
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def __magic_name__  (							*_lowerCamelCase:		Union[str, Any] )				->	Dict:

	'''simple docstring'''




	lowerCAmelCase				       =     torch.cuda.device_count()
	print(F"""Device count: {num_processes}""" )
	with patch_environment(
	    world_size=_lowerCamelCase,				master_addr='''127.0.01''',				master_port='''29500''',				mixed_precision=args[1].mixed_precision ):
		lowerCAmelCase				       =     PrepareForLaunch(_lowerCamelCase,				distributed_type='''MULTI_GPU''' )
		torch.multiprocessing.start_processes(_lowerCamelCase,				args=_lowerCamelCase,				nprocs=_lowerCamelCase,				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
					UpperCAmelCase          =							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
					UpperCAmelCase          =							[
					    """pip:./""",
					    """transformers""",
					    """datasets""",
					    """evaluate""",
					    """tqdm""",
					    """scipy""",
					    """scikit-learn""",
					    """tensorboard""",
					    """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
					]
					UpperCAmelCase          =							rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

					# Define train args/config, run train function
					UpperCAmelCase          =							argparse.Namespace(cpu=False, mixed_precision="""fp16""")
					UpperCAmelCase          =							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
					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'])




"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



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






			def __init__(self     :  Tuple			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  int:





				"""simple docstring"""


				super().__init__(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     self.feature_extractor
				lowerCAmelCase				       =     False






			def __call__(self     :  Tuple			,*SCREAMING_SNAKE_CASE_     :  Optional[Any]			,**SCREAMING_SNAKE_CASE_     :  Dict )					->  Any:





				"""simple docstring"""


				if self._in_target_context_manager:
					return self.current_processor(*SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )

				if "raw_speech" in kwargs:
					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
					lowerCAmelCase				       =     kwargs.pop('''raw_speech''' )
				else:
					lowerCAmelCase				       =     kwargs.pop('''audio'''			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     kwargs.pop('''sampling_rate'''			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     kwargs.pop('''text'''			,SCREAMING_SNAKE_CASE_ )
				if len(SCREAMING_SNAKE_CASE_ ) > 0:
					lowerCAmelCase				       =     args[0]
					lowerCAmelCase				       =     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:
					lowerCAmelCase				       =     self.feature_extractor(SCREAMING_SNAKE_CASE_			,*SCREAMING_SNAKE_CASE_			,sampling_rate=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )
				if text is not None:
					lowerCAmelCase				       =     self.tokenizer(SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )

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






			def 				UpperCAmelCase       (self     :  Optional[int]			,*SCREAMING_SNAKE_CASE_     :  Any			,**SCREAMING_SNAKE_CASE_     :  List[Any] )					->  str:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[int]			,*SCREAMING_SNAKE_CASE_     :  int			,**SCREAMING_SNAKE_CASE_     :  Any )					->  Dict:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			@contextmanager
			def 				UpperCAmelCase       (self     :  Optional[int] )					->  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.''' )
				lowerCAmelCase				       =     True
				lowerCAmelCase				       =     self.tokenizer
				yield
				lowerCAmelCase				       =     self.feature_extractor
				lowerCAmelCase				       =     False


