




"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     A__      (      lowercase_     ):
	A_							:				int						     =      (KDPMaAncestralDiscreteScheduler,)
	A_							:				List[str]						     =      1_0




	def 							a_		(   self  :	Optional[int]      ,    **lowercase  :	Dict	)					-> Optional[int]:
						__snake_case          =	{
						    """num_train_timesteps""": 1_100,
						    """beta_start""": 0.00_01,
						    """beta_end""": 0.02,
						    """beta_schedule""": """linear""",
						}

						config.update(**lowercase	)
						return config




	def 							a_		(   self  :	str	)					-> List[str]:
						for timesteps in [10, 50, 100, 1_000]:
											self.check_over_configs(num_train_timesteps=lowercase	)




	def 							a_		(   self  :	str	)					-> int:
						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]      ,    [0.00_02, 0.0_02, 0.02]	):
											self.check_over_configs(beta_start=lowercase      ,    beta_end=lowercase	)




	def 							a_		(   self  :	int	)					-> Any:
						for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowercase	)




	def 							a_		(   self  :	Any	)					-> List[str]:
						if torch_device == "mps":
											return
						__snake_case          =	self.scheduler_classes[0]
						__snake_case          =	self.get_scheduler_config()
						__snake_case          =	scheduler_class(**lowercase	)

						scheduler.set_timesteps(self.num_inference_steps	)

						__snake_case          =	torch.manual_seed(0	)

						__snake_case          =	self.dummy_model()
						__snake_case          =	self.dummy_sample_deter * scheduler.init_noise_sigma
						__snake_case          =	sample.to(lowercase	)

						for i, t in enumerate(scheduler.timesteps	):
											__snake_case          =	scheduler.scale_model_input(lowercase      ,    lowercase	)

											__snake_case          =	model(lowercase      ,    lowercase	)

											__snake_case          =	scheduler.step(lowercase      ,    lowercase      ,    lowercase      ,    generator=lowercase	)
											__snake_case          =	output.prev_sample

						__snake_case          =	torch.sum(torch.abs(lowercase	)	)
						__snake_case          =	torch.mean(torch.abs(lowercase	)	)

						assert abs(result_sum.item() - 1_38_49.38_77	) < 1e-2
						assert abs(result_mean.item() - 18.03_31	) < 5e-3




	def 							a_		(   self  :	Optional[int]	)					-> Optional[int]:
						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase	)




	def 							a_		(   self  :	Union[str, Any]	)					-> int:
						if torch_device == "mps":
											return
						__snake_case          =	self.scheduler_classes[0]
						__snake_case          =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
						__snake_case          =	scheduler_class(**lowercase	)

						scheduler.set_timesteps(self.num_inference_steps	)

						__snake_case          =	self.dummy_model()
						__snake_case          =	self.dummy_sample_deter * scheduler.init_noise_sigma
						__snake_case          =	sample.to(lowercase	)

						__snake_case          =	torch.manual_seed(0	)

						for i, t in enumerate(scheduler.timesteps	):
											__snake_case          =	scheduler.scale_model_input(lowercase      ,    lowercase	)

											__snake_case          =	model(lowercase      ,    lowercase	)

											__snake_case          =	scheduler.step(lowercase      ,    lowercase      ,    lowercase      ,    generator=lowercase	)
											__snake_case          =	output.prev_sample

						__snake_case          =	torch.sum(torch.abs(lowercase	)	)
						__snake_case          =	torch.mean(torch.abs(lowercase	)	)

						assert abs(result_sum.item() - 3_28.99_70	) < 1e-2
						assert abs(result_mean.item() - 0.42_84	) < 1e-3




	def 							a_		(   self  :	Union[str, Any]	)					-> str:
						if torch_device == "mps":
											return
						__snake_case          =	self.scheduler_classes[0]
						__snake_case          =	self.get_scheduler_config()
						__snake_case          =	scheduler_class(**lowercase	)

						scheduler.set_timesteps(self.num_inference_steps      ,    device=lowercase	)
						__snake_case          =	torch.manual_seed(0	)

