

'''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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  int      ,lowercase__     :  List[Any]    ):
   __lowercase													=				sys.stdout
   __lowercase													=				open(lowercase__      ,'''a'''    )






  def __getattr__(   self     :  str      ,lowercase__     :  List[Any]    ):
   return getattr(self.stdout      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  List[Any]    ):
   self.stdout.write(lowercase__    )
   # strip tqdm codes
   self.file.write(re.sub(r'''^.*\r'''      ,''''''      ,lowercase__      ,0      ,re.M    )    )


def 	_A  (					A__=80  ,       A__=False   ):






 """simple docstring"""



 __lowercase													=				[]

 # deal with critical env vars
 __lowercase													=				['''CUDA_VISIBLE_DEVICES''']
 for key in env_keys:
  __lowercase													=				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)
 __lowercase													=				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
 __lowercase													=				[]
 __lowercase													=				''''''
 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__   )
   __lowercase													=				''''''
 return "\\\n".join(A__   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

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

 # ensure we have --overwrite_output_dir
 __lowercase													=				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  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """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, 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]   )}  ,       )

 __lowercase													=				subprocess.run(A__  ,       capture_output=A__  ,       text=A__   )

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

 # save the streams
 __lowercase													=				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:
  __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       ):






 """simple docstring"""



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


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				pd.DataFrame(A__   )
 __lowercase													=				'''variation'''
 __lowercase													=				'''diff_%'''

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

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

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

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

 __lowercase													=				['''''', '''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 	_A  (					):






 """simple docstring"""



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

 __lowercase													=				args.output_dir
 Path(A__   ).mkdir(exist_ok=A__   )
 __lowercase													=				get_base_command(A__  ,       A__   )

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

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

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

 __lowercase													=				Tee(A__   )

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

 __lowercase													=				'''variation'''
 __lowercase													=				[]
 for id, variation in enumerate(tqdm(A__  ,       desc='''Total completion: '''  ,       leave=A__   )   ):
  __lowercase													=				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 random
from typing import Any


def 	_A  (					A__   ):






 """simple docstring"""



 for _ in range(len(A__   )   ):
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase						,			__lowercase													=				data[b], data[a]
 return data


if __name__ == "__main__":
 lowerCAmelCase__      		=			[0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase__      		=			['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

