
"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


snake_case			       =		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					if "token" in model_name_or_path:
										return "rag_token"
					if "sequence" in model_name_or_path:
										return "rag_sequence"
					if "bart" in model_name_or_path:
										return "bart"
					return None

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					return max(metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ) for gt in ground_truths      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]
					SCREAMING_SNAKE_CASE           =     []

					if args.gold_data_mode == "qa":
										SCREAMING_SNAKE_CASE           =     pd.read_csv(SCREAMING_SNAKE_CASE_,					sep='\t',					header=SCREAMING_SNAKE_CASE_      )
										for answer_list in data[1]:
															SCREAMING_SNAKE_CASE           =     ast.literal_eval(SCREAMING_SNAKE_CASE_      )
															answers.append(SCREAMING_SNAKE_CASE_      )
					else:
										SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]
										SCREAMING_SNAKE_CASE           =     [[reference] for reference in references]

					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
					for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
										total += 1
										em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     100.0 * em / total
					SCREAMING_SNAKE_CASE           =     100.0 * fa / total

					logger.info(f'''F1: {fa:.2f}'''      )
					logger.info(f'''EM: {em:.2f}'''      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     args.k
					SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]
					SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]

					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
					for hypo, reference in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     set(hypo.split('\t'      )[:k]      )
										SCREAMING_SNAKE_CASE           =     set(reference.split('\t'      )      )
										total += 1
										em += len(hypo_provenance & ref_provenance      ) / k

					SCREAMING_SNAKE_CASE           =     100.0 * em / total
					logger.info(f'''Precision@{k}: {em: .2f}'''      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					def strip_title(SCREAMING_SNAKE_CASE_      ):
										if title.startswith('"'      ):
															SCREAMING_SNAKE_CASE           =     title[1:]
										if title.endswith('"'      ):
															SCREAMING_SNAKE_CASE           =     title[:-1]
										return title

					SCREAMING_SNAKE_CASE           =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    SCREAMING_SNAKE_CASE_,					return_tensors='pt',					padding=SCREAMING_SNAKE_CASE_,					truncation=SCREAMING_SNAKE_CASE_,					)['input_ids'].to(args.device      )

					SCREAMING_SNAKE_CASE           =     rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     question_enc_outputs[0]

					SCREAMING_SNAKE_CASE           =     rag_model.retriever(
					    SCREAMING_SNAKE_CASE_,					question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy(),					prefix=rag_model.rag.generator.config.prefix,					n_docs=rag_model.config.n_docs,					return_tensors='pt',					)
					SCREAMING_SNAKE_CASE           =     rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
					SCREAMING_SNAKE_CASE           =     []
					for docs in all_docs:
										SCREAMING_SNAKE_CASE           =     [strip_title(SCREAMING_SNAKE_CASE_      ) for title in docs['title']]
										provenance_strings.append('\t'.join(SCREAMING_SNAKE_CASE_      )      )
					return provenance_strings

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    SCREAMING_SNAKE_CASE_,					return_tensors='pt',					padding=SCREAMING_SNAKE_CASE_,					truncation=SCREAMING_SNAKE_CASE_      )

										SCREAMING_SNAKE_CASE           =     inputs_dict.input_ids.to(args.device      )
										SCREAMING_SNAKE_CASE           =     inputs_dict.attention_mask.to(args.device      )
										SCREAMING_SNAKE_CASE           =     rag_model.generate(  # rag_model overwrites generate
										    SCREAMING_SNAKE_CASE_,					attention_mask=SCREAMING_SNAKE_CASE_,					num_beams=args.num_beams,					min_length=args.min_length,					max_length=args.max_length,					early_stopping=SCREAMING_SNAKE_CASE_,					num_return_sequences=1,					bad_words_ids=[[0, 0]],					)
										SCREAMING_SNAKE_CASE           =     rag_model.retriever.generator_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,					skip_special_tokens=SCREAMING_SNAKE_CASE_      )

										if args.print_predictions:
															for q, a in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
																				logger.info('Q: {} - A: {}'.format(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )      )

