



# 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    :		Optional[int]		      =    float("""nan""")


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[int]       ,	snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				sys.stdout
											SCREAMING_SNAKE_CASE							:     List[str]						=				open(snake_case       ,	'a'	)







						def __getattr__(  self    :				Any       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											return getattr(self.stdout       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				Dict	):



											'''simple docstring'''


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


def        __a			(   __lowerCAmelCase=80      ,	__lowerCAmelCase=False   )     ->     Any:
					SCREAMING_SNAKE_CASE							:     Tuple						=				[]

					# deal with critical env vars
					SCREAMING_SNAKE_CASE							:     List[Any]						=				['CUDA_VISIBLE_DEVICES']
					for key in env_keys:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				os.environ.get(__lowerCAmelCase      ,	__lowerCAmelCase   )
										if val is not None:
															cmd.append(F'''{key}={val}'''   )

    # python executable (not always needed if the script is executable)
					SCREAMING_SNAKE_CASE							:     Any						=				sys.executable if full_python_path else sys.executable.split('/'   )[-1]
					cmd.append(__lowerCAmelCase   )

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

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

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[int]:
					# unwrap multi-line input
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				re.sub(r'[\\\n]+'      ,	' '      ,	args.base_cmd   )

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

					# ensure we have --overwrite_output_dir
					SCREAMING_SNAKE_CASE							:     List[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        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Any:
					# 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, 10.31, 100.2, 55.6_666, 222.22_222_222]   )}      ,	)

					SCREAMING_SNAKE_CASE							:     Any						=				subprocess.run(__lowerCAmelCase      ,	capture_output=__lowerCAmelCase      ,	text=__lowerCAmelCase   )

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

					# save the streams
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				variation.replace(' '      ,	'-'   )
					with open(Path(__lowerCAmelCase   ) / F'''log.{prefix}.stdout.txt'''      ,	'w'   ) as f:
										f.write(result.stdout   )
					with open(Path(__lowerCAmelCase   ) / 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:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				json.load(__lowerCAmelCase   )

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

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

def        __a			(   )     ->     Dict:
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.cuda.get_device_properties(torch.device('cuda'   )   )
					return F'''
Datetime    : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'   )}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				pd.DataFrame(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				'variation'
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'diff_%'

					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				nan
					if base_variation is not None and len(df[df[variation_key] == base_variation]   ):
										# this may still return nan
										SCREAMING_SNAKE_CASE							:     List[str]						=				df.loc[df[variation_key] == base_variation][target_metric_key].item()
					if math.isnan(__lowerCAmelCase   ):
										# as a fallback, use the minimal value as the sentinel
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				df.loc[df[target_metric_key] != nan][target_metric_key].min()

					# create diff column if possible
					if not math.isnan(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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
					SCREAMING_SNAKE_CASE							:     int						=				[variation_key, target_metric_key, diff_key, *report_metric_keys]
					SCREAMING_SNAKE_CASE							:     Dict						=				df.reindex(__lowerCAmelCase      ,	axis='columns'   )  # reorder cols

					# capitalize
					SCREAMING_SNAKE_CASE							:     Tuple						=				df.rename(str.capitalize      ,	axis='columns'   )

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

					SCREAMING_SNAKE_CASE							:     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=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	floatfmt='.2f'   )]

					print('\n\n'.join(__lowerCAmelCase   )   )

def        __a			(   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				argparse.ArgumentParser()
					parser.add_argument(
					    '--base-cmd'      ,	default=__lowerCAmelCase      ,	type=__lowerCAmelCase      ,	required=__lowerCAmelCase      ,	help='Base cmd'      ,	)
					parser.add_argument(
					    '--variations'      ,	default=__lowerCAmelCase      ,	type=__lowerCAmelCase      ,	nargs='+'      ,	required=__lowerCAmelCase      ,	help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''      ,	)
					parser.add_argument(
					    '--base-variation'      ,	default=__lowerCAmelCase      ,	type=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	type=__lowerCAmelCase      ,	required=__lowerCAmelCase      ,	help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'      ,	)
					parser.add_argument(
					    '--report-metric-keys'      ,	default=''      ,	type=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	help='How many times to re-run each variation - an average will be reported'      ,	)
					parser.add_argument(
					    '--output_dir'      ,	default='output_benchmark'      ,	type=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	action='store_true'      ,	help='Whether to show the outputs of each run or just the benchmark progress'      ,	)
					SCREAMING_SNAKE_CASE							:     Tuple						=				parser.parse_args()

					SCREAMING_SNAKE_CASE							:     str						=				args.output_dir
					Path(__lowerCAmelCase   ).mkdir(exist_ok=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				get_base_command(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# split each dimension into its --foo variations
					SCREAMING_SNAKE_CASE							:     List[str]						=				[list(map(str.strip      ,	re.split(r'\|'      ,	__lowerCAmelCase   )   )   ) 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
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				list(map(str.strip      ,	map(' '.join      ,	itertools.product(*__lowerCAmelCase   )   )   )   )
					SCREAMING_SNAKE_CASE							:     Any						=				max(len(__lowerCAmelCase   ) for x in variations   )

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

					# capture prints into a log file for convenience
					SCREAMING_SNAKE_CASE							:     str						=				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}'''   )

					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Tee(__lowerCAmelCase   )

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

					SCREAMING_SNAKE_CASE							:     Any						=				'variation'
					SCREAMING_SNAKE_CASE							:     List[str]						=				[]
					for id, variation in enumerate(tqdm(__lowerCAmelCase      ,	desc='Total completion: '      ,	leave=__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     List[Any]						=				base_cmd + variation.split()
										results.append(
										    process_run(
										        id + 1      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	args.target_metric_key      ,	__lowerCAmelCase      ,	args.repeat_times      ,	__lowerCAmelCase      ,	args.verbose      ,	)   )

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


if __name__ == "__main__":
			main()




from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()