


'''simple docstring'''


# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


lowerCamelCase__    	=     float('nan')






class 	lowerCAmelCase__			:




			def __init__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     Any )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      sys.stdout
					_UpperCAmelCase		:       Optional[int]								=      open(__snake_case	,  "a" )




			def __getattr__(		self  :     str	,  lowerCamelCase__  :     List[str] )   ->Optional[int]:



					'''simple docstring'''







					return getattr(self.stdout	,  __snake_case )






			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					self.stdout.write(__snake_case )
					# strip tqdm codes
					self.file.write(re.sub(R"^.*\r"	,  ""	,  __snake_case	,  0	,  re.M ) )






def  __lowerCAmelCase (__lowerCAmelCase=80      , __lowerCAmelCase=False   )     ->   str:
		_UpperCAmelCase		:       Tuple								=      []

		# deal with critical env vars
		_UpperCAmelCase		:       Dict								=      ['''CUDA_VISIBLE_DEVICES''']
		for key in env_keys:
				_UpperCAmelCase		:       Optional[int]								=      os.environ.get(a_      , a_   )
				if val is not None:
						cmd.append(F"""{key}={val}"""   )

    # python executable (not always needed if the script is executable)
		_UpperCAmelCase		:       str								=      sys.executable if full_python_path else sys.executable.split("/"   )[-1]
		cmd.append(a_   )

		# now the normal args
		cmd += list(map(shlex.quote      , sys.argv   )   )

		# split up into up to MAX_WIDTH lines with shell multi-line escapes
		_UpperCAmelCase		:       List[Any]								=      []
		_UpperCAmelCase		:       Optional[int]								=      ''''''
		while len(a_   ) > 0:
				current_line += F"""{cmd.pop(0   )} """
				if len(a_   ) == 0 or len(a_   ) + len(cmd[0]   ) + 1 > max_width - 1:
						lines.append(a_   )
						_UpperCAmelCase		:       List[Any]								=      ''''''
		return "\\\n".join(a_   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   )     ->   Union[str, Any]:
		# unwrap multi-line input
		_UpperCAmelCase		:       Optional[int]								=      re.sub(R"[\\\n]+"      , " "      , args.base_cmd   )

		# remove --output_dir if any and set our own
		_UpperCAmelCase		:       int								=      re.sub("--output_dir\s+[^\s]+"      , ""      , args.base_cmd   )
		args.base_cmd += F""" --output_dir {output_dir}"""

		# ensure we have --overwrite_output_dir
		_UpperCAmelCase		:       Optional[Any]								=      re.sub("--overwrite_output_dir\s+"      , ""      , args.base_cmd   )
		args.base_cmd += " --overwrite_output_dir"

		return [sys.executable] + shlex.split(args.base_cmd   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )     ->   Dict:
		# Enable to debug everything but the run itself, to do it fast and see the progress.
		# This is useful for debugging the output formatting quickly - we can remove it later once
		# everybody is happy with the output
		if 0:
				import random
				from time import sleep

				sleep(0   )
				return dict(
				    {k: random.uniform(0      , 100   ) for k in metric_keys}      , **{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2]   )}      , )

		_UpperCAmelCase		:       Union[str, Any]								=      subprocess.run(a_      , capture_output=a_      , text=a_   )

		if verbose:
				print("STDOUT"      , result.stdout   )
				print("STDERR"      , result.stderr   )

		# save the streams
		_UpperCAmelCase		:       Dict								=      variation.replace(" "      , "-"   )
		with open(Path(a_   ) / F"""log.{prefix}.stdout.txt"""      , "w"   ) as f:
				f.write(result.stdout   )
		with open(Path(a_   ) / F"""log.{prefix}.stderr.txt"""      , "w"   ) as f:
				f.write(result.stderr   )

		if result.returncode != 0:
				if verbose:
						print("failed"   )
				return {target_metric_key: nan}

		with io.open(F"""{output_dir}/all_results.json"""      , "r"      , encoding="utf-8"   ) as f:
				_UpperCAmelCase		:       List[str]								=      json.load(a_   )

