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


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      "PyTorch"



		@property
		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return torch.__version__



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      self._prepare_inference_func(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					return self._measure_speed(_inference   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      self._prepare_inference_func(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					return self._measure_memory(_inference   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      self._prepare_train_func(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					return self._measure_speed(_train   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      self._prepare_train_func(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					return self._measure_memory(_train   )



		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      self.config_dict[model_name]

					if self.args.torchscript:
								_snake_case              =      True

					_snake_case              =      (
					    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:
											_snake_case              =      config.architectures[0]
											_snake_case              =      __import__("transformers"  ,					fromlist=[model_class]   )
											_snake_case              =      getattr(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
											_snake_case              =      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:
								_snake_case              =      MODEL_MAPPING[config.__class__](__SCREAMING_SNAKE_CASE   )

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

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

					def encoder_decoder_forward():
								with torch.no_grad():
											_snake_case              =      inference_model(__SCREAMING_SNAKE_CASE  ,					decoder_input_ids=__SCREAMING_SNAKE_CASE   )
								return outputs

					def encoder_forward():
								with torch.no_grad():
											_snake_case              =      inference_model(__SCREAMING_SNAKE_CASE   )
								return outputs

					_snake_case              =      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
					return _forward



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      self.config_dict[model_name]

					_snake_case              =      (
					    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:
											_snake_case              =      config.architectures[0]
											_snake_case              =      __import__("transformers"  ,					fromlist=[model_class]   )
											_snake_case              =      getattr(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
											_snake_case              =      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:
								_snake_case              =      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__SCREAMING_SNAKE_CASE   )

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

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

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

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

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



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""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
								_snake_case              =      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   ) / 1_0.0
					except RuntimeError as e:
								self.print_fn(F'Doesn\'t fit on GPU. {e}'   )
								return "N/A"







		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Callable[[], None]   ):






					"""simple docstring"""
					try:
								if self.args.trace_memory_line_by_line:
											_snake_case              =      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."   )
														_snake_case              =      "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()
														_snake_case              =      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
														_snake_case              =      nvml.nvmlDeviceGetMemoryInfo(__SCREAMING_SNAKE_CASE   )
														_snake_case              =      meminfo.used
														_snake_case              =      Memory(__SCREAMING_SNAKE_CASE   )
														# shutdown nvml
														nvml.nvmlShutdown()
								else:
											# cpu
											_snake_case              =      measure_peak_memory_cpu(__SCREAMING_SNAKE_CASE   )
											_snake_case              =      Memory(__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ) else memory_bytes

								if self.args.trace_memory_line_by_line:
											_snake_case              =      stop_memory_tracing(__SCREAMING_SNAKE_CASE   )
								else:
											_snake_case              =      None

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


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

