





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


snake_case 				=       float("""nan""")





class 				A_				:







       """simple docstring"""





       def __init__(    self	:	str						,__A	:	Optional[int]  )						->					Union[str, Any]:
         _lowercase							= sys.stdout
         _lowercase							= open(UpperCAmelCase__						,'a'  )







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







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	List[Any]  )						->					Union[str, Any]:
         self.stdout.write(UpperCAmelCase__  )
         # strip tqdm codes
         self.file.write(re.sub(r'^.*\r'						,''						,UpperCAmelCase__						,0						,re.M  )  )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]=80			,							snake_case__  :Dict=False     ):
  _lowercase							= []

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :int     ):
  _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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :Optional[int]			,							snake_case__  :Optional[int]			,							snake_case__  :List[str]			,							snake_case__  :Tuple			,							snake_case__  :List[str]			,							snake_case__  :int     ):


  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]     )}			,							)

  _lowercase							= subprocess.run(_UpperCamelCase			,							capture_output=_UpperCamelCase			,							text=_UpperCamelCase     )

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

  # save the streams
  _lowercase							= 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:
    _lowercase							= 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__       (  snake_case__  :str			,							snake_case__  :Optional[Any]			,							snake_case__  :Optional[int]			,							snake_case__  :Optional[Any]			,							snake_case__  :Optional[int]			,							snake_case__  :Any			,							snake_case__  :Dict			,							snake_case__  :Optional[int]			,							snake_case__  :List[str]			,							snake_case__  :Dict			,							):
  _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(_UpperCamelCase     )			,							desc=_UpperCamelCase			,							leave=_UpperCamelCase     ):
    _lowercase							= process_run_single(
        _UpperCamelCase			,							_UpperCamelCase			,							_UpperCamelCase			,							_UpperCamelCase			,							_UpperCamelCase			,							_UpperCamelCase			,							_UpperCamelCase     )
    _lowercase							= single_run_metrics[target_metric_key]
    if not math.isnan(_UpperCamelCase     ):
      metrics.append(_UpperCamelCase     )
      results.append(_UpperCamelCase     )
      outcome += "✓"
    else:
      outcome += "✘"
  _lowercase							= F"""\33[2K\r{outcome}"""
  if len(_UpperCamelCase     ) > 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(_UpperCamelCase     ) > 1:
      results_str += F""" {tuple(round(_UpperCamelCase			,							2     ) for x in results     )}"""
    print(_UpperCamelCase     )
    _lowercase							= variation
    return mean_metrics
  else:
    print(_UpperCamelCase     )
    return {variation_key: variation, target_metric_key: nan}

def 							SCREAMING_SNAKE_CASE__       (  ):
  _lowercase							= torch.cuda.get_device_properties(torch.device('cuda'     )     )
  return F"""
Datetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"     )}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
"""

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :Dict			,							snake_case__  :Optional[Any]			,							snake_case__  :Dict     ):
  _lowercase							= pd.DataFrame(_UpperCamelCase     )
  _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(_UpperCamelCase     ):
    # 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(_UpperCamelCase     ):
    _lowercase							= df.apply(
        lambda snake_case__     : 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(_UpperCamelCase			,							axis='columns'     )  # reorder cols

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

  # make the cols as narrow as possible
  _lowercase							= df.rename(lambda snake_case__     : c.replace('_'			,							'<br>'     )			,							axis='columns'     )
  _lowercase							= df.rename(lambda snake_case__     : 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=_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__       (  ):
  _lowercase							= 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'			,							)
  _lowercase							= parser.parse_args()

  _lowercase							= args.output_dir
  Path(_UpperCamelCase     ).mkdir(exist_ok=_UpperCamelCase     )
  _lowercase							= get_base_command(_UpperCamelCase			,							_UpperCamelCase     )

  # split each dimension into its --foo variations
  _lowercase							= [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
  _lowercase							= list(map(str.strip			,							map(' '.join			,							itertools.product(*_UpperCamelCase     )     )     )     )
  _lowercase							= max(len(_UpperCamelCase     ) 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(_UpperCamelCase     )

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

  _lowercase							= 'variation'
  _lowercase							= []
  for id, variation in enumerate(tqdm(_UpperCamelCase			,							desc='Total completion: '			,							leave=_UpperCamelCase     )     ):
    _lowercase							= 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()






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,*__A	:	Optional[int]						,**__A	:	List[Any]  )						->					None:
         warnings.warn(
             'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DonutImageProcessor instead.'						,__A						,)
         super().__init__(*__A						,**__A  )