


'''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


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






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      42
						__snake_case   =      42
						__snake_case   =      "PyTorch"







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







						def lowerCAmelCase__ (  self:	Tuple      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	float:
										__magic_name__				:					Optional[int]						       =       self._prepare_inference_func(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
										return self._measure_speed(_inference     )







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	[Memory, Optional[MemorySummary]]:
										__magic_name__				:					Optional[int]						       =       self._prepare_inference_func(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
										return self._measure_memory(_inference     )







						def lowerCAmelCase__ (  self:	Dict      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	float:
										__magic_name__				:					Optional[Any]						       =       self._prepare_train_func(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
										return self._measure_speed(_train     )







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	[Memory, Optional[MemorySummary]]:
										__magic_name__				:					Optional[Any]						       =       self._prepare_train_func(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
										return self._measure_memory(_train     )







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	Callable[[], None]:
										__magic_name__				:					Union[str, Any]						       =       self.config_dict[model_name]

										if self.args.torchscript:
														__magic_name__				:					str						       =       True

										__magic_name__				:					str						       =       (
										    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:
																		__magic_name__				:					int						       =       config.architectures[0]
																		__magic_name__				:					List[Any]						       =       __import__("transformers"      ,							fromlist=[model_class]     )
																		__magic_name__				:					int						       =       getattr(__UpperCamelCase      ,							__UpperCamelCase     )
																		__magic_name__				:					Optional[Any]						       =       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:
														__magic_name__				:					Dict						       =       MODEL_MAPPING[config.__class__](__UpperCamelCase     )

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

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

										def encoder_decoder_forward():
														with torch.no_grad():
																		__magic_name__				:					List[Any]						       =       inference_model(__UpperCamelCase      ,							decoder_input_ids=__UpperCamelCase     )
														return outputs

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

										__magic_name__				:					Tuple						       =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
										return _forward







						def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	Callable[[], None]:
										__magic_name__				:					str						       =       self.config_dict[model_name]

										__magic_name__				:					str						       =       (
										    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:
																		__magic_name__				:					Dict						       =       config.architectures[0]
																		__magic_name__				:					List[str]						       =       __import__("transformers"      ,							fromlist=[model_class]     )
																		__magic_name__				:					Any						       =       getattr(__UpperCamelCase      ,							__UpperCamelCase     )
																		__magic_name__				:					Any						       =       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:
														__magic_name__				:					Optional[Any]						       =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase     )

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

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

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

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

										__magic_name__				:					List[Any]						       =       (
										    compute_loss_and_backprob_encoder_decoder
										    if config.is_encoder_decoder
										    else compute_loss_and_backprob_encoder
										)
										return _train







						def lowerCAmelCase__ (  self:	Dict      ,							__UpperCamelCase:	List[str]     )							->	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(
																		    __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
														__magic_name__				:					int						       =       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     ) / 1_0.0
										except RuntimeError as e:
														self.print_fn(f"""Doesn't fit on GPU. {e}"""     )
														return "N/A"







						def lowerCAmelCase__ (  self:	List[str]      ,							__UpperCamelCase:	Callable[[], None]     )							->	[Memory, MemorySummary]:
										try:
														if self.args.trace_memory_line_by_line:
																		__magic_name__				:					Union[str, Any]						       =       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."     )
																						__magic_name__				:					Optional[int]						       =       "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()
																						__magic_name__				:					Tuple						       =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
																						__magic_name__				:					str						       =       nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase     )
																						__magic_name__				:					Dict						       =       meminfo.used
																						__magic_name__				:					Any						       =       Memory(__UpperCamelCase     )
																						# shutdown nvml
																						nvml.nvmlShutdown()
														else:
																		# cpu
																		__magic_name__				:					List[Any]						       =       measure_peak_memory_cpu(__UpperCamelCase     )
																		__magic_name__				:					Any						       =       Memory(__UpperCamelCase     ) if isinstance(__UpperCamelCase      ,							__UpperCamelCase     ) else memory_bytes

														if self.args.trace_memory_line_by_line:
																		__magic_name__				:					int						       =       stop_memory_tracing(__UpperCamelCase     )
														else:
																		__magic_name__				:					str						       =       None

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



'''simple docstring'''







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      (LMSDiscreteScheduler,)
						__snake_case   =      1_0







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							**__UpperCamelCase:	Any     )							->	List[str]:
										__magic_name__				:					List[str]						       =       {
										    "num_train_timesteps": 1100,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__UpperCamelCase     )
										return config







						def lowerCAmelCase__ (  self:	Any     )							->	str:
										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	int:
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]     ):
														self.check_over_configs(beta_start=__UpperCamelCase      ,							beta_end=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	Any:
										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[Any]:
										for t in [0, 500, 800]:
														self.check_over_forward(time_step=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[str]     )							->	Optional[int]:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					List[str]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					str						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Dict						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Optional[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Tuple						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Union[str, Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	int     )							->	Tuple:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					Tuple						       =       self.get_scheduler_config(prediction_type="v_prediction"     )
										__magic_name__				:					List[Any]						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					Tuple						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					int						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Dict						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Optional[Any]						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					List[Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 0.0_0_1_7     ) < 1E-2
										assert abs(result_mean.item() - 2.2_676E-06     ) < 1E-3







						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	str:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					List[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Any						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps      ,							device=__UpperCamelCase     )

										__magic_name__				:					Optional[Any]						       =       self.dummy_model()
										__magic_name__				:					Union[str, Any]						       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										__magic_name__				:					int						       =       sample.to(__UpperCamelCase     )

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Optional[int]						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					List[Any]						       =       output.prev_sample

										__magic_name__				:					Any						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					int						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					Optional[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase      ,							use_karras_sigmas=__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps      ,							device=__UpperCamelCase     )

										__magic_name__				:					int						       =       self.dummy_model()
										__magic_name__				:					Optional[int]						       =       self.dummy_sample_deter.to(__UpperCamelCase     ) * scheduler.init_noise_sigma
										__magic_name__				:					List[str]						       =       sample.to(__UpperCamelCase     )

										for t in scheduler.timesteps:
														__magic_name__				:					str						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					List[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					str						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					int						       =       output.prev_sample

										__magic_name__				:					str						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Dict						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 3_8_1_2.9_9_2_7     ) < 2E-2
										assert abs(result_mean.item() - 4.9_6_4_8     ) < 1E-3