

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def 						SCREAMING_SNAKE_CASE(  *UpperCamelCase  )    ->    Optional[int]:
	with open(UpperCamelCase   ,'r'  ) as fh:
		fcntl.flock(UpperCamelCase   ,fcntl.LOCK_EX  )
		try:
			print(*UpperCamelCase  )
		finally:
			fcntl.flock(UpperCamelCase   ,fcntl.LOCK_UN  )


lowerCAmelCase__										= int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
lowerCAmelCase__										= torch.device("cuda", local_rank)
lowerCAmelCase__										= socket.gethostname()

lowerCAmelCase__										= F'[{hostname}-{local_rank}]'

try:
						# test distributed
						dist.init_process_group("nccl")
						dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
						dist.barrier()

						# test cuda is available and can allocate memory
						torch.cuda.is_available()
						torch.ones(1).cuda(local_rank)

						# global rank
						lowerCAmelCase__										= dist.get_rank()
						lowerCAmelCase__										= dist.get_world_size()

						printflock(F'{gpu} is OK (global rank: {rank}/{world_size})')

						dist.barrier()
						if rank == 0:
												printflock(F'pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}')

except Exception:
						printflock(F'{gpu} is broken')
						raise









'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "Wav2Vec2FeatureExtractor"
							_lowerCamelCase		:      Any=   "AutoTokenizer"


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      int       	=					self.feature_extractor
								UpperCAmelCase_		:      str       	=					False


							@classmethod
							def 						_snake_case							(			cls    ,			_snake_case    ,			**_snake_case)							->				List[Any]:
								try:
									return super().from_pretrained(_snake_case    ,			**_snake_case)
								except OSError:
									warnings.warn(
									    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
									    ' include a `tokenizer_class` attribute is deprecated and will be '
									    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
									    ' attribute to either your `config.json` or `tokenizer_config.json` '
									    'file to suppress this warning: '    ,			_snake_case    ,			)

									UpperCAmelCase_		:      Any       	=					WavaVecaFeatureExtractor.from_pretrained(_snake_case    ,			**_snake_case)
									UpperCAmelCase_		:      List[str]       	=					WavaVecaCTCTokenizer.from_pretrained(_snake_case    ,			**_snake_case)

									return cls(feature_extractor=_snake_case    ,			tokenizer=_snake_case)


							def __call__(			self    ,			*_snake_case    ,			**_snake_case)							->				Optional[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor(*_snake_case    ,			**_snake_case)

								if "raw_speech" in kwargs:
									warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.')
									UpperCAmelCase_		:      List[str]       	=					kwargs.pop('raw_speech')
								else:
									UpperCAmelCase_		:      Dict       	=					kwargs.pop('audio'    ,			_snake_case)
								UpperCAmelCase_		:      str       	=					kwargs.pop('sampling_rate'    ,			_snake_case)
								UpperCAmelCase_		:      List[str]       	=					kwargs.pop('text'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      List[str]       	=					args[0]
									UpperCAmelCase_		:      Tuple       	=					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_		:      Union[str, Any]       	=					self.feature_extractor(_snake_case    ,			*_snake_case    ,			sampling_rate=_snake_case    ,			**_snake_case)
								if text is not None:
									UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer(_snake_case    ,			**_snake_case)

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


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				str:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor.pad(*_snake_case    ,			**_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					kwargs.pop('input_features'    ,			_snake_case)
								UpperCAmelCase_		:      Dict       	=					kwargs.pop('labels'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      Optional[Any]       	=					args[0]
									UpperCAmelCase_		:      int       	=					args[1:]

								if input_features is not None:
									UpperCAmelCase_		:      Dict       	=					self.feature_extractor.pad(_snake_case    ,			*_snake_case    ,			**_snake_case)
								if labels is not None:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.tokenizer.pad(_snake_case    ,			**_snake_case)

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					labels['input_ids']
									return input_features


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Dict:
								return self.tokenizer.batch_decode(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Tuple:
								return self.tokenizer.decode(*_snake_case    ,			**_snake_case)





							@contextmanager
							def 						_snake_case							(			self)							->				Tuple:
								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_		:      Tuple       	=					self.tokenizer
								yield
								UpperCAmelCase_		:      Tuple       	=					self.feature_extractor
								UpperCAmelCase_		:      Tuple       	=					False






