
"""simple docstring"""




import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
	from comet_ml import OfflineExperiment

if is_tensorboard_available():
	import struct

	import tensorboard.compat.proto.event_pba as event_pba

snake_case			       =    logging.getLogger(__name__)




@require_tensorboard
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_config'
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers(lowercase__		,     lowercase__      )
														accelerator.end_training()
														for child in Path(f'''{dirpath}/{project_name}'''      ).glob('*/**'      ):
																			SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : x.is_file()		,     child.iterdir()      )      )[0]
														self.assertNotEqual(str(lowercase__      )		,     ''      )


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_log'
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )
														accelerator.init_trackers(lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : x.is_file()		,     Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
														self.assertNotEqual(str(lowercase__      )		,     ''      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_log'
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )
														accelerator.init_trackers(lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'tensor': torch.tensor(1      )}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : x.is_file()		,     Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
														# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
														with open(lowercase__		,     'rb'      ) as f:
																			SCREAMING_SNAKE_CASE           =     f.read()
														SCREAMING_SNAKE_CASE           =     False
														while data:
																			SCREAMING_SNAKE_CASE           =     struct.unpack('Q'		,     data[:8]      )

																			SCREAMING_SNAKE_CASE           =     data[12 : 12 + int(header[0]      )]  # 8+4
																			SCREAMING_SNAKE_CASE           =     data[12 + int(header[0]      ) + 4 :]
																			SCREAMING_SNAKE_CASE           =     event_pba.Event()

																			event.ParseFromString(lowercase__      )
																			if event.HasField('summary'      ):
																								for value in event.summary.value:
																													if value.simple_value == 1.0 and value.tag == "tensor":
																																		SCREAMING_SNAKE_CASE           =     True
														self.assertTrue(lowercase__		,     'Converted tensor was not found in the log file!'      )


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									with self.assertRaisesRegex(lowercase__		,     'Logging with `tensorboard` requires a `logging_dir`'      ):
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'      )
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )




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







									SCREAMING_SNAKE_CASE           =     ProjectConfiguration(total_limit=30      )
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__		,     project_config=lowercase__      )






@require_wandb
@mock.patch.dict(os.environ  ,				{"WANDB_MODE": "offline"}				)
class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									super().setUp()
									# wandb let's us override where logs are stored to via the WANDB_DIR env var
									self.add_mocks(mock.patch.dict(os.environ		,     {'WANDB_DIR': self.tmpdir}      )      )


				@staticmethod
				def 							A (    lowercase__		,     lowercase__		,     lowercase__ = True      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     rf'''{section} ([\S\s]*?)\n\n'''
									if record:
														SCREAMING_SNAKE_CASE           =     rf'''Record: {pattern}'''
									SCREAMING_SNAKE_CASE           =     re.findall(lowercase__		,     lowercase__      )[0]
									# A config
									if section == "config" or section == "history":
														SCREAMING_SNAKE_CASE           =     re.findall(r'"([a-zA-Z0-9_.,]+)'		,     lowercase__      )
														return {key: val for key, val in zip(cleaned_record[0::2]		,     cleaned_record[1::2]      )}
									# Everything else
									else:
														return dict(re.findall(r'(\w+): "([^\s]+)"'		,     lowercase__      )      )




				@skip
				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_config'
									SCREAMING_SNAKE_CASE           =     Accelerator(log_with='wandb'      )
									SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
									SCREAMING_SNAKE_CASE           =     {'wandb': {'tags': ['my_tag']}}
									accelerator.init_trackers(lowercase__		,     lowercase__		,     lowercase__      )
									SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
									accelerator.log(lowercase__		,     step=0      )
									accelerator.end_training()
									# The latest offline log is stored at wandb/latest-run/*.wandb
									for child in Path(f'''{self.tmpdir}/wandb/latest-run'''      ).glob('*'      ):
														if child.is_file() and child.suffix == ".wandb":
																			SCREAMING_SNAKE_CASE           =     subprocess.check_output(
																			    ['wandb', 'sync', '--view', '--verbose', str(lowercase__      )]		,     env=os.environ.copy()      ).decode('utf8'		,     'ignore'      )
																			break

        # Check HPS through careful parsing and cleaning
									SCREAMING_SNAKE_CASE           =     self.parse_log(lowercase__		,     'config'      )
									self.assertEqual(logged_items['num_iterations']		,     '12'      )
									self.assertEqual(logged_items['learning_rate']		,     '0.01'      )
									self.assertEqual(logged_items['some_boolean']		,     'false'      )
									self.assertEqual(logged_items['some_string']		,     'some_value'      )
									self.assertEqual(logged_items['some_string']		,     'some_value'      )

									# Run tags
									SCREAMING_SNAKE_CASE           =     self.parse_log(lowercase__		,     'run'		,     lowercase__      )
									self.assertEqual(logged_items['tags']		,     'my_tag'      )