		# filter out just the keys we want
		return {k: v for k, v in metrics.items() if k in metric_keys}






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , )     ->   Union[str, Any]:
		_UpperCAmelCase		:       Dict								=      []
		_UpperCAmelCase		:       List[Any]								=      []
		_UpperCAmelCase		:       Any								=      F"""{id}: {variation:<{longest_variation_len}}"""
		_UpperCAmelCase		:       int								=      F"""{preamble}: """
		_UpperCAmelCase		:       str								=      set(report_metric_keys + [target_metric_key]   )
		for i in tqdm(range(a_   )      , desc=a_      , leave=a_   ):
				_UpperCAmelCase		:       Union[str, Any]								=      process_run_single(
				    a_      , a_      , a_      , a_      , a_      , a_      , a_   )
				_UpperCAmelCase		:       Optional[Any]								=      single_run_metrics[target_metric_key]
				if not math.isnan(a_   ):
						metrics.append(a_   )
						results.append(a_   )
						outcome += "✓"
				else:
						outcome += "✘"
		_UpperCAmelCase		:       List[str]								=      F"""\33[2K\r{outcome}"""
		if len(a_   ) > 0:
				_UpperCAmelCase		:       Tuple								=      {k: fmean([x[k] for x in metrics]   ) for k in metrics[0].keys()}
				_UpperCAmelCase		:       Union[str, Any]								=      round(mean_metrics[target_metric_key]      , 2   )
				_UpperCAmelCase		:       Optional[int]								=      F"""{outcome} {mean_target}"""
				if len(a_   ) > 1:
						results_str += F""" {tuple(round(a_      , 2   ) for x in results   )}"""
				print(a_   )
				_UpperCAmelCase		:       Union[str, Any]								=      variation
				return mean_metrics
		else:
				print(a_   )
				return {variation_key: variation, target_metric_key: nan}






def  __lowerCAmelCase ()     ->   Dict:
		_UpperCAmelCase		:       List[str]								=      torch.cuda.get_device_properties(torch.device("cuda"   )   )
		return F"""\nDatetime    : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'   )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n"""






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )     ->   Tuple:
		_UpperCAmelCase		:       Any								=      pd.DataFrame(a_   )
		_UpperCAmelCase		:       Any								=      '''variation'''
		_UpperCAmelCase		:       List[Any]								=      '''diff_%'''

		_UpperCAmelCase		:       Any								=      nan
		if base_variation is not None and len(df[df[variation_key] == base_variation]   ):
				# this may still return nan
				_UpperCAmelCase		:       Tuple								=      df.loc[df[variation_key] == base_variation][target_metric_key].item()
		if math.isnan(a_   ):
				# as a fallback, use the minimal value as the sentinel
				_UpperCAmelCase		:       Optional[int]								=      df.loc[df[target_metric_key] != nan][target_metric_key].min()

		# create diff column if possible
		if not math.isnan(a_   ):
				_UpperCAmelCase		:       Dict								=      df.apply(
				    lambda __lowerCAmelCase   : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value   )
				    if not math.isnan(r[target_metric_key]   )
				    else 0      , axis="columns"      , )

		# re-order columns
		_UpperCAmelCase		:       Tuple								=      [variation_key, target_metric_key, diff_key, *report_metric_keys]
		_UpperCAmelCase		:       Dict								=      df.reindex(a_      , axis="columns"   )  # reorder cols

		# capitalize
		_UpperCAmelCase		:       List[str]								=      df.rename(str.capitalize      , axis="columns"   )

		# make the cols as narrow as possible
		_UpperCAmelCase		:       Optional[Any]								=      df.rename(lambda __lowerCAmelCase   : c.replace("_"      , "<br>"   )      , axis="columns"   )
		_UpperCAmelCase		:       Union[str, Any]								=      df.rename(lambda __lowerCAmelCase   : c.replace("_"      , "\n"   )      , axis="columns"   )

		_UpperCAmelCase		:       Any								=      ['''''', '''Copy between the cut-here-lines and paste as is to github or a forum''']
		report += ["----------8<-----------------8<--------"]
		report += ["*** Results:", df_github.to_markdown(index=a_      , floatfmt=".2f"   )]
		report += ["```"]
		report += ["*** Setup:", get_versions()]
		report += ["*** The benchmark command line was:", get_original_command()]
		report += ["```"]
		report += ["----------8<-----------------8<--------"]
		report += ["*** Results (console):", df_console.to_markdown(index=a_      , floatfmt=".2f"   )]

		print("\n\n".join(a_   )   )






