





'''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


snake_case_  :							List[Any]            =	logging.get_logger(__name__)


class        __a   (lowerCamelCase  ):
							__a       : PyTorchBenchmarkArguments
							__a       : PretrainedConfig
							__a       : str          =							"PyTorch"

							@property
							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Union[str, Any]:







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

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int )       ->							float:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	self._prepare_inference_func(__magic_name__							,  __magic_name__							,  __magic_name__ )
													return self._measure_speed(_inference )

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int )       ->							[Memory, Optional[MemorySummary]]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	self._prepare_inference_func(__magic_name__							,  __magic_name__							,  __magic_name__ )
													return self._measure_memory(_inference )

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int )       ->							float:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	self._prepare_train_func(__magic_name__							,  __magic_name__							,  __magic_name__ )
													return self._measure_speed(_train )

							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int )       ->							[Memory, Optional[MemorySummary]]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	self._prepare_train_func(__magic_name__							,  __magic_name__							,  __magic_name__ )
													return self._measure_memory(_train )

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int )       ->							Callable[[], None]:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.config_dict[model_name]

													if self.args.torchscript:
																			UpperCAmelCase_      :   int      =	True

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

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

													# encoder-decoder has vocab size saved differently
													UpperCAmelCase_      :   Dict      =	config.vocab_size if hasattr(__magic_name__							,  '''vocab_size''' ) else config.encoder.vocab_size
													UpperCAmelCase_      :   Optional[Any]      =	torch.randint(__magic_name__							,  (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():
																									UpperCAmelCase_      :   int      =	torch.jit.trace(__magic_name__							,  __magic_name__ )
													else:
																			UpperCAmelCase_      :   Any      =	model

													def encoder_decoder_forward():
																			with torch.no_grad():
																									UpperCAmelCase_      :   Any      =	inference_model(__magic_name__							,  decoder_input_ids=__magic_name__ )
																			return outputs

													def encoder_forward():
																			with torch.no_grad():
																									UpperCAmelCase_      :   int      =	inference_model(__magic_name__ )
																			return outputs

													UpperCAmelCase_      :   Optional[Any]      =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
													return _forward

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int )       ->							Callable[[], None]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	self.config_dict[model_name]

													UpperCAmelCase_      :   int      =	(
													    hasattr(__magic_name__							,  '''architectures''' )
													    and isinstance(config.architectures							,  __magic_name__ )
													    and len(config.architectures ) > 0
													)
													if not self.args.only_pretrain_model and has_model_class_in_config:
																			try:
																									UpperCAmelCase_      :   Tuple      =	config.architectures[0]
																									UpperCAmelCase_      :   Optional[int]      =	__import__('''transformers'''							,  fromlist=[model_class] )
																									UpperCAmelCase_      :   List[Any]      =	getattr(__magic_name__							,  __magic_name__ )
																									UpperCAmelCase_      :   Dict      =	model_cls(__magic_name__ )
																			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:
																			UpperCAmelCase_      :   Optional[int]      =	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__magic_name__ )

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

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

													# encoder-decoder has vocab size saved differently
													UpperCAmelCase_      :   Tuple      =	config.vocab_size if hasattr(__magic_name__							,  '''vocab_size''' ) else config.encoder.vocab_size
													UpperCAmelCase_      :   Tuple      =	torch.randint(__magic_name__							,  (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():
																			UpperCAmelCase_      :   List[str]      =	train_model(__magic_name__							,  labels=__magic_name__ )[0]
																			loss.backward()
																			return loss

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

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

							def      UpperCAmelCase__						(				self				:  Union[str, Any]							,  __magic_name__				:  Union[str, Any] )       ->							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(
																									    __magic_name__							,  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
																			UpperCAmelCase_      :   str      =	timeit.repeat(
																			    __magic_name__							,  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(__magic_name__ ) / 1_0.0
													except RuntimeError as e:
																			self.print_fn(F"""Doesn't fit on GPU. {e}""" )
																			return "N/A"



							def      UpperCAmelCase__						(				self				:  List[str]							,  __magic_name__				:  Callable[[], None] )       ->							[Memory, MemorySummary]:







													"""simple docstring"""
													try:
																			if self.args.trace_memory_line_by_line:
																									UpperCAmelCase_      :   int      =	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.''' )
																															UpperCAmelCase_      :   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()
																															UpperCAmelCase_      :   Dict      =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
																															UpperCAmelCase_      :   Any      =	nvml.nvmlDeviceGetMemoryInfo(__magic_name__ )
																															UpperCAmelCase_      :   int      =	meminfo.used
																															UpperCAmelCase_      :   Any      =	Memory(__magic_name__ )
																															# shutdown nvml
																															nvml.nvmlShutdown()
																			else:
																									# cpu
																									UpperCAmelCase_      :   Optional[Any]      =	measure_peak_memory_cpu(__magic_name__ )
																									UpperCAmelCase_      :   Dict      =	Memory(__magic_name__ ) if isinstance(__magic_name__							,  __magic_name__ ) else memory_bytes

																			if self.args.trace_memory_line_by_line:
																									UpperCAmelCase_      :   Dict      =	stop_memory_tracing(__magic_name__ )
																			else:
																									UpperCAmelCase_      :   str      =	None

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







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