										return answers

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser()
					parser.add_argument(
					    '--model_type',					choices=['rag_sequence', 'rag_token', 'bart'],					type=SCREAMING_SNAKE_CASE_,					help=(
					        'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
					        ' model_name_or_path'
					    ),					)
					parser.add_argument(
					    '--index_name',					default=SCREAMING_SNAKE_CASE_,					choices=['exact', 'compressed', 'legacy'],					type=SCREAMING_SNAKE_CASE_,					help='RAG model retriever type',					)
					parser.add_argument(
					    '--index_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					help='Path to the retrieval index',					)
					parser.add_argument('--n_docs',					default=5,					type=SCREAMING_SNAKE_CASE_,					help='Number of retrieved docs'      )
					parser.add_argument(
					    '--model_name_or_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					help='Path to pretrained checkpoints or model identifier from huggingface.co/models',					)
					parser.add_argument(
					    '--eval_mode',					choices=['e2e', 'retrieval'],					default='e2e',					type=SCREAMING_SNAKE_CASE_,					help=(
					        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
					        ' precision@k.'
					    ),					)
					parser.add_argument('--k',					default=1,					type=SCREAMING_SNAKE_CASE_,					help='k for the precision@k calculation'      )
					parser.add_argument(
					    '--evaluation_set',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					help='Path to a file containing evaluation samples',					)
					parser.add_argument(
					    '--gold_data_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					help='Path to a tab-separated file with gold samples',					)
					parser.add_argument(
					    '--gold_data_mode',					default='qa',					type=SCREAMING_SNAKE_CASE_,					choices=['qa', 'ans'],					help=(
					        'Format of the gold data file'
					        'qa - a single line in the following format: question [tab] answer_list'
					        'ans - a single line of the gold file contains the expected answer string'
					    ),					)
					parser.add_argument(
					    '--predictions_path',					type=SCREAMING_SNAKE_CASE_,					default='predictions.txt',					help='Name of the predictions file, to be stored in the checkpoints directory',					)
					parser.add_argument(
					    '--eval_all_checkpoints',					action='store_true',					help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number',					)
					parser.add_argument(
					    '--eval_batch_size',					default=8,					type=SCREAMING_SNAKE_CASE_,					help='Batch size per GPU/CPU for evaluation.',					)
					parser.add_argument(
					    '--recalculate',					help='Recalculate predictions even if the prediction file exists',					action='store_true',					)
					parser.add_argument(
					    '--num_beams',					default=4,					type=SCREAMING_SNAKE_CASE_,					help='Number of beams to be used when generating answers',					)
					parser.add_argument('--min_length',					default=1,					type=SCREAMING_SNAKE_CASE_,					help='Min length of the generated answers'      )
					parser.add_argument('--max_length',					default=5_0,					type=SCREAMING_SNAKE_CASE_,					help='Max length of the generated answers'      )

					parser.add_argument(
					    '--print_predictions',					action='store_true',					help='If True, prints predictions while evaluating.',					)
					parser.add_argument(
					    '--print_docs',					action='store_true',					help='If True, prints docs retried while generating.',					)
					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					SCREAMING_SNAKE_CASE           =     torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     {}
					if args.model_type is None:
										SCREAMING_SNAKE_CASE           =     infer_model_type(args.model_name_or_path      )
										assert args.model_type is not None
					if args.model_type.startswith('rag'      ):
										SCREAMING_SNAKE_CASE           =     RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
										SCREAMING_SNAKE_CASE           =     args.n_docs
										if args.index_name is not None:
															SCREAMING_SNAKE_CASE           =     args.index_name
										if args.index_path is not None:
															SCREAMING_SNAKE_CASE           =     args.index_path
					else:
										SCREAMING_SNAKE_CASE           =     BartForConditionalGeneration

