"""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 		__A							:






     def __init__(	self       ,  a__						):
       _lowerCAmelCase		: Union[str, Any]						    =  sys.stdout
       _lowerCAmelCase		: List[Any]						    =  open(UpperCAmelCase__       ,  """a"""						)






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






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







def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[str]=80    ,_lowerCamelCase		: Dict=False			) ->   Tuple:
  _lowerCAmelCase		: Optional[int]						    =  []

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: int			) ->   Tuple:
  _lowerCAmelCase		: List[str]						    =  re.sub(r"""[\\\n]+"""    ,""" """    ,args.base_cmd			)

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

  # ensure we have --overwrite_output_dir
  _lowerCAmelCase		: Dict						    =  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  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: int			) ->   Optional[Any]:


  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		: Dict						    =  subprocess.run(_UpperCamelCase    ,capture_output=_UpperCamelCase    ,text=_UpperCamelCase			)

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

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





def 							SCREAMING_SNAKE_CASE  (    ) ->   str:
  _lowerCAmelCase		: 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  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict			) ->   Optional[Any]:
  _lowerCAmelCase		: Dict						    =  pd.DataFrame(_UpperCamelCase			)
  _lowerCAmelCase		: List[Any]						    =  """variation"""
  _lowerCAmelCase		: str						    =  """diff_%"""

  _lowerCAmelCase		: Any						    =  nan
  if base_variation is not None and len(df[df[variation_key] == base_variation]			):
    # this may still return nan
    _lowerCAmelCase		: List[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
    _lowerCAmelCase		: Union[str, Any]						    =  df.loc[df[target_metric_key] != nan][target_metric_key].min()

  # create diff column if possible
  if not math.isnan(_UpperCamelCase			):
    _lowerCAmelCase		: Dict						    =  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
  _lowerCAmelCase		: List[Any]						    =  [variation_key, target_metric_key, diff_key, *report_metric_keys]
  _lowerCAmelCase		: int						    =  df.reindex(_UpperCamelCase    ,axis="""columns"""			)  # reorder cols

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

  # make the cols as narrow as possible
  _lowerCAmelCase		: List[str]						    =  df.rename(lambda _lowerCamelCase			: c.replace("""_"""    ,"""<br>"""			)    ,axis="""columns"""			)
  _lowerCAmelCase		: Tuple						    =  df.rename(lambda _lowerCamelCase			: c.replace("""_"""    ,"""\n"""			)    ,axis="""columns"""			)

  _lowerCAmelCase		: Optional[int]						    =  ["""""", """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  (    ) ->   Tuple:
  _lowerCAmelCase		: 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"""    ,)
  _lowerCAmelCase		: int						    =  parser.parse_args()

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

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

  # split wanted keys
  _lowerCAmelCase		: Optional[int]						    =  args.report_metric_keys.split()

  # capture prints into a log file for convenience
  _lowerCAmelCase		: 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}"			)

  _lowerCAmelCase		: int						    =  Tee(_UpperCamelCase			)

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

  _lowerCAmelCase		: List[Any]						    =  """variation"""
  _lowerCAmelCase		: List[Any]						    =  []
  for id, variation in enumerate(tqdm(_UpperCamelCase    ,desc="""Total completion: """    ,leave=_UpperCamelCase			)			):
    _lowerCAmelCase		: 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :	Tuple 					=     {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WavLMForAudioFrameClassification',
         'WavLMForCTC',
         'WavLMForSequenceClassification',
         'WavLMForXVector',
         'WavLMModel',
         'WavLMPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavlm import (
              WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavLMForAudioFrameClassification,
              WavLMForCTC,
              WavLMForSequenceClassification,
              WavLMForXVector,
              WavLMModel,
              WavLMPreTrainedModel,
          )

else:
     import sys

     _a   :	Any 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




