





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[str]							=			logging.get_logger(__name__)






class  _UpperCAmelCase       (     __A			):
					UpperCamelCase								=							4_2
					UpperCamelCase								=							4_2
					UpperCamelCase								=							'''PyTorch'''


					@property
					def      lowerCamelCase	( self      :Union[str, Any]		):
												return torch.__version__


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int		):
												A		=  self._prepare_inference_func(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)
												return self._measure_speed(_inference		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int		):
												A		=  self._prepare_inference_func(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)
												return self._measure_memory(_inference		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int		):
												A		=  self._prepare_train_func(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)
												return self._measure_speed(_train		)


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int		):
												A		=  self._prepare_train_func(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)
												return self._measure_memory(_train		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int		):
												A		=  self.config_dict[model_name]

												if self.args.torchscript:
																			A		=  True

												A		=  (
												    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:
																										A		=  config.architectures[0]
																										A		=  __import__("transformers"					,	fromlist=[model_class]		)
																										A		=  getattr(__UpperCamelCase					,	__UpperCamelCase		)
																										A		=  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:
																			A		=  MODEL_MAPPING[config.__class__](__UpperCamelCase		)

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

												# encoder-decoder has vocab size saved differently
												A		=  config.vocab_size if hasattr(__UpperCamelCase					,	"vocab_size"		) else config.encoder.vocab_size
												A		=  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():
																										A		=  torch.jit.trace(__UpperCamelCase					,	__UpperCamelCase		)
												else:
																			A		=  model

												def encoder_decoder_forward():
																			with torch.no_grad():
																										A		=  inference_model(__UpperCamelCase					,	decoder_input_ids=__UpperCamelCase		)
																			return outputs

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

												A		=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
												return _forward


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int		):
												A		=  self.config_dict[model_name]

												A		=  (
												    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:
																										A		=  config.architectures[0]
																										A		=  __import__("transformers"					,	fromlist=[model_class]		)
																										A		=  getattr(__UpperCamelCase					,	__UpperCamelCase		)
																										A		=  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:
																			A		=  MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase		)

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

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

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

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

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


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Optional[Any]		):

												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
																			A		=  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		) / 10.0
												except RuntimeError as e:
																			self.print_fn(f"Doesn't fit on GPU. {e}"		)
																			return "N/A"


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Callable[[], None]		):

												try:
																			if self.args.trace_memory_line_by_line:
																										A		=  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."		)
																																	A		=  "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()
																																	A		=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
																																	A		=  nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase		)
																																	A		=  meminfo.used
																																	A		=  Memory(__UpperCamelCase		)
																																	# shutdown nvml
																																	nvml.nvmlShutdown()
																			else:
																										# cpu
																										A		=  measure_peak_memory_cpu(__UpperCamelCase		)
																										A		=  Memory(__UpperCamelCase		) if isinstance(__UpperCamelCase					,	__UpperCamelCase		) else memory_bytes

																			if self.args.trace_memory_line_by_line:
																										A		=  stop_memory_tracing(__UpperCamelCase		)
																			else:
																										A		=  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 argparse
import os
import sys
import urllib.request
import zipfile


_snake_case					:		Any							=			['CoLA', 'SST', 'MRPC', 'QQP', 'STS', 'MNLI', 'SNLI', 'QNLI', 'RTE', 'WNLI', 'diagnostic']
_snake_case					:		List[str]							=			{
    'CoLA': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4',
    'SST': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8',
    'MRPC': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc',
    'QQP': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5',
    'STS': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5',
    'MNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce',
    'SNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df',
    'QNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601',
    'RTE': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb',
    'WNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf',
    'diagnostic': 'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D',
}

_snake_case					:		List[str]							=			'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'
_snake_case					:		List[str]							=			'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							print(F"Downloading and extracting {task}..."	)
							A		=  F"{task}.zip"
							urllib.request.urlretrieve(TASKaPATH[task] ,  UpperCamelCase	)
							with zipfile.ZipFile(UpperCamelCase	) as zip_ref:
														zip_ref.extractall(UpperCamelCase	)
							os.remove(UpperCamelCase	)
							print("\tCompleted!"	)