					SCREAMING_SNAKE_CASE           =     (
					    [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
					    if args.eval_all_checkpoints
					    else [args.model_name_or_path]
					)

					logger.info('Evaluate the following checkpoints: %s',					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     get_scores if args.eval_mode == 'e2e' else get_precision_at_k
					SCREAMING_SNAKE_CASE           =     evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

					for checkpoint in checkpoints:
										if os.path.exists(args.predictions_path      ) and (not args.recalculate):
															logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path      )      )
															score_fn(SCREAMING_SNAKE_CASE_,					args.predictions_path,					args.gold_data_path      )
															continue

										logger.info('***** Running evaluation for {} *****'.format(SCREAMING_SNAKE_CASE_      )      )
										logger.info('  Batch size = %d',					args.eval_batch_size      )
										logger.info('  Predictions will be stored under {}'.format(args.predictions_path      )      )

										if args.model_type.startswith('rag'      ):
															SCREAMING_SNAKE_CASE           =     RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_      )
															SCREAMING_SNAKE_CASE           =     model_class.from_pretrained(SCREAMING_SNAKE_CASE_,					retriever=SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_      )
															model.retriever.init_retrieval()
										else:
															SCREAMING_SNAKE_CASE           =     model_class.from_pretrained(SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_      )
										model.to(args.device      )

										with open(args.evaluation_set,					'r'      ) as eval_file, open(args.predictions_path,					'w'      ) as preds_file:
															SCREAMING_SNAKE_CASE           =     []
															for line in tqdm(SCREAMING_SNAKE_CASE_      ):
																				questions.append(line.strip()      )
																				if len(SCREAMING_SNAKE_CASE_      ) == args.eval_batch_size:
																									SCREAMING_SNAKE_CASE           =     evaluate_batch_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
																									preds_file.write('\n'.join(SCREAMING_SNAKE_CASE_      ) + '\n'      )
																									preds_file.flush()
																									SCREAMING_SNAKE_CASE           =     []
															if len(SCREAMING_SNAKE_CASE_      ) > 0:
																				SCREAMING_SNAKE_CASE           =     evaluate_batch_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
																				preds_file.write('\n'.join(SCREAMING_SNAKE_CASE_      )      )
																				preds_file.flush()

															score_fn(SCREAMING_SNAKE_CASE_,					args.predictions_path,					args.gold_data_path      )


if __name__ == "__main__":
	snake_case			       =		get_args()
	main(args)


"""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			       =		logging.get_logger(__name__)




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				PyTorchBenchmarkArguments
				UpperCAmelCase_    :				PretrainedConfig
				UpperCAmelCase_    :				str						  =    "PyTorch"


				@property
				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									return torch.__version__


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									if self.args.torchscript:
														SCREAMING_SNAKE_CASE           =     True

									SCREAMING_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:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_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:
														SCREAMING_SNAKE_CASE           =     MODEL_MAPPING[config.__class__](lowercase__      )

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

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_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():
																			SCREAMING_SNAKE_CASE           =     torch.jit.trace(lowercase__		,     lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									def encoder_decoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__		,     decoder_input_ids=lowercase__      )
														return outputs

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

									SCREAMING_SNAKE_CASE           =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									SCREAMING_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:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_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:
														SCREAMING_SNAKE_CASE           =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase__      )

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

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

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

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

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


				def 							A (    self		,     lowercase__      )   ->   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(
																			    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
														SCREAMING_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		,     lowercase__      )   ->   [Memory, MemorySummary]:
									"""simple docstring"""







									try:
														if self.args.trace_memory_line_by_line:
																			SCREAMING_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.'      )
																								SCREAMING_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()
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetMemoryInfo(lowercase__      )
																								SCREAMING_SNAKE_CASE           =     meminfo.used
																								SCREAMING_SNAKE_CASE           =     Memory(lowercase__      )
																								# shutdown nvml
																								nvml.nvmlShutdown()
														else:
																			# cpu
																			SCREAMING_SNAKE_CASE           =     measure_peak_memory_cpu(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     Memory(lowercase__      ) if isinstance(lowercase__		,     lowercase__      ) else memory_bytes

														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     stop_memory_tracing(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     None

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