





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


__A       : Optional[int]						    = logging.get_logger(__name__)







class     lowerCamelCase(	UpperCamelCase_  ):
					'''simple docstring'''


					__magic_name__        = 42
					__magic_name__        = 42
					__magic_name__        = 'PyTorch'




					@property
					def 					lowerCAmelCase__		(						self       ):
									return torch.__version__




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
									_A    =       self._prepare_inference_func(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase       )
									return self._measure_speed(_inference       )




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
									_A    =       self._prepare_inference_func(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase       )
									return self._measure_memory(_inference       )




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
									_A    =       self._prepare_train_func(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase       )
									return self._measure_speed(_train       )




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
									_A    =       self._prepare_train_func(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase       )
									return self._measure_memory(_train       )




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
									_A    =       self.config_dict[model_name]

									if self.args.torchscript:
													_A    =       True

									_A    =       (
									    hasattr(__UpperCamelCase							,    'architectures'       )
									    and isinstance(config.architectures							,    __UpperCamelCase       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
													try:
																	_A    =       config.architectures[0]
																	_A    =       __import__('transformers'							,    fromlist=[model_class]       )
																	_A    =       getattr(__UpperCamelCase							,    __UpperCamelCase       )
																	_A    =       model_cls(__UpperCamelCase       )
													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    =       MODEL_MAPPING[config.__class__](__UpperCamelCase       )

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

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

									def encoder_decoder_forward():
													with torch.no_grad():
																	_A    =       inference_model(__UpperCamelCase							,    decoder_input_ids=__UpperCamelCase       )
													return outputs

									def encoder_forward():
													with torch.no_grad():
																	_A    =       inference_model(__UpperCamelCase       )
													return outputs

									_A    =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
									_A    =       self.config_dict[model_name]

									_A    =       (
									    hasattr(__UpperCamelCase							,    'architectures'       )
									    and isinstance(config.architectures							,    __UpperCamelCase       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
													try:
																	_A    =       config.architectures[0]
																	_A    =       __import__('transformers'							,    fromlist=[model_class]       )
																	_A    =       getattr(__UpperCamelCase							,    __UpperCamelCase       )
																	_A    =       model_cls(__UpperCamelCase       )
													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    =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase       )

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

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

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

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

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




					def 					lowerCAmelCase__		(						self							,    snake_case_       ):


									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(
																	    __UpperCamelCase							,    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    =       timeit.repeat(
													    __UpperCamelCase							,    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(__UpperCamelCase       ) / 10.0
									except RuntimeError as e:
													self.print_fn(F"Doesn\'t fit on GPU. {e}"       )
													return "N/A"




					def 					lowerCAmelCase__		(						self							,    snake_case_       ):


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

													if self.args.trace_memory_line_by_line:
																	_A    =       stop_memory_tracing(__UpperCamelCase       )
													else:
																	_A    =       None

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












from ..utils import DummyObject, requires_backends


__A       : str						    = None





def 							__lowerCAmelCase(					*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    requires_backends(_SCREAMING_SNAKE_CASE       ,    ['sentencepiece', 'tokenizers']							)





