

'''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


snake_case  : List[str]					    =	float('nan')




class 					lowerCamelCase__:

 def __init__(      self			,							__UpperCAmelCase	):



    """simple docstring"""







    __lowercase							      =				sys.stdout
    __lowercase							      =				open(_SCREAMING_SNAKE_CASE			,							"""a"""	)

 def __getattr__(      self			,							__UpperCAmelCase	):



    """simple docstring"""







    return getattr(self.stdout			,							_SCREAMING_SNAKE_CASE	)




 def   __magic_name__						(      self			,							__UpperCAmelCase	):



    """simple docstring"""







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







def 			lowercase__			(					__UpperCamelCase  :							int=80	,							__UpperCamelCase  :							str=False		):




   '''simple docstring'''





   __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 			lowercase__			(					__UpperCamelCase  :							List[str]	,							__UpperCamelCase  :							Dict		):




   '''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 			lowercase__			(					__UpperCamelCase  :							Optional[Any]	,							__UpperCamelCase  :							List[Any]	,							__UpperCamelCase  :							Optional[Any]	,							__UpperCamelCase  :							int	,							__UpperCamelCase  :							Union[str, Any]	,							__UpperCamelCase  :							Any	,							__UpperCamelCase  :							int		):




   '''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, 222.2222_2222]		)}	,							)

   __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 			lowercase__			(					__UpperCamelCase  :							List[Any]	,							__UpperCamelCase  :							Tuple	,							__UpperCamelCase  :							Optional[Any]	,							__UpperCamelCase  :							str	,							__UpperCamelCase  :							List[str]	,							__UpperCamelCase  :							Union[str, Any]	,							__UpperCamelCase  :							int	,							__UpperCamelCase  :							Tuple	,							__UpperCamelCase  :							Union[str, Any]	,							__UpperCamelCase  :							int	,							):




   '''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(_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 			lowercase__			(					):




   '''simple docstring'''





   __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 			lowercase__			(					__UpperCamelCase  :							str	,							__UpperCamelCase  :							Union[str, Any]	,							__UpperCamelCase  :							Any	,							__UpperCamelCase  :							Dict	,							__UpperCamelCase  :							Tuple		):




   '''simple docstring'''





   __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 __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
   __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 __UpperCamelCase		: c.replace("""_"""	,							"""<br>"""		)	,							axis="""columns"""		)
   __lowercase							      =				df.rename(lambda __UpperCamelCase		: 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 			lowercase__			(					):




   '''simple docstring'''





   __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()









'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_		:     Optional[int]          =		object()

# For specifying empty leaf dict `{}`
UpperCamelCase_		:     Optional[int]          =		object()





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= tuple((re.compile(x + "$"       ) for x in qs)       )
     for i in range(len(_UpperCamelCase       ) - len(_UpperCamelCase       ) + 1       ):
          _snake_case				= [x.match(_UpperCamelCase       ) for x, y in zip(_UpperCamelCase   ,		ks[i:]       )]
          if matches and all(_UpperCamelCase       ):
               return True
     return False





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     def replace(_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[str]       ):
          for rule, replacement in rules:
               if _match(_UpperCamelCase   ,		_UpperCamelCase       ):
                    return replacement
          return val

     return replace





def       __a      (  )		->					Any:

     """simple docstring"""




     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         (("transformer", "wte", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("attention", "out_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("mlp", "c_fc", "bias"), P("mp"       )),
         (("mlp", "c_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]





def       __a      (  _UpperCamelCase:       Union[str, Any]       )		->					Any:

     """simple docstring"""




     _snake_case				= _get_partition_rules()
     _snake_case				= _replacement_rules(_UpperCamelCase       )
     _snake_case				= {k: _unmatched for k in flatten_dict(_UpperCamelCase       )}
     _snake_case				= {k: replace(_UpperCamelCase   ,		_UpperCamelCase       ) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(_UpperCamelCase       )       )


