




'''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_      :  Tuple				     =  float('nan')







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  str						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							List[str]     						=  sys.stdout
           a_     :							List[str]     						=  open(lowercase__						,						"""a"""       )






      def __getattr__( self   :  Dict						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           return getattr(self.stdout						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str=80 ,    UpperCamelCase__    :					List[str]=False				):




     """simple docstring"""


     a_     :							str     						=  []

     # deal with critical env vars
     a_     :							Optional[int]     						=  ["""CUDA_VISIBLE_DEVICES"""]
     for key in env_keys:
          a_     :							Tuple     						=  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_     :							str     						=  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_     :							Any     						=  []
     a_     :							Tuple     						=  """"""
     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_     :							Union[str, Any]     						=  """"""
     return "\\\n".join(UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							Union[str, Any]     						=  re.sub(R"""[\\\n]+""" ,    """ """ ,    args.base_cmd				)

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

     # ensure we have --overwrite_output_dir
     a_     :							Optional[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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Union[str, 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_     :							Tuple     						=  subprocess.run(UpperCamelCase__ ,    capture_output=UpperCamelCase__ ,    text=UpperCamelCase__				)

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

     # save the streams
     a_     :							str     						=  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_     :							List[str]     						=  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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Dict ,    UpperCamelCase__    :					int ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Union[str, Any] ,    ):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Optional[Any]     						=  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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Union[str, Any]				):




     """simple docstring"""


     a_     :							Optional[int]     						=  pd.DataFrame(UpperCamelCase__				)
     a_     :							int     						=  """variation"""
     a_     :							Tuple     						=  """diff_%"""

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

     # create diff column if possible
     if not math.isnan(UpperCamelCase__				):
          a_     :							Optional[int]     						=  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_     :							int     						=  [variation_key, target_metric_key, diff_key, *report_metric_keys]
     a_     :							Optional[int]     						=  df.reindex(UpperCamelCase__ ,    axis="""columns"""				)  # reorder cols

     # capitalize
     a_     :							Any     						=  df.rename(str.capitalize ,    axis="""columns"""				)

     # make the cols as narrow as possible
     a_     :							int     						=  df.rename(lambda UpperCamelCase__				: c.replace("""_""" ,    """<br>"""				) ,    axis="""columns"""				)
     a_     :							Any     						=  df.rename(lambda UpperCamelCase__				: c.replace("""_""" ,    """\n"""				) ,    axis="""columns"""				)

     a_     :							Union[str, 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=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  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							int     						=  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_     :							Optional[int]     						=  parser.parse_args()

     a_     :							Union[str, Any]     						=  args.output_dir
     Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
     a_     :							int     						=  get_base_command(UpperCamelCase__ ,    UpperCamelCase__				)

     # split each dimension into its --foo variations
     a_     :							Tuple     						=  [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_     :							Optional[Any]     						=  list(map(str.strip ,    map(""" """.join ,    itertools.product(*UpperCamelCase__				)				)				)				)
     a_     :							Tuple     						=  max(len(UpperCamelCase__				) for x in variations				)

     # split wanted keys
     a_     :							Dict     						=  args.report_metric_keys.split()

     # capture prints into a log file for convenience
     a_     :							Union[str, Any]     						=  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_     :							str     						=  Tee(UpperCamelCase__				)

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

     a_     :							List[Any]     						=  """variation"""
     a_     :							List[Any]     						=  []
     for id, variation in enumerate(tqdm(UpperCamelCase__ ,    desc="""Total completion: """ ,    leave=UpperCamelCase__				)				):
          a_     :							Tuple     						=  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'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_      :  List[Any]				     =  2_9979_2458

# Symbols
lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_      :  Tuple				     =  symbols('ct x y z')



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     if velocity > c:
          raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""				)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError("""Speed must be greater than or equal to 1!"""				)

     return velocity / c



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return 1 / sqrt(1 - beta(UpperCamelCase__				) ** 2				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return np.array(
         [
             [gamma(UpperCamelCase__				), -gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), 0, 0],
             [-gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), gamma(UpperCamelCase__				), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					np.ndarray | None = None				):




     """simple docstring"""


     if event is None:
          a_     :							str     						=  np.array([ct, x, y, z]				)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(UpperCamelCase__				) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 lowerCAmelCase_      :  Union[str, Any]				     =  transform(2997_9245)
 print('Example of four vector: ')
 print(f"ct' = {four_vector[0]}")
 print(f"x' = {four_vector[1]}")
 print(f"y' = {four_vector[2]}")
 print(f"z' = {four_vector[3]}")

 # Substitute symbols with numerical values
 lowerCAmelCase_      :  Tuple				     =  {ct: c, x: 1, y: 1, z: 1}
 lowerCAmelCase_      :  Optional[int]				     =  [four_vector[i].subs(sub_dict) for i in range(4)]

 print(f"\n{numerical_vector}")