						__snake_case          =	self.dummy_model()
						__snake_case          =	self.dummy_sample_deter.to(lowercase	) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__snake_case          =	scheduler.scale_model_input(lowercase      ,    lowercase	)

											__snake_case          =	model(lowercase      ,    lowercase	)

											__snake_case          =	scheduler.step(lowercase      ,    lowercase      ,    lowercase      ,    generator=lowercase	)
											__snake_case          =	output.prev_sample

						__snake_case          =	torch.sum(torch.abs(lowercase	)	)
						__snake_case          =	torch.mean(torch.abs(lowercase	)	)

						assert abs(result_sum.item() - 1_38_49.38_18	) < 1e-1
						assert abs(result_mean.item() - 18.03_31	) < 1e-3








"""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__ :    List[str] 					=							logging.get_logger(__name__)
class     A__      (      lowercase_     ):
	A_							:				PyTorchBenchmarkArguments
	A_							:				PretrainedConfig
	A_							:				str						     =      "PyTorch"




	@property
	def 							a_		(   self  :	Optional[Any]	)					-> Tuple:
						return torch.__version__




	def 							a_		(   self  :	int      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_inference	)




	def 							a_		(   self  :	Dict      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_inference	)




	def 							a_		(   self  :	str      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_train	)




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_train	)




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						if self.args.torchscript:
											__snake_case          =	True

						__snake_case          =	(
						    hasattr(lowercase      ,    """architectures"""	)
						    and isinstance(config.architectures      ,    lowercase	)
						    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(lowercase      ,    lowercase	)
																__snake_case          =	model_cls(lowercase	)
											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__](lowercase	)

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

						# encoder-decoder has vocab size saved differently
						__snake_case          =	config.vocab_size if hasattr(lowercase      ,    """vocab_size"""	) else config.encoder.vocab_size
						__snake_case          =	torch.randint(lowercase      ,    (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(lowercase      ,    lowercase	)
						else:
											__snake_case          =	model

						def encoder_decoder_forward():
											with torch.no_grad():
																__snake_case          =	inference_model(lowercase      ,    decoder_input_ids=lowercase	)
											return outputs

						def encoder_forward():
											with torch.no_grad():
																__snake_case          =	inference_model(lowercase	)
											return outputs

						__snake_case          =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
						return _forward




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						__snake_case          =	(
						    hasattr(lowercase      ,    """architectures"""	)
						    and isinstance(config.architectures      ,    lowercase	)
						    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(lowercase      ,    lowercase	)
																__snake_case          =	model_cls(lowercase	)
											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__](lowercase	)

						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(lowercase      ,    """vocab_size"""	) else config.encoder.vocab_size
						__snake_case          =	torch.randint(lowercase      ,    (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(lowercase      ,    labels=lowercase	)[0]
											loss.backward()
											return loss

						def compute_loss_and_backprob_encoder_decoder():
											__snake_case          =	train_model(lowercase      ,    decoder_input_ids=lowercase      ,    labels=lowercase	)[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  :	Tuple      ,    lowercase  :	List[Any]	)					-> float:
						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(
																    lowercase      ,    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(
											    lowercase      ,    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(lowercase	) / 10.0
						except RuntimeError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Callable[[], None]	)					-> [Memory, MemorySummary]:
						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(lowercase	)
																					__snake_case          =	meminfo.used
																					__snake_case          =	Memory(lowercase	)
																					# shutdown nvml
																					nvml.nvmlShutdown()
											else:
																# cpu
																__snake_case          =	measure_peak_memory_cpu(lowercase	)
																__snake_case          =	Memory(lowercase	) if isinstance(lowercase      ,    lowercase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
																__snake_case          =	stop_memory_tracing(lowercase	)
											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


