




'''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


_snake_case : str  				=						float('nan')

class      A       :







							def __init__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							sys.stdout
														_a      	=							open(lowerCAmelCase_				,     '''a'''   )







							def __getattr__(    self			:    Dict				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				List[str]:


														"""simple docstring"""


														return getattr(self.stdout				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[int]   )     ->				str:


														"""simple docstring"""


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



def 						snake_case_       (UpperCamelCase			:    List[str]=80					,		UpperCamelCase			:    int=False ):





							'''simple docstring'''


							_a      	=							[]

							# deal with critical env vars
							_a      	=							['''CUDA_VISIBLE_DEVICES''']
							for key in env_keys:
														_a      	=							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)
							_a      	=							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
							_a      	=							[]
							_a      	=							''''''
							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 )
																					_a      	=							''''''
							return "\\\n".join(UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


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

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

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

							return [sys.executable] + shlex.split(args.base_cmd )
def 						snake_case_       (UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    List[str]					,		UpperCamelCase			:    Any ):





							'''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] )}					,		)

							_a      	=							subprocess.run(UpperCamelCase					,		capture_output=UpperCamelCase					,		text=UpperCamelCase )

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

							# save the streams
							_a      	=							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:
														_a      	=							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 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    Tuple					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    str					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    List[str]					,		):





							'''simple docstring'''


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





							'''simple docstring'''


							_a      	=							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 						snake_case_       (UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							pd.DataFrame(UpperCamelCase )
							_a      	=							'''variation'''
							_a      	=							'''diff_%'''

							_a      	=							nan
							if base_variation is not None and len(df[df[variation_key] == base_variation] ):
														# this may still return nan
														_a      	=							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
														_a      	=							df.loc[df[target_metric_key] != nan][target_metric_key].min()

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

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

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

							_a      	=							['''''', '''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 						snake_case_       ():





							'''simple docstring'''


							_a      	=							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'''					,		)
							_a      	=							parser.parse_args()

							_a      	=							args.output_dir
							Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
							_a      	=							get_base_command(UpperCamelCase					,		UpperCamelCase )

							# split each dimension into its --foo variations
							_a      	=							[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
							_a      	=							list(map(str.strip					,		map(''' '''.join					,		itertools.product(*UpperCamelCase ) ) ) )
							_a      	=							max(len(UpperCamelCase ) for x in variations )

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

							# capture prints into a log file for convenience
							_a      	=							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}' )

							_a      	=							Tee(UpperCamelCase )

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

							_a      	=							'''variation'''
							_a      	=							[]
							for id, variation in enumerate(tqdm(UpperCamelCase					,		desc='''Total completion: '''					,		leave=UpperCamelCase ) ):
														_a      	=							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()







'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