									# Actual logging
									SCREAMING_SNAKE_CASE           =     self.parse_log(lowercase__		,     'history'      )
									self.assertEqual(logged_items['total_loss']		,     '0.1'      )
									self.assertEqual(logged_items['iteration']		,     '1'      )
									self.assertEqual(logged_items['my_text']		,     'some_value'      )
									self.assertEqual(logged_items['_step']		,     '0'      )






def   UpperCamelCase_  (		self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     run_name
					SCREAMING_SNAKE_CASE           =     OfflineExperiment(project_name=SCREAMING_SNAKE_CASE_,					offline_directory=SCREAMING_SNAKE_CASE_      )
					logger.info(f'''Initialized offline CometML project {self.run_name}'''      )
					logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'      )




@require_comet_ml
@mock.patch.object(__magic_name__  ,				"__init__"  ,				__magic_name__				)
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				@staticmethod
				def 							A (    lowercase__		,     lowercase__		,     lowercase__ = False      )   ->   Optional[int]:
									"""simple docstring"""







									for log in log_list:
														SCREAMING_SNAKE_CASE           =     json.loads(lowercase__      )['payload']
														if is_param and "param" in j.keys():
																			if j["param"]["paramName"] == key:
																								return j["param"]["paramValue"]
														if "log_other" in j.keys():
																			if j["log_other"]["key"] == key:
																								return j["log_other"]["val"]
														if "metric" in j.keys():
																			if j["metric"]["metricName"] == key:
																								return j["metric"]["metricValue"]


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     CometMLTracker('test_project_with_config'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers(lowercase__		,     lowercase__      )
														accelerator.end_training()
														SCREAMING_SNAKE_CASE           =     os.listdir(lowercase__      )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     zipfile.ZipFile(lowercase__		,     'r'      )
														SCREAMING_SNAKE_CASE           =     archive.open('messages.json'      ).read().decode('utf-8'      )
									SCREAMING_SNAKE_CASE           =     log.split('\n'      )[:-1]
									self.assertEqual(self.get_value_from_key(lowercase__		,     'num_iterations'		,     lowercase__      )		,     12      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'learning_rate'		,     lowercase__      )		,     0.01      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'some_boolean'		,     lowercase__      )		,     lowercase__      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'some_string'		,     lowercase__      )		,     'some_value'      )




				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     CometMLTracker('test_project_with_config'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														accelerator.init_trackers(lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														SCREAMING_SNAKE_CASE           =     os.listdir(lowercase__      )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     zipfile.ZipFile(lowercase__		,     'r'      )
														SCREAMING_SNAKE_CASE           =     archive.open('messages.json'      ).read().decode('utf-8'      )
									SCREAMING_SNAKE_CASE           =     log.split('\n'      )[:-1]
									self.assertEqual(self.get_value_from_key(lowercase__		,     'curr_step'		,     lowercase__      )		,     0      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'total_loss'      )		,     0.1      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'iteration'      )		,     1      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'my_text'      )		,     'some_value'      )






class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				str						  =    [
				    "total_loss",
				    "iteration",
				    "my_text",
				    "learning_rate",
				    "num_iterations",
				    "some_boolean",
				    "some_string",
				]

				UpperCAmelCase_    :				Optional[int]						  =    "my_custom_tracker"
				UpperCAmelCase_    :				Union[str, Any]						  =    False


				def __init__(    self		,     lowercase__      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     open(f'''{dir}/log.csv'''		,     'w+'      )
									SCREAMING_SNAKE_CASE           =     csv.DictWriter(self.f		,     fieldnames=self._col_names      )
									self.writer.writeheader()


				@property
				def 							A (    self      )   ->   int:
									"""simple docstring"""







									return self.writer


				def 							A (    self		,     lowercase__      )   ->   List[str]:
									"""simple docstring"""







									logger.info('Call init'      )
									self.writer.writerow(lowercase__      )


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







									logger.info('Call log'      )
									self.writer.writerow(lowercase__      )




				def 							A (    self      )   ->   int:
									"""simple docstring"""







									self.f.close()






class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   int:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     MyCustomTracker(lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers('Some name'		,     lowercase__      )
														accelerator.end_training()
														with open(f'''{d}/log.csv'''		,     'r'      ) as f:
																			SCREAMING_SNAKE_CASE           =     csv.DictReader(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     next(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     {
																			    'total_loss': '',
																			    'iteration': '',
																			    'my_text': '',
																			    'learning_rate': '0.01',
																			    'num_iterations': '12',
																			    'some_boolean': 'False',
																			    'some_string': 'some_value',
																			}
																			self.assertDictEqual(lowercase__		,     lowercase__      )




				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     MyCustomTracker(lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														accelerator.init_trackers('Some name'      )
														SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														with open(f'''{d}/log.csv'''		,     'r'      ) as f:
																			SCREAMING_SNAKE_CASE           =     csv.DictReader(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     next(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     {
																			    'total_loss': '0.1',
																			    'iteration': '1',
																			    'my_text': 'some_value',
																			    'learning_rate': '',
																			    'num_iterations': '',
																			    'some_boolean': '',
																			    'some_string': '',
																			}
																			self.assertDictEqual(lowercase__		,     lowercase__      )


"""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
