




'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				"""AutoFeatureExtractor"""
  UpperCamelCase_			:      List[Any]										=				"""Speech2Text2Tokenizer"""



  def __init__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Any:







       '''simple docstring'''



       super().__init__(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		self.feature_extractor
       A:		Dict          =		False



  def __call__( self				:							Any					,  *SCREAMING_SNAKE_CASE_				:							Any					,  **SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		Optional[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.'''	)
            A:		List[str]          =		kwargs.pop('''raw_speech'''	)
       else:
            A:		Any          =		kwargs.pop('''audio'''					,  SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		kwargs.pop('''sampling_rate'''					,  SCREAMING_SNAKE_CASE_	)
       A:		Optional[int]          =		kwargs.pop('''text'''					,  SCREAMING_SNAKE_CASE_	)
       if len(SCREAMING_SNAKE_CASE_	) > 0:
            A:		str          =		args[0]
            A:		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:
            A:		Tuple          =		self.feature_extractor(SCREAMING_SNAKE_CASE_					,  *SCREAMING_SNAKE_CASE_					,  sampling_rate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)
       if text is not None:
            A:		List[Any]          =		self.tokenizer(SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

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



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







       '''simple docstring'''



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



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







       '''simple docstring'''



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




  @contextmanager
  def 					_snake_case     ( self				:							str	)			->		int:







       '''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.'''	)
       A:		List[Any]          =		True
       A:		List[str]          =		self.tokenizer
       yield
       A:		List[Any]          =		self.feature_extractor
       A:		int          =		False












'''simple docstring'''


import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
     import torch

     from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
     import pyanvml.pyanvml as nvml


UpperCamelCase			     =					logging.get_logger(__name__)






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      PyTorchBenchmarkArguments
  UpperCamelCase_			:      PretrainedConfig
  UpperCamelCase_			:      str										=				"PyTorch"



  @property
  def 					_snake_case     ( self				:							int	)			->		List[str]:







       '''simple docstring'''



       return torch.__version__



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		float:







       '''simple docstring'''



       A:		List[str]          =		self._prepare_inference_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_speed(_inference	)



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		[Memory, Optional[MemorySummary]]:







       '''simple docstring'''



       A:		Any          =		self._prepare_inference_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_memory(_inference	)



  def 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		float:







       '''simple docstring'''



       A:		List[Any]          =		self._prepare_train_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_speed(_train	)



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		[Memory, Optional[MemorySummary]]:







       '''simple docstring'''



       A:		List[Any]          =		self._prepare_train_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_memory(_train	)



  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Callable[[], None]:







       '''simple docstring'''



       A:		int          =		self.config_dict[model_name]

       if self.args.torchscript:
            A:		int          =		True

       A:		Any          =		(
           hasattr(SCREAMING_SNAKE_CASE_					,  '''architectures'''	)
           and isinstance(config.architectures					,  SCREAMING_SNAKE_CASE_	)
           and len(config.architectures	) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
            try:
                 A:		Optional[int]          =		config.architectures[0]
                 A:		Optional[int]          =		__import__('''transformers'''					,  fromlist=[model_class]	)
                 A:		Dict          =		getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
                 A:		Optional[Any]          =		model_cls(SCREAMING_SNAKE_CASE_	)
            except ImportError:
                 raise ImportError(
                     f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                     ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
       else:
            A:		Union[str, Any]          =		MODEL_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_	)

       model.eval()
       model.to(self.args.device	)

       # encoder-decoder has vocab size saved differently
       A:		Tuple          =		config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_					,  '''vocab_size'''	) else config.encoder.vocab_size
       A:		List[Any]          =		torch.randint(SCREAMING_SNAKE_CASE_					,  (batch_size, sequence_length)					,  dtype=torch.long					,  device=self.args.device	)

       if self.args.fpaa:
            logger.info('''Running training in Mixed Precision...'''	)
            if not self.args.is_gpu:
                 raise ValueError('''Mixed precision is possible only for GPU.'''	)
            # amp seems to have memory leaks so that memory usage
            # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
            model.half()

       if self.args.torchscript:
            with torch.no_grad():
                 A:		Any          =		torch.jit.trace(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       else:
            A:		Dict          =		model

       def encoder_decoder_forward():
            with torch.no_grad():
                 A:		List[str]          =		inference_model(SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_	)
            return outputs

       def encoder_forward():
            with torch.no_grad():
                 A:		Union[str, Any]          =		inference_model(SCREAMING_SNAKE_CASE_	)
            return outputs

       A:		List[Any]          =		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
       return _forward



  def 					_snake_case     ( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Callable[[], None]:







