




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  :		Tuple		     =   logging.get_logger(__name__)







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : PyTorchBenchmarkArguments
	a     : PretrainedConfig
	a     : str	  =					"PyTorch"

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





								"""simple docstring"""


								return torch.__version__

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      float:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							self._prepare_inference_func(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    )
								return self._measure_speed(_inference    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      [Memory, Optional[MemorySummary]]:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							self._prepare_inference_func(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    )
								return self._measure_memory(_inference    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      float:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							self._prepare_train_func(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    )
								return self._measure_speed(_train    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      [Memory, Optional[MemorySummary]]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							self._prepare_train_func(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    )
								return self._measure_memory(_train    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      Callable[[], None]:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							self.config_dict[model_name]

								if self.args.torchscript:
															lowercase__  :	Union[str, Any]        =							True

								lowercase__  :	Union[str, Any]        =							(
								    hasattr(lowerCamelCase	,				"architectures"    )
								    and isinstance(config.architectures	,				lowerCamelCase    )
								    and len(config.architectures    ) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																						lowercase__  :	int        =							config.architectures[0]
																						lowercase__  :	Optional[Any]        =							__import__("transformers"	,				fromlist=[model_class]    )
																						lowercase__  :	List[str]        =							getattr(lowerCamelCase	,				lowerCamelCase    )
																						lowercase__  :	Tuple        =							model_cls(lowerCamelCase    )
															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:
															lowercase__  :	Dict        =							MODEL_MAPPING[config.__class__](lowerCamelCase    )

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

								# encoder-decoder has vocab size saved differently
								lowercase__  :	Tuple        =							config.vocab_size if hasattr(lowerCamelCase	,				"vocab_size"    ) else config.encoder.vocab_size
								lowercase__  :	Optional[Any]        =							torch.randint(lowerCamelCase	,				(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():
																						lowercase__  :	Union[str, Any]        =							torch.jit.trace(lowerCamelCase	,				lowerCamelCase    )
								else:
															lowercase__  :	List[str]        =							model

								def encoder_decoder_forward():
															with torch.no_grad():
																						lowercase__  :	Optional[int]        =							inference_model(lowerCamelCase	,				decoder_input_ids=lowerCamelCase    )
															return outputs

								def encoder_forward():
															with torch.no_grad():
																						lowercase__  :	Dict        =							inference_model(lowerCamelCase    )
															return outputs

								lowercase__  :	Optional[int]        =							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
								return _forward

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      Callable[[], None]:





								"""simple docstring"""


								lowercase__  :	Any        =							self.config_dict[model_name]

								lowercase__  :	Optional[Any]        =							(
								    hasattr(lowerCamelCase	,				"architectures"    )
								    and isinstance(config.architectures	,				lowerCamelCase    )
								    and len(config.architectures    ) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																						lowercase__  :	Union[str, Any]        =							config.architectures[0]
																						lowercase__  :	Union[str, Any]        =							__import__("transformers"	,				fromlist=[model_class]    )
																						lowercase__  :	List[Any]        =							getattr(lowerCamelCase	,				lowerCamelCase    )
																						lowercase__  :	int        =							model_cls(lowerCamelCase    )
															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:
															lowercase__  :	int        =							MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase    )

								if self.args.torchscript:
															raise NotImplementedError("Training for torchscript is currently not implemented"    )
								else:
															lowercase__  :	int        =							model

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

								# encoder-decoder has vocab size saved differently
								lowercase__  :	Optional[int]        =							config.vocab_size if hasattr(lowerCamelCase	,				"vocab_size"    ) else config.encoder.vocab_size
								lowercase__  :	int        =							torch.randint(lowerCamelCase	,				(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():
															lowercase__  :	Union[str, Any]        =							train_model(lowerCamelCase	,				labels=lowerCamelCase    )[0]
															loss.backward()
															return loss

								def compute_loss_and_backprob_encoder_decoder():
															lowercase__  :	Any        =							train_model(lowerCamelCase	,				decoder_input_ids=lowerCamelCase	,				labels=lowerCamelCase    )[0]
															loss.backward()
															return loss

								lowercase__  :	int        =							(
								    compute_loss_and_backprob_encoder_decoder
								    if config.is_encoder_decoder
								    else compute_loss_and_backprob_encoder
								)
								return _train

	def    __a     (					self	,				lowerCamelCase    ) ->      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(
																						    lowerCamelCase	,				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
															lowercase__  :	Any        =							timeit.repeat(
															    lowerCamelCase	,				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(lowerCamelCase    ) / 10.0
								except RuntimeError as e:
															self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
															return "N/A"







	def    __a     (					self	,				lowerCamelCase    ) ->      [Memory, MemorySummary]:





								"""simple docstring"""


								try:
															if self.args.trace_memory_line_by_line:
																						lowercase__  :	str        =							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."    )
																													lowercase__  :	Optional[Any]        =							"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()
																													lowercase__  :	Optional[Any]        =							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
																													lowercase__  :	Optional[Any]        =							nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase    )
																													lowercase__  :	Tuple        =							meminfo.used
																													lowercase__  :	Any        =							Memory(lowerCamelCase    )
																													# shutdown nvml
																													nvml.nvmlShutdown()
															else:
																						# cpu
																						lowercase__  :	int        =							measure_peak_memory_cpu(lowerCamelCase    )
																						lowercase__  :	Optional[Any]        =							Memory(lowerCamelCase    ) if isinstance(lowerCamelCase	,				lowerCamelCase    ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																						lowercase__  :	List[Any]        =							stop_memory_tracing(lowerCamelCase    )
															else:
																						lowercase__  :	Optional[Any]        =							None

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





def  snake_case_					(		)      ->			int:

							for a in range(3_00    ):
														for b in range(a + 1    ,4_00    ):
																					for c in range(b + 1    ,5_00    ):
																												if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
																																			return a * b * c

							return -1
def  snake_case_					(		)      ->			int:

							for a in range(3_00    ):
														for b in range(4_00    ):
																					lowercase__  :	Dict        =							10_00 - a - b
																					if a < b < c and (a**2) + (b**2) == (c**2):
																												return a * b * c

							return -1
def  snake_case_					(		)      ->			None:
							import timeit

							print(
							    timeit.timeit("solution()"    ,setup="from __main__ import solution"    ,number=10_00    )    )
							print(
							    timeit.timeit(
							        "solution_fast()"    ,setup="from __main__ import solution_fast"    ,number=10_00    )    )


if __name__ == "__main__":
						print(f'{solution() = }')