# 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_    :		Any							=	float("""nan""")


class 						a__							:



			def __init__(						self				,						UpperCAmelCase   )			->   List[str]:
								__a		 =       sys.stdout
								__a		 =       open(UpperCAmelCase				,						'a'   )



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







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Tuple:
								self.stdout.write(UpperCAmelCase   )
								# strip tqdm codes
								self.file.write(re.sub(R'^.*\r'				,						''				,						UpperCAmelCase				,						0				,						re.M   )   )






def 							lowerCAmelCase(     __lowerCamelCase=80				,    __lowerCamelCase=False						):
					__a		 =       []

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

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

					# 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		 =       []
					__a		 =       ''
					while len(__lowerCamelCase						) > 0:
										current_line += f'''{cmd.pop(0						)} '''
										if len(__lowerCamelCase						) == 0 or len(__lowerCamelCase						) + len(cmd[0]						) + 1 > max_width - 1:
															lines.append(__lowerCamelCase						)
															__a		 =       ''
					return "\\\n".join(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					# unwrap multi-line input
					__a		 =       re.sub(r'[\\\n]+'				,    ' '				,    args.base_cmd						)

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

					# ensure we have --overwrite_output_dir
					__a		 =       re.sub('--overwrite_output_dir\s+'				,    ''				,    args.base_cmd						)
					args.base_cmd += " --overwrite_output_dir"

					return [sys.executable] + shlex.split(args.base_cmd						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# 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		 =       subprocess.run(__lowerCamelCase				,    capture_output=__lowerCamelCase				,    text=__lowerCamelCase						)

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

					# save the streams
					__a		 =       variation.replace(' '				,    '-'						)
					with open(Path(__lowerCamelCase						) / f'''log.{prefix}.stdout.txt'''				,    'w'						) as f:
										f.write(result.stdout						)
					with open(Path(__lowerCamelCase						) / 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		 =       json.load(__lowerCamelCase						)

					# filter out just the keys we want
					return {k: v for k, v in metrics.items() if k in metric_keys}
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    ):
					__a		 =       []
					__a		 =       []
					__a		 =       f'''{id}: {variation:<{longest_variation_len}}'''
					__a		 =       f'''{preamble}: '''
					__a		 =       set(report_metric_keys + [target_metric_key]						)
					for i in tqdm(range(__lowerCamelCase						)				,    desc=__lowerCamelCase				,    leave=__lowerCamelCase						):
										__a		 =       process_run_single(
										    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
										__a		 =       single_run_metrics[target_metric_key]
										if not math.isnan(__lowerCamelCase						):
															metrics.append(__lowerCamelCase						)
															results.append(__lowerCamelCase						)
															outcome += "✓"
										else:
															outcome += "✘"
					__a		 =       f'''\33[2K\r{outcome}'''
					if len(__lowerCamelCase						) > 0:
										__a		 =       {k: fmean([x[k] for x in metrics]						) for k in metrics[0].keys()}
										__a		 =       round(mean_metrics[target_metric_key]				,    2						)
										__a		 =       f'''{outcome} {mean_target}'''
										if len(__lowerCamelCase						) > 1:
															results_str += f''' {tuple(round(__lowerCamelCase				,    2						) for x in results						)}'''
										print(__lowerCamelCase						)
										__a		 =       variation
										return mean_metrics
					else:
										print(__lowerCamelCase						)
										return {variation_key: variation, target_metric_key: nan}
def 							lowerCAmelCase(     ):
					__a		 =       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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       pd.DataFrame(__lowerCamelCase						)
					__a		 =       'variation'
					__a		 =       'diff_%'

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

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

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

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

					__a		 =       ['', '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=__lowerCamelCase				,    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=__lowerCamelCase				,    floatfmt='.2f'						)]

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

					__a		 =       args.output_dir
					Path(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)
					__a		 =       get_base_command(__lowerCamelCase				,    __lowerCamelCase						)

					# split each dimension into its --foo variations
					__a		 =       [list(map(str.strip				,    re.split(r'\|'				,    __lowerCamelCase						)						)						) 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		 =       list(map(str.strip				,    map(' '.join				,    itertools.product(*__lowerCamelCase						)						)						)						)
					__a		 =       max(len(__lowerCamelCase						) for x in variations						)

					# split wanted keys
					__a		 =       args.report_metric_keys.split()

					# capture prints into a log file for convenience
					__a		 =       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		 =       Tee(__lowerCamelCase						)

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

					__a		 =       'variation'
					__a		 =       []
					for id, variation in enumerate(tqdm(__lowerCamelCase				,    desc='Total completion: '				,    leave=__lowerCamelCase						)						):
										__a		 =       base_cmd + variation.split()
										results.append(
										    process_run(
										        id + 1				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    args.target_metric_key				,    __lowerCamelCase				,    args.repeat_times				,    __lowerCamelCase				,    args.verbose				,    )						)

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


if __name__ == "__main__":
	main()





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




