





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_				= float("nan")

class 					A     :



      def __init__(       self  ,	lowercase_       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case   :				int  					=    sys.stdout
             _snake_case   :				Any  					=    open(__SCREAMING_SNAKE_CASE  ,	'''a'''       )



      def __getattr__(       self  ,	lowercase_       )   -> Union[str, Any]:



             '''simple docstring'''

             return getattr(self.stdout  ,	__SCREAMING_SNAKE_CASE       )



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



             '''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     A_      (	lowercase_=80   ,		lowercase_=False	)      ->    Optional[int]:
       _snake_case   :				Optional[int]  					=    []

       # deal with critical env vars
       _snake_case   :				Dict  					=    ['''CUDA_VISIBLE_DEVICES''']
       for key in env_keys:
              _snake_case   :				Tuple  					=    os.environ.get(__A   ,		__A	)
              if val is not None:
                     cmd.append(f'''{key}={val}'''	)

    # python executable (not always needed if the script is executable)
       _snake_case   :				Dict  					=    sys.executable if full_python_path else sys.executable.split('''/'''	)[-1]
       cmd.append(__A	)

       # now the normal args
       cmd += list(map(shlex.quote   ,		sys.argv	)	)

       # split up into up to MAX_WIDTH lines with shell multi-line escapes
       _snake_case   :				Union[str, Any]  					=    []
       _snake_case   :				int  					=    ''''''
       while len(__A	) > 0:
              current_line += f'''{cmd.pop(0	)} '''
              if len(__A	) == 0 or len(__A	) + len(cmd[0]	) + 1 > max_width - 1:
                     lines.append(__A	)
                     _snake_case   :				Optional[Any]  					=    ''''''
       return "\\\n".join(__A	)




def     A_      (	lowercase_   ,		lowercase_	)      ->    Optional[int]:
       # unwrap multi-line input
       _snake_case   :				int  					=    re.sub(r'''[\\\n]+'''   ,		''' '''   ,		args.base_cmd	)

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

       # ensure we have --overwrite_output_dir
       _snake_case   :				int  					=    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_      (	lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_	)      ->    List[Any]:
       # Enable to debug everything but the run itself, to do it fast and see the progress.
       # This is useful for debugging the output formatting quickly - we can remove it later once
       # everybody is happy with the output
       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]	)}   ,		)

       _snake_case   :				List[str]  					=    subprocess.run(__A   ,		capture_output=__A   ,		text=__A	)

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

       # save the streams
       _snake_case   :				Union[str, Any]  					=    variation.replace(''' '''   ,		'''-'''	)
       with open(Path(__A	) / f'''log.{prefix}.stdout.txt'''   ,		'''w'''	) as f:
              f.write(result.stdout	)
       with open(Path(__A	) / 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:
              _snake_case   :				Union[str, Any]  					=    json.load(__A	)

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




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




def     A_      (	)      ->    Optional[Any]:
       _snake_case   :				Dict  					=    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_      (	lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_	)      ->    Optional[int]:
       _snake_case   :				Dict  					=    pd.DataFrame(__A	)
       _snake_case   :				int  					=    '''variation'''
       _snake_case   :				List[Any]  					=    '''diff_%'''

       _snake_case   :				List[str]  					=    nan
       if base_variation is not None and len(df[df[variation_key] == base_variation]	):
              # this may still return nan
              _snake_case   :				List[Any]  					=    df.loc[df[variation_key] == base_variation][target_metric_key].item()
       if math.isnan(__A	):
              # as a fallback, use the minimal value as the sentinel
              _snake_case   :				List[str]  					=    df.loc[df[target_metric_key] != nan][target_metric_key].min()

       # create diff column if possible
       if not math.isnan(__A	):
              _snake_case   :				Dict  					=    df.apply(
                  lambda lowercase_	: 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
       _snake_case   :				Tuple  					=    [variation_key, target_metric_key, diff_key, *report_metric_keys]
       _snake_case   :				Tuple  					=    df.reindex(__A   ,		axis='''columns'''	)  # reorder cols

       # capitalize
       _snake_case   :				Tuple  					=    df.rename(str.capitalize   ,		axis='''columns'''	)

       # make the cols as narrow as possible
       _snake_case   :				int  					=    df.rename(lambda lowercase_	: c.replace('''_'''   ,		'''<br>'''	)   ,		axis='''columns'''	)
       _snake_case   :				List[str]  					=    df.rename(lambda lowercase_	: c.replace('''_'''   ,		'''\n'''	)   ,		axis='''columns'''	)

