


"""simple docstring"""


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


A  :     str    					=    float("nan")
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a			):
										__lowerCAmelCase       					=    sys.stdout
										__lowerCAmelCase       					=    open(UpperCAmelCase__					,     "a"			)

							def __getattr__(  self					,     __a			):
										return getattr(self.stdout					,     UpperCAmelCase__			)



							def     snake_case       (  self					,     __a			):
										self.stdout.write(UpperCAmelCase__			)
										# strip tqdm codes
										self.file.write(re.sub(R"^.*\r"					,     ""					,     UpperCAmelCase__					,     0					,     re.M			)			)


def _lowerCamelCase			(	_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 _lowerCamelCase			(	_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 _lowerCamelCase			(	_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, 1_00.2, 55.66_66, 2_22.22_22_22_22]   )}					,				)

			__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 _lowerCamelCase			(	_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 _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    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			(	_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 _lowerCamelCase			(	):

			'''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()




"""simple docstring"""


A  :     List[Any]    					=    [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A  :     Optional[int]    					=    [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A  :     Optional[int]    					=    {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			assert len(str(_UpperCamelCase   )   ) > 2, "year should be in YYYY format"
			assert 1 <= month <= 12, "month should be between 1 to 12"
			assert 1 <= day <= 31, "day should be between 1 to 31"

			# Doomsday algorithm:
			__lowerCAmelCase       					=    year // 100
			__lowerCAmelCase       					=    (5 * (century % 4) + 2) % 7
			__lowerCAmelCase       					=    year % 100
			__lowerCAmelCase       					=    centurian % 12
			__lowerCAmelCase       					=    (
			    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
			) % 7
			__lowerCAmelCase       					=    (
			    DOOMSDAY_NOT_LEAP[month - 1]
			    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
			    else DOOMSDAY_LEAP[month - 1]
			)
			__lowerCAmelCase       					=    (dooms_day + day - day_anchor) % 7
			return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
	import doctest

	doctest.testmod()




