

# 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     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A							):
												__lowerCAmelCase					=							sys.stdout
												__lowerCAmelCase					=							open(_A					,						'a'							)




							def __getattr__( self					,						_A							):
												return getattr(self.stdout					,						_A							)




							def        A__  ( self					,						_A							):
												self.stdout.write(_A							)
												# strip tqdm codes
												self.file.write(re.sub(R'^.*\r'					,						''					,						_A					,						0					,						re.M							)							)


def        __lowercase    (  UpperCAmelCase__=80		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]

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

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

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

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							re.sub(R'[\\\n]+'		,					' '		,					args.base_cmd      )

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

					# ensure we have --overwrite_output_dir
					__lowerCAmelCase					=							re.sub('--overwrite_output_dir\s+'		,					''		,					args.base_cmd      )
					args.base_cmd += " --overwrite_output_dir"

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					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, 55.6666, 222.22222222]      )}		,					)

					__lowerCAmelCase					=							subprocess.run(UpperCAmelCase__		,					capture_output=UpperCAmelCase__		,					text=UpperCAmelCase__      )

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

					# save the streams
					__lowerCAmelCase					=							variation.replace(' '		,					'-'      )
					with open(Path(UpperCAmelCase__      ) / F"""log.{prefix}.stdout.txt"""		,					'w'      ) as f:
										f.write(result.stdout      )
					with open(Path(UpperCAmelCase__      ) / 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:
										__lowerCAmelCase					=							json.load(UpperCAmelCase__      )

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					):

					"""simple docstring"""






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


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							pd.DataFrame(UpperCAmelCase__      )
					__lowerCAmelCase					=							'variation'
					__lowerCAmelCase					=							'diff_%'

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

					# create diff column if possible
					if not math.isnan(UpperCAmelCase__      ):
										__lowerCAmelCase					=							df.apply(
										    lambda UpperCAmelCase__      : 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
					__lowerCAmelCase					=							[variation_key, target_metric_key, diff_key, *report_metric_keys]
					__lowerCAmelCase					=							df.reindex(UpperCAmelCase__		,					axis='columns'      )  # reorder cols

					# capitalize
					__lowerCAmelCase					=							df.rename(str.capitalize		,					axis='columns'      )

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

					__lowerCAmelCase					=							['', '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=UpperCAmelCase__		,					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=UpperCAmelCase__		,					floatfmt='.2f'      )]

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


def        __lowercase    (  ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							args.output_dir
					Path(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
					__lowerCAmelCase					=							get_base_command(UpperCAmelCase__		,					UpperCAmelCase__      )

					# split each dimension into its --foo variations
					__lowerCAmelCase					=							[list(map(str.strip		,					re.split(R'\|'		,					UpperCAmelCase__      )      )      ) 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
					__lowerCAmelCase					=							list(map(str.strip		,					map(' '.join		,					itertools.product(*UpperCAmelCase__      )      )      )      )
					__lowerCAmelCase					=							max(len(UpperCAmelCase__      ) for x in variations      )

					# split wanted keys
					__lowerCAmelCase					=							args.report_metric_keys.split()

					# capture prints into a log file for convenience
					__lowerCAmelCase					=							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}"""      )

					__lowerCAmelCase					=							Tee(UpperCAmelCase__      )

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

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

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


if __name__ == "__main__":
		main()









from __future__ import annotations

from collections.abc import MutableSequence





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												if len(_A							) != degree + 1:
																	raise ValueError(
																	    'The number of coefficients should be equal to the degree + 1.'							)

												__lowerCAmelCase					=							list(_A							)
												__lowerCAmelCase					=							degree




							def __add__( self					,						_A							):

												if self.degree > polynomial_a.degree:
																	__lowerCAmelCase					=							self.coefficients[:]
																	for i in range(polynomial_a.degree + 1							):
																						coefficients[i] += polynomial_a.coefficients[i]
																	return Polynomial(self.degree					,						_A							)
												else:
																	__lowerCAmelCase					=							polynomial_a.coefficients[:]
																	for i in range(self.degree + 1							):
																						coefficients[i] += self.coefficients[i]
																	return Polynomial(polynomial_a.degree					,						_A							)




							def __sub__( self					,						_A							):
												return self + polynomial_a * Polynomial(0					,						[-1]							)




							def __neg__( self							):
												return Polynomial(self.degree					,						[-c for c in self.coefficients]							)




							def __mul__( self					,						_A							):
												__lowerCAmelCase					=							[0] * (self.degree + polynomial_a.degree + 1)
												for i in range(self.degree + 1							):
																	for j in range(polynomial_a.degree + 1							):
																						coefficients[i + j] += (
																						    self.coefficients[i] * polynomial_a.coefficients[j]
																						)

												return Polynomial(self.degree + polynomial_a.degree					,						_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							0
												for i in range(self.degree + 1							):
																	result += self.coefficients[i] * (substitution**i)
												return result




							def __str__( self							):
												__lowerCAmelCase					=							''
												for i in range(self.degree					,						-1					,						-1							):
																	if self.coefficients[i] == 0:
																						continue
																	elif self.coefficients[i] > 0:
																						if polynomial:
																											polynomial += " + "
																	else:
																						polynomial += " - "

																	if i == 0:
																						polynomial += str(abs(self.coefficients[i]							)							)
																	elif i == 1:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x"
																	else:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x^" + str(_A							)

												return polynomial




							def __repr__( self							):
												return self.__str__()




							def        A__  ( self							):
												__lowerCAmelCase					=							[0] * self.degree
												for i in range(self.degree							):
																	__lowerCAmelCase					=							self.coefficients[i + 1] * (i + 1)
												return Polynomial(self.degree - 1					,						_A							)




							def        A__  ( self					,						_A = 0							):
												__lowerCAmelCase					=							[0] * (self.degree + 2)
												__lowerCAmelCase					=							constant
												for i in range(self.degree + 1							):
																	__lowerCAmelCase					=							self.coefficients[i] / (i + 1)
												return Polynomial(self.degree + 1					,						_A							)




							def __eq__( self					,						_A							):
												if not isinstance(_A					,						_A							):
																	return False

												if self.degree != polynomial_a.degree:
																	return False

												for i in range(self.degree + 1							):
																	if self.coefficients[i] != polynomial_a.coefficients[i]:
																						return False

												return True




							def __ne__( self					,						_A							):
												return not self.__eq__(_A							)






