

# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

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


__UpperCAmelCase								=			float("""nan""")



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						sys.stdout
     __lowerCAmelCase  :      List[str]    =						open(lowerCAmelCase	,							"""a"""   )







  def __getattr__(      self			:   List[Any]	,							lowerCAmelCase			:   str   )							->     Optional[int]:






     """simple docstring"""







     return getattr(self.stdout	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







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






def      snake_case_				(__A					:						Dict=8_0 ,       __A					:						Dict=False					):
   __lowerCAmelCase  :      Dict    =						[]

   # deal with critical env vars
   __lowerCAmelCase  :      Optional[Any]    =						["""CUDA_VISIBLE_DEVICES"""]
   for key in env_keys:
      __lowerCAmelCase  :      str    =						os.environ.get(lowercase_ ,       lowercase_					)
      if val is not None:
         cmd.append(f'''{key}={val}'''					)

      # python executable (not always needed if the script is executable)
   __lowerCAmelCase  :      Optional[int]    =						sys.executable if full_python_path else sys.executable.split("""/"""					)[-1]
   cmd.append(lowercase_					)

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

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





def      snake_case_				(__A					:						Any ,       __A					:						Any					):
   __lowerCAmelCase  :      Tuple    =						re.sub(r"""[\\\n]+""" ,       """ """ ,       args.base_cmd					)

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

   # ensure we have --overwrite_output_dir
   __lowerCAmelCase  :      Dict    =						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_				(__A					:						Dict ,       __A					:						Optional[Any] ,       __A					:						Union[str, Any] ,       __A					:						Dict ,       __A					:						int ,       __A					:						List[str] ,       __A					:						Union[str, Any]					):





   if 0:
      import random
      from time import sleep

      sleep(0					)
      return dict(
          {k: random.uniform(0 ,       1_0_0					) for k in metric_keys} ,       **{target_metric_key: random.choice([nan, 10.31, 1_0_0.2, 55.6666, 2_2_2.2_2_2_2_2_2_2_2]					)} ,       )

   __lowerCAmelCase  :      Optional[Any]    =						subprocess.run(lowercase_ ,       capture_output=lowercase_ ,       text=lowercase_					)

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

   # save the streams
   __lowerCAmelCase  :      Optional[Any]    =						variation.replace(""" """ ,       """-"""					)
   with open(Path(lowercase_					) / f'''log.{prefix}.stdout.txt''' ,       """w"""					) as f:
      f.write(result.stdout					)
   with open(Path(lowercase_					) / 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:
      __lowerCAmelCase  :      Tuple    =						json.load(lowercase_					)

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





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





def      snake_case_				():
   __lowerCAmelCase  :      List[Any]    =						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**3_0:0.2f}GB
'''





def      snake_case_				(__A					:						int ,       __A					:						List[Any] ,       __A					:						Tuple ,       __A					:						str ,       __A					:						Optional[Any]					):
   __lowerCAmelCase  :      Tuple    =						pd.DataFrame(lowercase_					)
   __lowerCAmelCase  :      Tuple    =						"""variation"""
   __lowerCAmelCase  :      Any    =						"""diff_%"""

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

   # create diff column if possible
   if not math.isnan(lowercase_					):
      __lowerCAmelCase  :      List[str]    =						df.apply(
          lambda __A					: round(1_0_0 * (r[target_metric_key] - sentinel_value) / sentinel_value					)
          if not math.isnan(r[target_metric_key]					)
          else 0 ,       axis="""columns""" ,       )

   # re-order columns
   __lowerCAmelCase  :      str    =						[variation_key, target_metric_key, diff_key, *report_metric_keys]
   __lowerCAmelCase  :      List[Any]    =						df.reindex(lowercase_ ,       axis="""columns"""					)  # reorder cols

   # capitalize
   __lowerCAmelCase  :      Optional[Any]    =						df.rename(str.capitalize ,       axis="""columns"""					)

   # make the cols as narrow as possible
   __lowerCAmelCase  :      Optional[Any]    =						df.rename(lambda __A					: c.replace("""_""" ,       """<br>"""					) ,       axis="""columns"""					)
   __lowerCAmelCase  :      Optional[int]    =						df.rename(lambda __A					: c.replace("""_""" ,       """\n"""					) ,       axis="""columns"""					)

   __lowerCAmelCase  :      Tuple    =						["""""", """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=lowercase_ ,       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=lowercase_ ,       floatfmt=""".2f"""					)]

   print("""\n\n""".join(lowercase_					)					)





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

   __lowerCAmelCase  :      str    =						args.output_dir
   Path(lowercase_					).mkdir(exist_ok=lowercase_					)
   __lowerCAmelCase  :      Optional[int]    =						get_base_command(lowercase_ ,       lowercase_					)

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

   # split wanted keys
   __lowerCAmelCase  :      List[Any]    =						args.report_metric_keys.split()

   # capture prints into a log file for convenience
   __lowerCAmelCase  :      int    =						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}'''					)

   __lowerCAmelCase  :      List[str]    =						Tee(lowercase_					)

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

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

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


if __name__ == "__main__":
   main()





import numpy as np
import qiskit





def      snake_case_				(__A					:						int = 8 ,       __A					:						int | None = None					)      ->     str:
   __lowerCAmelCase  :      List[Any]    =						np.random.default_rng(seed=__A					)

   # Roughly 25% of the qubits will contribute to the key.
   # So we take more than we need.
   __lowerCAmelCase  :      Tuple    =						6 * key_len
   # Measurement basis for Alice's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)
   # The set of states Alice will prepare.
   __lowerCAmelCase  :      List[str]    =						rng.integers(2 ,       size=__A					)
   # Measurement basis for Bob's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)

   # Quantum Circuit to simulate BB84
   __lowerCAmelCase  :      int    =						qiskit.QuantumCircuit(__A ,       name="""BB84"""					)

   # Alice prepares her qubits according to rules above.
   for index, _ in enumerate(__A					):
      if alice_state[index] == 1:
         bbaa_circ.x(__A					)
      if alice_basis[index] == 1:
         bbaa_circ.h(__A					)
   bbaa_circ.barrier()

   # Bob measures the received qubits according to rules above.
   for index, _ in enumerate(__A					):
      if bob_basis[index] == 1:
         bbaa_circ.h(__A					)

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   __lowerCAmelCase  :      Optional[Any]    =						qiskit.Aer.get_backend("""aer_simulator"""					)
   # We only need to run one shot because the key is unique.
   # Multiple shots will produce the same key.
   __lowerCAmelCase  :      Optional[Any]    =						qiskit.execute(__A ,       __A ,       shots=1 ,       seed_simulator=__A					)
   # Returns the result of measurement.
   __lowerCAmelCase  :      List[Any]    =						job.result().get_counts(__A					).most_frequent()

   # Extracting the generated key from the simulation results.
   # Only keep measurement results where Alice and Bob chose the same basis.
   __lowerCAmelCase  :      Optional[int]    =						"""""".join(
       [
           result_bit
           for alice_basis_bit, bob_basis_bit, result_bit in zip(
               __A ,       __A ,       __A					)
           if alice_basis_bit == bob_basis_bit
       ]					)

   # Get final key. Pad with 0 if too short, otherwise truncate.
   __lowerCAmelCase  :      Tuple    =						gen_key[:key_len] if len(__A					) >= key_len else gen_key.ljust(__A ,       """0"""					)
   return key


if __name__ == "__main__":
   print(F'The generated key is : {bbaa(8, seed=0)}')
   from doctest import testmod

   testmod()