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							print("Processing MRPC..."	)
							A		=  os.path.join(UpperCamelCase ,  "MRPC"	)
							if not os.path.isdir(UpperCamelCase	):
														os.mkdir(UpperCamelCase	)
							if path_to_data:
														A		=  os.path.join(UpperCamelCase ,  "msr_paraphrase_train.txt"	)
														A		=  os.path.join(UpperCamelCase ,  "msr_paraphrase_test.txt"	)
							else:
														print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN	)
														A		=  os.path.join(UpperCamelCase ,  "msr_paraphrase_train.txt"	)
														A		=  os.path.join(UpperCamelCase ,  "msr_paraphrase_test.txt"	)
														urllib.request.urlretrieve(UpperCamelCase ,  UpperCamelCase	)
														urllib.request.urlretrieve(UpperCamelCase ,  UpperCamelCase	)
							if not os.path.isfile(UpperCamelCase	):
														raise ValueError(F"Train data not found at {mrpc_train_file}"	)
							if not os.path.isfile(UpperCamelCase	):
														raise ValueError(F"Test data not found at {mrpc_test_file}"	)
							urllib.request.urlretrieve(TASKaPATH["MRPC"] ,  os.path.join(UpperCamelCase ,  "dev_ids.tsv"	)	)

							A		=  []
							with open(os.path.join(UpperCamelCase ,  "dev_ids.tsv"	) ,  encoding="utf8"	) as ids_fh:
														for row in ids_fh:
																					dev_ids.append(row.strip().split("\t"	)	)

							with open(UpperCamelCase ,  encoding="utf8"	) as data_fh, open(
							    os.path.join(UpperCamelCase ,  "train.tsv"	) ,  "w" ,  encoding="utf8"	) as train_fh, open(os.path.join(UpperCamelCase ,  "dev.tsv"	) ,  "w" ,  encoding="utf8"	) as dev_fh:
														A		=  data_fh.readline()
														train_fh.write(UpperCamelCase	)
														dev_fh.write(UpperCamelCase	)
														for row in data_fh:
																					A,     A,     A,     A,     A		=  row.strip().split("\t"	)
																					if [ida, ida] in dev_ids:
																												dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, ida, ida, sa, sa)	)
																					else:
																												train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, ida, ida, sa, sa)	)

							with open(UpperCamelCase ,  encoding="utf8"	) as data_fh, open(
							    os.path.join(UpperCamelCase ,  "test.tsv"	) ,  "w" ,  encoding="utf8"	) as test_fh:
														A		=  data_fh.readline()
														test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n"	)
														for idx, row in enumerate(UpperCamelCase	):
																					A,     A,     A,     A,     A		=  row.strip().split("\t"	)
																					test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, ida, ida, sa, sa)	)
							print("\tCompleted!"	)






def  A__    (      UpperCamelCase	):
							print("Downloading and extracting diagnostic..."	)
							if not os.path.isdir(os.path.join(UpperCamelCase ,  "diagnostic"	)	):
														os.mkdir(os.path.join(UpperCamelCase ,  "diagnostic"	)	)
							A		=  os.path.join(UpperCamelCase ,  "diagnostic" ,  "diagnostic.tsv"	)
							urllib.request.urlretrieve(TASKaPATH["diagnostic"] ,  UpperCamelCase	)
							print("\tCompleted!"	)
							return






def  A__    (      UpperCamelCase	):
							A		=  task_names.split(","	)
							if "all" in task_names:
														A		=  TASKS
							else:
														A		=  []
														for task_name in task_names:
																					if task_name not in TASKS:
																												raise ValueError(F"Task {task_name} not found!"	)
																					tasks.append(UpperCamelCase	)
							return tasks






def  A__    (      UpperCamelCase	):
							A		=  argparse.ArgumentParser()
							parser.add_argument("--data_dir" ,  help="directory to save data to" ,  type=UpperCamelCase ,  default="glue_data"	)
							parser.add_argument(
							    "--tasks" ,  help="tasks to download data for as a comma separated string" ,  type=UpperCamelCase ,  default="all"	)
							parser.add_argument(
							    "--path_to_mrpc" ,  help="path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt" ,  type=UpperCamelCase ,  default="" ,  )
							A		=  parser.parse_args(UpperCamelCase	)

							if not os.path.isdir(args.data_dir	):
														os.mkdir(args.data_dir	)
							A		=  get_tasks(args.tasks	)

							for task in tasks:
														if task == "MRPC":
																					format_mrpc(args.data_dir ,  args.path_to_mrpc	)
														elif task == "diagnostic":
																					download_diagnostic(args.data_dir	)
														else:
																					download_and_extract(UpperCamelCase ,  args.data_dir	)


if __name__ == "__main__":
		sys.exit(main(sys.argv[1:]))


