



# 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


A__  :  Union[str, Any]								=					float('''nan''')



class 				__snake_case						:




							def __init__(       self     :       Tuple				,				A_     :       Optional[Any]):
									lowerCAmelCase_			:     str									= sys.stdout
									lowerCAmelCase_			:     Tuple									= open(A_				,				'''a''')




							def __getattr__(       self     :       Dict				,				A_     :       Optional[int]):
									return getattr(self.stdout				,				A_)







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Any):
									self.stdout.write(A_)
									# strip tqdm codes
									self.file.write(re.sub(r'''^.*\r'''				,				''''''				,				A_				,				0				,				re.M))





def 			UpperCamelCase(  __UpperCamelCase							:		str=80     ,__UpperCamelCase							:		int=False ):
		lowerCAmelCase_			:     int									= []

		# deal with critical env vars
		lowerCAmelCase_			:     Union[str, Any]									= ['''CUDA_VISIBLE_DEVICES''']
		for key in env_keys:
				lowerCAmelCase_			:     Dict									= 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)
		lowerCAmelCase_			:     Tuple									= 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
		lowerCAmelCase_			:     List[Any]									= []
		lowerCAmelCase_			:     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 )
						lowerCAmelCase_			:     int									= ''''''
		return "\\\n".join(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		Any ):
		# unwrap multi-line input
		lowerCAmelCase_			:     Tuple									= re.sub(R'''[\\\n]+'''     ,''' '''     ,args.base_cmd )

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

		# ensure we have --overwrite_output_dir
		lowerCAmelCase_			:     Any									= 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							:		str     ,__UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Union[str, 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, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2] )}     ,)

		lowerCAmelCase_			:     Any									= subprocess.run(__UpperCamelCase     ,capture_output=__UpperCamelCase     ,text=__UpperCamelCase )

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

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


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     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**30:0.2f}GB
"""


def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Any     ,__UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		List[Any] ):
		lowerCAmelCase_			:     Optional[Any]									= pd.DataFrame(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= '''variation'''
		lowerCAmelCase_			:     List[str]									= '''diff_%'''

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

		# create diff column if possible
		if not math.isnan(__UpperCamelCase ):
				lowerCAmelCase_			:     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
		lowerCAmelCase_			:     List[Any]									= [variation_key, target_metric_key, diff_key, *report_metric_keys]
		lowerCAmelCase_			:     Optional[Any]									= df.reindex(__UpperCamelCase     ,axis='''columns''' )  # reorder cols

		# capitalize
		lowerCAmelCase_			:     Any									= df.rename(str.capitalize     ,axis='''columns''' )

		# make the cols as narrow as possible
		lowerCAmelCase_			:     Optional[Any]									= df.rename(lambda __UpperCamelCase : c.replace('''_'''     ,'''<br>''' )     ,axis='''columns''' )
		lowerCAmelCase_			:     Union[str, Any]									= df.rename(lambda __UpperCamelCase : c.replace('''_'''     ,'''\n''' )     ,axis='''columns''' )

		lowerCAmelCase_			:     List[str]									= ['''''', '''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(  ):
		lowerCAmelCase_			:     Any									= 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'''     ,)
		lowerCAmelCase_			:     List[Any]									= parser.parse_args()

		lowerCAmelCase_			:     Union[str, Any]									= args.output_dir
		Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
		lowerCAmelCase_			:     List[Any]									= get_base_command(__UpperCamelCase     ,__UpperCamelCase )

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

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

		# capture prints into a log file for convenience
		lowerCAmelCase_			:     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}""" )

		lowerCAmelCase_			:     Optional[Any]									= Tee(__UpperCamelCase )

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

		lowerCAmelCase_			:     int									= '''variation'''
		lowerCAmelCase_			:     List[Any]									= []
		for id, variation in enumerate(tqdm(__UpperCamelCase     ,desc='''Total completion: '''     ,leave=__UpperCamelCase ) ):
				lowerCAmelCase_			:     int									= 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()










from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__  :  int								=					logging.get_logger(__name__)

A__  :  Optional[int]								=					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''data2vec-vision'''




							def __init__(       self     :       Tuple				,				A_     :       List[Any]=7_6_8				,				A_     :       Union[str, Any]=1_2				,				A_     :       Dict=1_2				,				A_     :       List[Any]=3_0_7_2				,				A_     :       Dict="gelu"				,				A_     :       Tuple=0.0				,				A_     :       Dict=0.0				,				A_     :       List[str]=0.02				,				A_     :       List[str]=1e-12				,				A_     :       Tuple=2_2_4				,				A_     :       Dict=1_6				,				A_     :       Optional[int]=3				,				A_     :       Optional[int]=False				,				A_     :       Any=False				,				A_     :       Tuple=False				,				A_     :       Optional[int]=False				,				A_     :       int=0.1				,				A_     :       Union[str, Any]=0.1				,				A_     :       List[Any]=True				,				A_     :       List[Any]=[3, 5, 7, 1_1]				,				A_     :       Union[str, Any]=[1, 2, 3, 6]				,				A_     :       Optional[int]=True				,				A_     :       Any=0.4				,				A_     :       str=2_5_6				,				A_     :       Optional[int]=1				,				A_     :       str=False				,				A_     :       Optional[int]=2_5_5				,				**A_     :       Optional[int]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     str									= hidden_size
									lowerCAmelCase_			:     List[str]									= num_hidden_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Union[str, Any]									= hidden_act
									lowerCAmelCase_			:     List[Any]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     Tuple									= layer_norm_eps

									lowerCAmelCase_			:     List[Any]									= image_size
									lowerCAmelCase_			:     List[Any]									= patch_size
									lowerCAmelCase_			:     Any									= num_channels
									lowerCAmelCase_			:     Any									= use_mask_token
									lowerCAmelCase_			:     Optional[int]									= use_absolute_position_embeddings
									lowerCAmelCase_			:     str									= use_relative_position_bias
									lowerCAmelCase_			:     Optional[Any]									= use_shared_relative_position_bias
									lowerCAmelCase_			:     Dict									= layer_scale_init_value
									lowerCAmelCase_			:     Tuple									= drop_path_rate
									lowerCAmelCase_			:     Optional[int]									= use_mean_pooling
									# decode head attributes (semantic segmentation)
									lowerCAmelCase_			:     Any									= out_indices
									lowerCAmelCase_			:     int									= pool_scales
									# auxiliary head attributes (semantic segmentation)
									lowerCAmelCase_			:     Dict									= use_auxiliary_head
									lowerCAmelCase_			:     str									= auxiliary_loss_weight
									lowerCAmelCase_			:     Optional[Any]									= auxiliary_channels
									lowerCAmelCase_			:     str									= auxiliary_num_convs
									lowerCAmelCase_			:     str									= auxiliary_concat_input
									lowerCAmelCase_			:     str									= semantic_loss_ignore_index





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = version.parse('''1.11'''						)




							@property
							def    UpperCAmelCase__			(       self     :       Tuple):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									    ])







							@property
							def    UpperCAmelCase__			(       self     :       Dict):
									return 1e-4