       '''simple docstring'''



       A:		int          =		self.config_dict[model_name]

       A:		str          =		(
           hasattr(SCREAMING_SNAKE_CASE_					,  '''architectures'''	)
           and isinstance(config.architectures					,  SCREAMING_SNAKE_CASE_	)
           and len(config.architectures	) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
            try:
                 A:		List[str]          =		config.architectures[0]
                 A:		List[str]          =		__import__('''transformers'''					,  fromlist=[model_class]	)
                 A:		Optional[int]          =		getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
                 A:		Union[str, Any]          =		model_cls(SCREAMING_SNAKE_CASE_	)
            except ImportError:
                 raise ImportError(
                     f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                     ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
       else:
            A:		Union[str, Any]          =		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_	)

       if self.args.torchscript:
            raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
       else:
            A:		str          =		model

       model.train()
       model.to(self.args.device	)

       # encoder-decoder has vocab size saved differently
       A:		Tuple          =		config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_					,  '''vocab_size'''	) else config.encoder.vocab_size
       A:		str          =		torch.randint(SCREAMING_SNAKE_CASE_					,  (batch_size, sequence_length)					,  dtype=torch.long					,  device=self.args.device	)

       if self.args.fpaa:
            logger.info('''Running training in Mixed Precision...'''	)
            if not self.args.is_gpu:
                 raise ValueError('''Mixed precision is possible only for GPU.'''	)

            # amp seems to have memory leaks so that memory usage
            # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
            model.half()

       def compute_loss_and_backprob_encoder():
            A:		str          =		train_model(SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)[0]
            loss.backward()
            return loss

       def compute_loss_and_backprob_encoder_decoder():
            A:		Dict          =		train_model(SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)[0]
            loss.backward()
            return loss

       A:		str          =		(
           compute_loss_and_backprob_encoder_decoder
           if config.is_encoder_decoder
           else compute_loss_and_backprob_encoder
       )
       return _train



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							Any	)			->		float:







       '''simple docstring'''



       try:
            if self.args.is_tpu or self.args.torchscript:
                 # run additional 10 times to stabilize compilation for tpu and torchscript
                 logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''	)
                 timeit.repeat(
                     SCREAMING_SNAKE_CASE_					,  repeat=1					,  number=5					,  )

            # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
            A:		Any          =		timeit.repeat(
                SCREAMING_SNAKE_CASE_					,  repeat=self.args.repeat					,  number=10					,  )

            if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                 import torch_xla.debug.metrics as met

                 self.print_fn(met.metrics_report()	)

            return min(SCREAMING_SNAKE_CASE_	) / 10.0
       except RuntimeError as e:
            self.print_fn(f"""Doesn't fit on GPU. {e}"""	)
            return "N/A"




  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							Callable[[], None]	)			->		[Memory, MemorySummary]:







       '''simple docstring'''



       try:
            if self.args.trace_memory_line_by_line:
                 A:		List[Any]          =		start_memory_tracing('''transformers'''	)

            if self.args.is_tpu:
                 # tpu
                 raise NotImplementedError(
                     '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
                     ''' `--no-memory` or `args.memory=False`'''	)
            elif self.args.is_gpu:
                 if not is_pyanvml_available():
                      logger.warning(
                          '''py3nvml not installed, we won\'t log GPU memory usage. '''
                          '''Install py3nvml (pip install py3nvml) to log information about GPU.'''	)
                      A:		Optional[int]          =		'''N/A'''
                 else:
                      logger.info(
                          '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
                          ''' on the same GPU.'''	)
                      # init nvml
                      nvml.nvmlInit()
                      func()
                      A:		Union[str, Any]          =		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
                      A:		Union[str, Any]          =		nvml.nvmlDeviceGetMemoryInfo(SCREAMING_SNAKE_CASE_	)
                      A:		Tuple          =		meminfo.used
                      A:		Tuple          =		Memory(SCREAMING_SNAKE_CASE_	)
                      # shutdown nvml
                      nvml.nvmlShutdown()
            else:
                 # cpu
                 A:		int          =		measure_peak_memory_cpu(SCREAMING_SNAKE_CASE_	)
                 A:		str          =		Memory(SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	) else memory_bytes

            if self.args.trace_memory_line_by_line:
                 A:		Dict          =		stop_memory_tracing(SCREAMING_SNAKE_CASE_	)
            else:
                 A:		List[str]          =		None

            return memory, summary
       except RuntimeError as e:
            self.print_fn(f"""Doesn't fit on GPU. {e}"""	)
            return "N/A", None






