'''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_ : str						  =      float("nan")





class 							lowercase						:


   def __init__(   self				:	Tuple   ,		__lowerCAmelCase				:	Any)      ->					Any:
         lowercase_     =      sys.stdout
         lowercase_     =      open(__lowerCAmelCase   ,		"a")


   def __getattr__(   self				:	str   ,		__lowerCAmelCase				:	List[Any])      ->					Tuple:
         return getattr(self.stdout   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	Optional[Any])      ->					Optional[Any]:
         self.stdout.write(__lowerCAmelCase)
         # strip tqdm codes
         self.file.write(re.sub(R"^.*\r"   ,		""   ,		__lowerCAmelCase   ,		0   ,		re.M))




def __a (	__lowerCamelCase			:						Tuple=80 ,		__lowerCamelCase			:						str=False				)	->							str:
      '''simple docstring'''


      lowercase_     =      []

      # deal with critical env vars
      lowercase_     =      ["CUDA_VISIBLE_DEVICES"]
      for key in env_keys:
            lowercase_     =      os.environ.get(__lowerCamelCase ,		__lowerCamelCase				)
            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(__lowerCamelCase				)

      # 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(__lowerCamelCase				) > 0:
            current_line += f'{cmd.pop(0				)} '
            if len(__lowerCamelCase				) == 0 or len(__lowerCamelCase				) + len(cmd[0]				) + 1 > max_width - 1:
                  lines.append(__lowerCamelCase				)
                  lowercase_     =      ""
      return "\\\n".join(__lowerCamelCase				)


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Tuple				)	->							str:
      '''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 (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						List[Any]				)	->							Dict:
      '''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(__lowerCamelCase ,		capture_output=__lowerCamelCase ,		text=__lowerCamelCase				)

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

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

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


def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Dict ,		)	->							Optional[Any]:
      '''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(__lowerCamelCase				) ,		desc=__lowerCamelCase ,		leave=__lowerCamelCase				):
            lowercase_     =      process_run_single(
                __lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
            lowercase_     =      single_run_metrics[target_metric_key]
            if not math.isnan(__lowerCamelCase				):
                  metrics.append(__lowerCamelCase				)
                  results.append(__lowerCamelCase				)
                  outcome += "✓"
            else:
                  outcome += "✘"
      lowercase_     =      f'\33[2K\r{outcome}'
      if len(__lowerCamelCase				) > 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(__lowerCamelCase				) > 1:
                  results_str += f' {tuple(round(__lowerCamelCase ,		2				) for x in results				)}'
            print(__lowerCamelCase				)
            lowercase_     =      variation
            return mean_metrics
      else:
            print(__lowerCamelCase				)
            return {variation_key: variation, target_metric_key: nan}


def __a (	)	->							Optional[Any]:
      '''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 (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						str				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      pd.DataFrame(__lowerCamelCase				)
      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(__lowerCamelCase				):
            # 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(__lowerCamelCase				):
            lowercase_     =      df.apply(
                lambda __lowerCamelCase				: 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(__lowerCamelCase ,		axis="columns"				)  # reorder cols

      # capitalize
      lowercase_     =      df.rename(str.capitalize ,		axis="columns"				)

      # make the cols as narrow as possible
      lowercase_     =      df.rename(lambda __lowerCamelCase				: c.replace("_" ,		"<br>"				) ,		axis="columns"				)
      lowercase_     =      df.rename(lambda __lowerCamelCase				: 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=__lowerCamelCase ,		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=__lowerCamelCase ,		floatfmt=".2f"				)]

      print("\n\n".join(__lowerCamelCase				)				)


def __a (	)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      argparse.ArgumentParser()
      parser.add_argument(
          "--base-cmd" ,		default=__lowerCamelCase ,		type=__lowerCamelCase ,		required=__lowerCamelCase ,		help="Base cmd" ,		)
      parser.add_argument(
          "--variations" ,		default=__lowerCamelCase ,		type=__lowerCamelCase ,		nargs="+" ,		required=__lowerCamelCase ,		help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'" ,		)
      parser.add_argument(
          "--base-variation" ,		default=__lowerCamelCase ,		type=__lowerCamelCase ,		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=__lowerCamelCase ,		type=__lowerCamelCase ,		required=__lowerCamelCase ,		help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second" ,		)
      parser.add_argument(
          "--report-metric-keys" ,		default="" ,		type=__lowerCamelCase ,		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=__lowerCamelCase ,		help="How many times to re-run each variation - an average will be reported" ,		)
      parser.add_argument(
          "--output_dir" ,		default="output_benchmark" ,		type=__lowerCamelCase ,		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=__lowerCamelCase ,		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(__lowerCamelCase				).mkdir(exist_ok=__lowerCamelCase				)
      lowercase_     =      get_base_command(__lowerCamelCase ,		__lowerCamelCase				)

      # split each dimension into its --foo variations
      lowercase_     =      [list(map(str.strip ,		re.split(R"\|" ,		__lowerCamelCase				)				)				) 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(*__lowerCamelCase				)				)				)				)
      lowercase_     =      max(len(__lowerCamelCase				) 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(__lowerCamelCase				)

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

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

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


if __name__ == "__main__":
       main()







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






