_class.__name__

							import transformers.models.auto as auto_module

							if not hasattr(_A				,_A				):
													raise ValueError(F'{auto_class} is not a valid auto class.'				)

							_UpperCamelCase      :	Tuple				       =     auto_class





	@classmethod
	def 			UpperCamelCase_						(				cls      :       Dict				,lowerCamelCase__      :       int				,**lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	List[str]				       =     []
							for attribute_name in cls.attributes:
													_UpperCamelCase      :	List[str]				       =     getattr(cls				,F'{attribute_name}_class'				)
													if isinstance(_A				,_A				):
																			_UpperCamelCase      :	int				       =     tuple(getattr(_A				,_A				) if n is not None else None for n in class_name				)
																			_UpperCamelCase      :	Tuple				       =     kwargs.get('use_fast'				,_A				)
																			if use_fast and classes[1] is not None:
																									_UpperCamelCase      :	Tuple				       =     classes[1]
																			else:
																									_UpperCamelCase      :	Optional[Any]				       =     classes[0]
													else:
																			_UpperCamelCase      :	List[str]				       =     getattr(_A				,_A				)

													args.append(attribute_class.from_pretrained(_A				,**_A				)				)
							return args





	@property
	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     getattr(self				,self.attributes[0]				)
							return getattr(_A				,'model_input_names'				,_A				)
snake_case_				:			str						 =						copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
					snake_case_				:			str						 =						ProcessorMixin.push_to_hub.__doc__.format(
					    object='processor', object_class='AutoProcessor', object_files='processor files'
					)







'''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'])


