

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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  int      ,lowercase__     :  List[Any]    ):
   __lowercase													=				sys.stdout
   __lowercase													=				open(lowercase__      ,'''a'''    )






  def __getattr__(   self     :  str      ,lowercase__     :  List[Any]    ):
   return getattr(self.stdout      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  List[Any]    ):
   self.stdout.write(lowercase__    )
   # strip tqdm codes
   self.file.write(re.sub(r'''^.*\r'''      ,''''''      ,lowercase__      ,0      ,re.M    )    )


def 	_A  (					A__=80  ,       A__=False   ):






 """simple docstring"""



 __lowercase													=				[]

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


def 	_A  (					A__  ,       A__   ):






 """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 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """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, 222.22222222]   )}  ,       )

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

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

 # save the streams
 __lowercase													=				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:
  __lowercase													=				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  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       ):






 """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(A__   )  ,       desc=A__  ,       leave=A__   ):
  __lowercase													=				process_run_single(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   )
  __lowercase													=				single_run_metrics[target_metric_key]
  if not math.isnan(A__   ):
   metrics.append(A__   )
   results.append(A__   )
   outcome += "✓"
  else:
   outcome += "✘"
 __lowercase													=				F"\33[2K\r{outcome}"
 if len(A__   ) > 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(A__   ) > 1:
   results_str += F" {tuple(round(A__  ,       2   ) for x in results   )}"
  print(A__   )
  __lowercase													=				variation
  return mean_metrics
 else:
  print(A__   )
  return {variation_key: variation, target_metric_key: nan}


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				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  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				pd.DataFrame(A__   )
 __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(A__   ):
  # 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(A__   ):
  __lowercase													=				df.apply(
      lambda A__   : 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(A__  ,       axis='''columns'''   )  # reorder cols

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

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






 """simple docstring"""



 __lowercase													=				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'''  ,       )
 __lowercase													=				parser.parse_args()

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

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

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

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

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__      		=			numpy.array([0, 0])
lowerCAmelCase__      		=			numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__      		=			numpy.array([1, 0])
lowerCAmelCase__      		=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				initial_vectors
 for _ in range(A__   ):
  __lowercase													=				iteration_step(A__   )
 return vectors


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				[]
 for i, start_vector in enumerate(vectors[:-1]   ):
  __lowercase													=				vectors[i + 1]
  new_vectors.append(A__   )
  __lowercase													=				end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3   )
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3  ,       60   )   )
  new_vectors.append(start_vector + difference_vector * 2 / 3   )
 new_vectors.append(vectors[-1]   )
 return new_vectors


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				numpy.radians(A__   )
 __lowercase						,			__lowercase													=				numpy.cos(A__   ), numpy.sin(A__   )
 __lowercase													=				numpy.array(((c, -s), (s, c))   )
 return numpy.dot(A__  ,       A__   )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				plt.gca()
 axes.set_aspect('''equal'''   )

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 __lowercase						,			__lowercase													=				zip(*A__   )
 plt.plot(A__  ,       A__   )
 plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase__      		=			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)

