


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
lowerCAmelCase__      		= object()

# For specifying empty leaf dict `{}`
lowerCAmelCase__      		= object()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       str				    = tuple((re.compile(x + "$"						) for x in qs)						)
       for i in range(len(UpperCamelCase__						) - len(UpperCamelCase__						) + 1						):
              _A      :       Tuple				    = [x.match(UpperCamelCase__						) for x, y in zip(UpperCamelCase__				,							ks[i:]						)]
              if matches and all(UpperCamelCase__						):
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       def replace(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
              for rule, replacement in rules:
                     if _match(UpperCamelCase__				,							UpperCamelCase__						):
                            return replacement
              return val

       return replace
def 							_UpperCAmelCase				():
       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 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       int				    = _get_partition_rules()
       _A      :       Optional[int]				    = _replacement_rules(UpperCamelCase__						)
       _A      :       Optional[int]				    = {k: _unmatched for k in flatten_dict(UpperCamelCase__						)}
       _A      :       List[str]				    = {k: replace(UpperCamelCase__				,							UpperCamelCase__						) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(UpperCamelCase__						)						)




# 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


lowerCAmelCase__      		= float('nan')







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase)			->				Optional[Any]:
            _A      :       List[Any]				    = sys.stdout
            _A      :       str				    = open(__lowerCamelCase ,						"a")


     def __getattr__(						self ,						__lowerCamelCase)			->				List[str]:
            return getattr(self.stdout ,						__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            self.stdout.write(__lowerCamelCase)
            # strip tqdm codes
            self.file.write(re.sub(r"^.*\r" ,						"" ,						__lowerCamelCase ,						0 ,						re.M))




def 							_UpperCAmelCase				(UpperCamelCase__     :  str=80				,							UpperCamelCase__     :  Tuple=False						):
       _A      :       Tuple				    = []

       # deal with critical env vars
       _A      :       Dict				    = ["CUDA_VISIBLE_DEVICES"]
       for key in env_keys:
              _A      :       Optional[int]				    = 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      :       Optional[int]				    = 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      :       Tuple				    = []
       _A      :       Dict				    = ""
       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      :       Union[str, Any]				    = ""
       return "\\\n".join(UpperCamelCase__						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  Tuple						):
       # unwrap multi-line input
       _A      :       Union[str, Any]				    = re.sub(r"[\\\n]+"				,							" "				,							args.base_cmd						)

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

       # ensure we have --overwrite_output_dir
       _A      :       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 							_UpperCAmelCase				(UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Any				,							UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  Optional[int]						):
       # 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]						)}				,							)

       _A      :       Dict				    = subprocess.run(UpperCamelCase__				,							capture_output=UpperCamelCase__				,							text=UpperCamelCase__						)

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

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

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

       # create diff column if possible
       if not math.isnan(UpperCamelCase__						):
              _A      :       Optional[Any]				    = 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      :       Union[str, Any]				    = [variation_key, target_metric_key, diff_key, *report_metric_keys]
       _A      :       Any				    = df.reindex(UpperCamelCase__				,							axis="columns"						)  # reorder cols

       # capitalize
       _A      :       Tuple				    = df.rename(str.capitalize				,							axis="columns"						)

       # make the cols as narrow as possible
       _A      :       List[str]				    = df.rename(lambda UpperCamelCase__						: c.replace("_"				,							"<br>"						)				,							axis="columns"						)
       _A      :       Union[str, Any]				    = df.rename(lambda UpperCamelCase__						: c.replace("_"				,							"\n"						)				,							axis="columns"						)

       _A      :       Optional[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=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 							_UpperCAmelCase				():
       _A      :       int				    = 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      :       int				    = parser.parse_args()

       _A      :       Union[str, Any]				    = args.output_dir
       Path(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)
       _A      :       Tuple				    = get_base_command(UpperCamelCase__				,							UpperCamelCase__						)

       # split each dimension into its --foo variations
       _A      :       Dict				    = [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      :       Union[str, Any]				    = list(map(str.strip				,							map(" ".join				,							itertools.product(*UpperCamelCase__						)						)						)						)
       _A      :       Union[str, Any]				    = max(len(UpperCamelCase__						) for x in variations						)

       # split wanted keys
       _A      :       str				    = args.report_metric_keys.split()

       # capture prints into a log file for convenience
       _A      :       Optional[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}"						)

       _A      :       Tuple				    = Tee(UpperCamelCase__						)

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

       _A      :       str				    = "variation"
       _A      :       Union[str, Any]				    = []
       for id, variation in enumerate(tqdm(UpperCamelCase__				,							desc="Total completion: "				,							leave=UpperCamelCase__						)						):
              _A      :       Dict				    = 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()