       _snake_case   :				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=__A   ,		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=__A   ,		floatfmt='''.2f'''	)]

       print('''\n\n'''.join(__A	)	)




def     A_      (	)      ->    int:
       _snake_case   :				Union[str, Any]  					=    argparse.ArgumentParser()
       parser.add_argument(
           '''--base-cmd'''   ,		default=__A   ,		type=__A   ,		required=__A   ,		help='''Base cmd'''   ,		)
       parser.add_argument(
           '''--variations'''   ,		default=__A   ,		type=__A   ,		nargs='''+'''   ,		required=__A   ,		help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''''   ,		)
       parser.add_argument(
           '''--base-variation'''   ,		default=__A   ,		type=__A   ,		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=__A   ,		type=__A   ,		required=__A   ,		help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'''   ,		)
       parser.add_argument(
           '''--report-metric-keys'''   ,		default=''''''   ,		type=__A   ,		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=__A   ,		help='''How many times to re-run each variation - an average will be reported'''   ,		)
       parser.add_argument(
           '''--output_dir'''   ,		default='''output_benchmark'''   ,		type=__A   ,		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=__A   ,		action='''store_true'''   ,		help='''Whether to show the outputs of each run or just the benchmark progress'''   ,		)
       _snake_case   :				Tuple  					=    parser.parse_args()

       _snake_case   :				Any  					=    args.output_dir
       Path(__A	).mkdir(exist_ok=__A	)
       _snake_case   :				Any  					=    get_base_command(__A   ,		__A	)

       # split each dimension into its --foo variations
       _snake_case   :				Optional[int]  					=    [list(map(str.strip   ,		re.split(r'''\|'''   ,		__A	)	)	) 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
       _snake_case   :				Tuple  					=    list(map(str.strip   ,		map(''' '''.join   ,		itertools.product(*__A	)	)	)	)
       _snake_case   :				str  					=    max(len(__A	) for x in variations	)

       # split wanted keys
       _snake_case   :				List[str]  					=    args.report_metric_keys.split()

       # capture prints into a log file for convenience
       _snake_case   :				List[str]  					=    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}'''	)

       _snake_case   :				int  					=    Tee(__A	)

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

       _snake_case   :				Optional[int]  					=    '''variation'''
       _snake_case   :				List[str]  					=    []
       for id, variation in enumerate(tqdm(__A   ,		desc='''Total completion: '''   ,		leave=__A	)	):
              _snake_case   :				Optional[int]  					=    base_cmd + variation.split()
              results.append(
                  process_run(
                      id + 1   ,		__A   ,		__A   ,		__A   ,		__A   ,		args.target_metric_key   ,		__A   ,		args.repeat_times   ,		__A   ,		args.verbose   ,		)	)

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


if __name__ == "__main__":
   main()














'''simple docstring'''







from __future__ import annotations

from typing import TypedDict





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					42
			snake_case__	=					42





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )

				return [s[i:] + s[:i] for i in range(len(__A   )   )]





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )
				if not s:
								raise ValueError('''The parameter s must not be empty.'''   )

				a_		:	str									=   all_rotations(__A   )
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				a_		:	BWTTransformDict									=   {
				    "bwt_string": "".join([word[-1] for word in rotations]   ),
				    "idx_original_string": rotations.index(__A   ),
				}
				return response





def 		_UpperCAmelCase (				__A				: str    ,		__A				: int   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter bwt_string type must be str.'''   )
				if not bwt_string:
								raise ValueError('''The parameter bwt_string must not be empty.'''   )
				try:
								a_		:	Union[str, Any]									=   int(__A   )
				except ValueError:
								raise TypeError(
								    '''The parameter idx_original_string type must be int or passive'''
								    ''' of cast to int.'''   )
				if idx_original_string < 0:
								raise ValueError('''The parameter idx_original_string must not be lower than 0.'''   )
				if idx_original_string >= len(__A   ):
								raise ValueError(
								    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''   )

				a_		:	Union[str, Any]									=   [''''''] * len(__A   )
				for _ in range(len(__A   )   ):
								for i in range(len(__A   )   ):
												a_		:	int									=   bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		__lowerCAmelCase									=   'Provide a string that I will generate its BWT transform: '
		__lowerCAmelCase									=   input(entry_msg).strip()
		__lowerCAmelCase									=   bwt_transform(s)
		print(
		    F"""Burrows Wheeler transform for string '{s}' results """
		    F"""in '{result['bwt_string']}'"""
		)
		__lowerCAmelCase									=   reverse_bwt(result['bwt_string'], result['idx_original_string'])
		print(
		    F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
		    F"""we get original string '{original_string}'"""
		)




