






"""simple docstring"""



from ..utils import DummyObject, requires_backends




class 							__lowercase(							metaclass=_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							["""torch""", """librosa"""]






						def __init__(       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	List[Any]:




											"""simple docstring"""





											requires_backends(self     ,    ['torch', 'librosa'])






						@classmethod
						def 		lowerCAmelCase (       cls     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Union[str, Any]:




											"""simple docstring"""





											requires_backends(cls     ,    ['torch', 'librosa'])






						@classmethod
						def 		lowerCAmelCase (       cls     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	str:




											"""simple docstring"""





											requires_backends(cls     ,    ['torch', 'librosa'])




class 							__lowercase(							metaclass=_UpperCamelCase	):
						lowerCAmelCase_		:				List[str]=							["""torch""", """librosa"""]






						def __init__(       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





											requires_backends(self     ,    ['torch', 'librosa'])






						@classmethod
						def 		lowerCAmelCase (       cls     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Any:




											"""simple docstring"""





											requires_backends(cls     ,    ['torch', 'librosa'])






						@classmethod
						def 		lowerCAmelCase (       cls     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Dict:




											"""simple docstring"""





											requires_backends(cls     ,    ['torch', 'librosa'])







"""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


_snake_case							:   str	  =       logging.get_logger(__name__)




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				PyTorchBenchmarkArguments
						lowerCAmelCase_		:				PretrainedConfig
						lowerCAmelCase_		:				str=							"PyTorch"






						@property
						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											return torch.__version__






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	float:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_inference_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_speed(_inference)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	[Memory, Optional[MemorySummary]]:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_inference_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_memory(_inference)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	float:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_train_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_speed(_train)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	[Memory, Optional[MemorySummary]]:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_train_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_memory(_train)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Callable[[], None]:




											"""simple docstring"""





											lowerCamelCase__					=self.config_dict[model_name]

											if self.args.torchscript:
																lowerCamelCase__					=True

											lowerCamelCase__					=(
											    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:
																					lowerCamelCase__					=config.architectures[0]
																					lowerCamelCase__					=__import__('transformers'     ,    fromlist=[model_class])
																					lowerCamelCase__					=getattr(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=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:
																lowerCamelCase__					=MODEL_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE)

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

											# encoder-decoder has vocab size saved differently
											lowerCamelCase__					=config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE     ,    'vocab_size') else config.encoder.vocab_size
											lowerCamelCase__					=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():
																					lowerCamelCase__					=torch.jit.trace(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											else:
																lowerCamelCase__					=model

											def encoder_decoder_forward():
																with torch.no_grad():
																					lowerCamelCase__					=inference_model(_SCREAMING_SNAKE_CASE     ,    decoder_input_ids=_SCREAMING_SNAKE_CASE)
																return outputs

											def encoder_forward():
																with torch.no_grad():
																					lowerCamelCase__					=inference_model(_SCREAMING_SNAKE_CASE)
																return outputs

											lowerCamelCase__					=encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
											return _forward






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Callable[[], None]:




											"""simple docstring"""





											lowerCamelCase__					=self.config_dict[model_name]

											lowerCamelCase__					=(
											    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:
																					lowerCamelCase__					=config.architectures[0]
																					lowerCamelCase__					=__import__('transformers'     ,    fromlist=[model_class])
																					lowerCamelCase__					=getattr(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=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:
																lowerCamelCase__					=MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE)

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

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

											# encoder-decoder has vocab size saved differently
											lowerCamelCase__					=config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE     ,    'vocab_size') else config.encoder.vocab_size
											lowerCamelCase__					=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():
																lowerCamelCase__					=train_model(_SCREAMING_SNAKE_CASE     ,    labels=_SCREAMING_SNAKE_CASE)[0]
																loss.backward()
																return loss

											def compute_loss_and_backprob_encoder_decoder():
																lowerCamelCase__					=train_model(_SCREAMING_SNAKE_CASE     ,    decoder_input_ids=_SCREAMING_SNAKE_CASE     ,    labels=_SCREAMING_SNAKE_CASE)[0]
																loss.backward()
																return loss

											lowerCamelCase__					=(
											    compute_loss_and_backprob_encoder_decoder
											    if config.is_encoder_decoder
											    else compute_loss_and_backprob_encoder
											)
											return _train






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	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
																lowerCamelCase__					=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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	[Memory, MemorySummary]:




											"""simple docstring"""





											try:
																if self.args.trace_memory_line_by_line:
																					lowerCamelCase__					=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.')
																										lowerCamelCase__					='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()
																										lowerCamelCase__					=nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
																										lowerCamelCase__					=nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE)
																										lowerCamelCase__					=meminfo.used
																										lowerCamelCase__					=Memory(_SCREAMING_SNAKE_CASE)
																										# shutdown nvml
																										nvml.nvmlShutdown()
																else:
																					# cpu
																					lowerCamelCase__					=measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=Memory(_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE) else memory_bytes

																if self.args.trace_memory_line_by_line:
																					lowerCamelCase__					=stop_memory_tracing(_SCREAMING_SNAKE_CASE)
																else:
																					lowerCamelCase__					=None

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