





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""ClapFeatureExtractor"""
 UpperCAmelCase__						=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
 def __init__( self    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Any      )     ->      Any:
     super().__init__(UpperCAmelCase     ,	UpperCAmelCase      )
 def __call__( self    : Optional[Any]     ,	UpperCAmelCase    : Union[str, Any]=None     ,	UpperCAmelCase    : Optional[int]=None     ,	UpperCAmelCase    : Any=None     ,	**UpperCAmelCase    : Tuple      )     ->      Any:
     lowerCamelCase__       : Optional[Any]   					=						kwargs.pop('sampling_rate'     ,	UpperCAmelCase      )

     if text is None and audios is None:
         raise ValueError('You have to specify either text or audios. Both cannot be none.'      )

     if text is not None:
         lowerCamelCase__       : Optional[Any]   					=						self.tokenizer(UpperCAmelCase     ,	return_tensors=UpperCAmelCase     ,	**UpperCAmelCase      )

     if audios is not None:
         lowerCamelCase__       : List[str]   					=						self.feature_extractor(
             UpperCAmelCase     ,	sampling_rate=UpperCAmelCase     ,	return_tensors=UpperCAmelCase     ,	**UpperCAmelCase      )

     if text is not None and audios is not None:
         lowerCamelCase__       : List[str]   					=						audio_features.input_features
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCAmelCase      )     ,	tensor_type=UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	*UpperCAmelCase    : Tuple     ,	**UpperCAmelCase    : List[Any]      )     ->      Optional[Any]:
     return self.tokenizer.batch_decode(*UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	*UpperCAmelCase    : Tuple     ,	**UpperCAmelCase    : Any      )     ->      List[Any]:
     return self.tokenizer.decode(*UpperCAmelCase     ,	**UpperCAmelCase      )







 @property
 def 	A_      ( self    : Any      )     ->      List[Any]:
     lowerCamelCase__       : Union[str, Any]   					=						self.tokenizer.model_input_names
     lowerCamelCase__       : Union[str, Any]   					=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						max(self.process.memory_info().rss     ,	self.cpu_memory_peak      )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break
 def 	A_      ( self    : Tuple      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : List[str]   					=						(torch.cuda.memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20
        lowerCamelCase__       : Optional[Any]   					=						(torch.cuda.max_memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    print(F"""{description}:"""					)
    print(F"""- Time: {measures["time"]:.2f}s"""					)
    for i in range(torch.cuda.device_count()					):
        print(F"""- GPU {i} allocated: {measures[str(_UpperCAmelCase					)]:.2f}MiB"""					)
        lowerCamelCase__       : List[str]   					=						measures[F"""{i}-peak"""]
        print(F"""- GPU {i} peak: {peak:.2f}MiB"""					)
    print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""					)
    print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""					)





