

"""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


SCREAMING_SNAKE_CASE_					:	str							  =						float('nan')



class 					a						:



    """simple docstring"""



    def __init__(     self: str    ,						UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         A__										=  sys.stdout
         A__										=  open(UpperCAmelCase__    ,						"""a"""       )

    def __getattr__(     self: List[Any]    ,						UpperCamelCase: Tuple       ):






         """simple docstring"""


         return getattr(self.stdout    ,						UpperCAmelCase__       )





    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: List[Any]       ):






         """simple docstring"""


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





def  _snake_case     (    UpperCAmelCase_  :					List[str]=80					,   UpperCAmelCase_  :					Dict=False			):
     A__										=  []

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





def  _snake_case     (    UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					int			):
     A__										=  re.sub(R"""[\\\n]+"""					,   """ """					,   args.base_cmd			)

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

     # ensure we have --overwrite_output_dir
     A__										=  re.sub("""--overwrite_output_dir\s+"""					,   """"""					,   args.base_cmd			)
     args.base_cmd += " --overwrite_output_dir"

     return [sys.executable] + shlex.split(args.base_cmd			)





def  _snake_case     (    UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					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, 1_00.2, 55.66_66, 2_22.22_22_22_22]			)}					,   )

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

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

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





def  _snake_case     (    ):
     A__										=  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  _snake_case     (    UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Dict			):
     A__										=  pd.DataFrame(_UpperCamelCase			)
     A__										=  """variation"""
     A__										=  """diff_%"""

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

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

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

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

     A__										=  ["""""", """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  _snake_case     (    ):
     A__										=  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__										=  parser.parse_args()

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

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

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

     # capture prints into a log file for convenience
     A__										=  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__										=  Tee(_UpperCamelCase			)

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

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



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






