



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    """AutoFeatureExtractor"""
	lowercase__						       =    """Speech2Text2Tokenizer"""





	def __init__(				self      :       List[str]				,lowerCamelCase__      :       Optional[int]				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							super().__init__(lowerCamelCase__				,lowerCamelCase__				)
							_UpperCamelCase      :	Tuple				       =     self.feature_extractor
							_UpperCamelCase      :	Optional[int]				       =     False





	def __call__(				self      :       List[Any]				,*lowerCamelCase__      :       List[str]				,**lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
													return self.current_processor(*lowerCamelCase__				,**lowerCamelCase__				)

							if "raw_speech" in kwargs:
													warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
													_UpperCamelCase      :	Optional[Any]				       =     kwargs.pop('raw_speech'				)
							else:
													_UpperCamelCase      :	Any				       =     kwargs.pop('audio'				,lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     kwargs.pop('sampling_rate'				,lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     kwargs.pop('text'				,lowerCamelCase__				)
							if len(lowerCamelCase__				) > 0:
													_UpperCamelCase      :	Union[str, Any]				       =     args[0]
													_UpperCamelCase      :	List[str]				       =     args[1:]

							if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

							if audio is not None:
													_UpperCamelCase      :	str				       =     self.feature_extractor(lowerCamelCase__				,*lowerCamelCase__				,sampling_rate=lowerCamelCase__				,**lowerCamelCase__				)
							if text is not None:
													_UpperCamelCase      :	Optional[Any]				       =     self.tokenizer(lowerCamelCase__				,**lowerCamelCase__				)

							if text is None:
													return inputs
							elif audio is None:
													return encodings
							else:
													_UpperCamelCase      :	List[Any]				       =     encodings['input_ids']
													return inputs





	def 			UpperCamelCase_						(				self      :       Any				,*lowerCamelCase__      :       List[Any]				,**lowerCamelCase__      :       Any				):







							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__				,**lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,*lowerCamelCase__      :       Union[str, Any]				,**lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__				,**lowerCamelCase__				)





	@contextmanager
	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							warnings.warn(
							    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
							    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
							    'your audio inputs, or in a separate call.'				)
							_UpperCamelCase      :	Optional[Any]				       =     True
							_UpperCamelCase      :	List[Any]				       =     self.tokenizer
							yield
							_UpperCamelCase      :	List[Any]				       =     self.feature_extractor
							_UpperCamelCase      :	int				       =     False







'''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'])