def  __lowerCAmelCase ()     ->   Any:
		_UpperCAmelCase		:       Dict								=      argparse.ArgumentParser()
		parser.add_argument(
		    "--base-cmd"      , default=a_      , type=a_      , required=a_      , help="Base cmd"      , )
		parser.add_argument(
		    "--variations"      , default=a_      , type=a_      , nargs="+"      , required=a_      , help="Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'"      , )
		parser.add_argument(
		    "--base-variation"      , default=a_      , type=a_      , help="Baseline variation to compare to. if None the minimal target value will be used to compare against"      , )
		parser.add_argument(
		    "--target-metric-key"      , default=a_      , type=a_      , required=a_      , help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"      , )
		parser.add_argument(
		    "--report-metric-keys"      , default=""      , type=a_      , help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples"      , )
		parser.add_argument(
		    "--repeat-times"      , default=1      , type=a_      , help="How many times to re-run each variation - an average will be reported"      , )
		parser.add_argument(
		    "--output_dir"      , default="output_benchmark"      , type=a_      , help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"      , )
		parser.add_argument(
		    "--verbose"      , default=a_      , action="store_true"      , help="Whether to show the outputs of each run or just the benchmark progress"      , )
		_UpperCAmelCase		:       Optional[Any]								=      parser.parse_args()

		_UpperCAmelCase		:       List[Any]								=      args.output_dir
		Path(a_   ).mkdir(exist_ok=a_   )
		_UpperCAmelCase		:       List[str]								=      get_base_command(a_      , a_   )

		# split each dimension into its --foo variations
		_UpperCAmelCase		:       int								=      [list(map(str.strip      , re.split(R"\|"      , a_   )   )   ) for x in args.variations]
		# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
		# while stripping white space for inputs that were empty
		_UpperCAmelCase		:       Optional[int]								=      list(map(str.strip      , map(" ".join      , itertools.product(*a_   )   )   )   )
		_UpperCAmelCase		:       Tuple								=      max(len(a_   ) for x in variations   )

		# split wanted keys
		_UpperCAmelCase		:       Tuple								=      args.report_metric_keys.split()

		# capture prints into a log file for convenience
		_UpperCAmelCase		:       Dict								=      F"""benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S'   )}.txt"""
		print(F"""\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt"""   )
		print(F"""and this script's output is also piped into {report_fn}"""   )

		_UpperCAmelCase		:       Optional[Any]								=      Tee(a_   )

		print(F"""\n*** Running {len(a_   )} benchmarks:"""   )
		print(F"""Base command: {' '.join(a_   )}"""   )

		_UpperCAmelCase		:       Dict								=      '''variation'''
		_UpperCAmelCase		:       Optional[Any]								=      []
		for id, variation in enumerate(tqdm(a_      , desc="Total completion: "      , leave=a_   )   ):
				_UpperCAmelCase		:       int								=      base_cmd + variation.split()
				results.append(
				    process_run(
				        id + 1      , a_      , a_      , a_      , a_      , args.target_metric_key      , a_      , args.repeat_times      , a_      , args.verbose      , )   )

		process_results(a_      , args.target_metric_key      , a_      , args.base_variation      , a_   )


if __name__ == "__main__":
						main()







'''simple docstring'''


import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__    	=     16
lowerCamelCase__    	=     32






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return int(x / 2**20   )






class 	lowerCAmelCase__			:




			def __enter__(		self  :     int )   ->Optional[Any]:



					'''simple docstring'''







					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_UpperCAmelCase		:       Tuple								=      torch.cuda.memory_allocated()
					return self






			def __exit__(		self  :     Tuple	,  *lowerCamelCase__  :     str )   ->int:



					'''simple docstring'''







					gc.collect()
					torch.cuda.empty_cache()
					_UpperCAmelCase		:       List[str]								=      torch.cuda.memory_allocated()
					_UpperCAmelCase		:       Tuple								=      torch.cuda.max_memory_allocated()
					_UpperCAmelCase		:       List[Any]								=      bamb(self.end - self.begin )
					_UpperCAmelCase		:       int								=      bamb(self.peak - self.begin )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = 16      , __lowerCAmelCase = "bert-base-cased"      , __lowerCAmelCase = 320      , __lowerCAmelCase = 160      , ):
		_UpperCAmelCase		:       int								=      AutoTokenizer.from_pretrained(__lowerCAmelCase   )
		_UpperCAmelCase		:       Any								=      load_dataset(
		    "glue"      , "mrpc"      , split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

		def tokenize_function(__lowerCAmelCase   ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCAmelCase		:       List[Any]								=      tokenizer(examples["sentence1"]      , examples["sentence2"]      , truncation=__lowerCAmelCase      , max_length=__lowerCAmelCase   )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		_UpperCAmelCase		:       int								=      datasets.map(
		    __lowerCAmelCase      , batched=__lowerCAmelCase      , remove_columns=["idx", "sentence1", "sentence2"]      , load_from_cache_file=__lowerCAmelCase   )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		_UpperCAmelCase		:       List[Any]								=      tokenized_datasets.rename_column("label"      , "labels"   )

		def collate_fn(__lowerCAmelCase   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(__lowerCAmelCase      , padding="max_length"      , max_length=128      , return_tensors="pt"   )
				return tokenizer.pad(__lowerCAmelCase      , padding="longest"      , return_tensors="pt"   )

		# Instantiate dataloaders.
		_UpperCAmelCase		:       Any								=      DataLoader(
		    tokenized_datasets["train"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )
		_UpperCAmelCase		:       List[str]								=      DataLoader(
		    tokenized_datasets["validation"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )

		return train_dataloader, eval_dataloader






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		# Initialize accelerator
		_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_UpperCAmelCase		:       List[Any]								=      config["lr"]
		_UpperCAmelCase		:       List[Any]								=      int(config["num_epochs"]   )
		_UpperCAmelCase		:       int								=      int(config["seed"]   )
		_UpperCAmelCase		:       Union[str, Any]								=      int(config["batch_size"]   )
		_UpperCAmelCase		:       Tuple								=      args.model_name_or_path

		set_seed(__lowerCAmelCase   )
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      get_dataloaders(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , args.n_train      , args.n_val   )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCAmelCase		:       Optional[int]								=      AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase      , return_dict=__lowerCAmelCase   )

		# Instantiate optimizer
		_UpperCAmelCase		:       Dict								=      (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_UpperCAmelCase		:       str								=      optimizer_cls(params=model.parameters()      , lr=__lowerCAmelCase   )

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCAmelCase		:       Any								=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				_UpperCAmelCase		:       Any								=      1
		_UpperCAmelCase		:       Optional[int]								=      (len(__lowerCAmelCase   ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_UpperCAmelCase		:       Tuple								=      get_linear_schedule_with_warmup(
				    optimizer=__lowerCAmelCase      , num_warmup_steps=0      , num_training_steps=__lowerCAmelCase      , )
		else:
				_UpperCAmelCase		:       Optional[Any]								=      DummyScheduler(__lowerCAmelCase      , total_num_steps=__lowerCAmelCase      , warmup_num_steps=0   )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      accelerator.prepare(
		    __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

		# We need to keep track of how many total steps we have iterated over
		_UpperCAmelCase		:       Union[str, Any]								=      0
		# We also need to keep track of the stating epoch so files are named properly
		_UpperCAmelCase		:       str								=      0

		# Now we train the model
		_UpperCAmelCase		:       Optional[Any]								=      {}
		for epoch in range(__lowerCAmelCase      , __lowerCAmelCase   ):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(__lowerCAmelCase   ):
								_UpperCAmelCase		:       Union[str, Any]								=      model(**__lowerCAmelCase   )
								_UpperCAmelCase		:       Union[str, Any]								=      outputs.loss
								_UpperCAmelCase		:       List[Any]								=      loss / gradient_accumulation_steps
								accelerator.backward(__lowerCAmelCase   )
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()

								overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
				accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
				accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
				accelerator.print(
				    "Total Peak Memory consumed during the train (max): {}".format(
				        tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
				_UpperCAmelCase		:       Optional[int]								=      tracemalloc.peaked + bamb(tracemalloc.begin   )
				if args.peak_memory_upper_bound is not None:
						assert (
						    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
						), "Peak memory usage exceeded the upper bound"

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir      , "peak_memory_utilization.json"   )      , "w"   ) as f:
						json.dump(__lowerCAmelCase      , __lowerCAmelCase   )






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Any								=      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
		parser.add_argument(
		    "--model_name_or_path"      , type=__lowerCAmelCase      , default="bert-base-cased"      , help="Path to pretrained model or model identifier from huggingface.co/models."      , required=__lowerCAmelCase      , )
		parser.add_argument(
		    "--output_dir"      , type=__lowerCAmelCase      , default="."      , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      , )
		parser.add_argument(
		    "--peak_memory_upper_bound"      , type=__lowerCAmelCase      , default=__lowerCAmelCase      , help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."      , )
		parser.add_argument(
		    "--n_train"      , type=__lowerCAmelCase      , default=320      , help="Number of training examples to use."      , )
		parser.add_argument(
		    "--n_val"      , type=__lowerCAmelCase      , default=160      , help="Number of validation examples to use."      , )
		parser.add_argument(
		    "--num_epochs"      , type=__lowerCAmelCase      , default=1      , help="Number of train epochs."      , )
		_UpperCAmelCase		:       Tuple								=      parser.parse_args()
		_UpperCAmelCase		:       Optional[Any]								=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
		training_function(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
						main()



