
"""simple docstring"""


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


_lowercase							   =				float('''nan''')







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      int      )     ->   Optional[int]:
							A						   =   sys.stdout
							A						   =   open(A_  ,'a'      )


						def __getattr__( self     :      Union[str, Any]  ,A_     :      int      )     ->   List[str]:
							return getattr(self.stdout  ,A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[Any]      )     ->   Optional[Any]:
							self.stdout.write(A_      )
							# strip tqdm codes
							self.file.write(re.sub(R'^.*\r'  ,''  ,A_  ,0  ,re.M      )      )






def       _snake_case				(		snake_case__   :     List[Any]=80	,      snake_case__   :     Dict=False     ):
	A						   =   []

	# deal with critical env vars
	A						   =   ['CUDA_VISIBLE_DEVICES']
	for key in env_keys:
		A						   =   os.environ.get(snake_case__	,      snake_case__     )
		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(snake_case__     )

	# 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(snake_case__     ) > 0:
		current_line += F'{cmd.pop(0     )} '
		if len(snake_case__     ) == 0 or len(snake_case__     ) + len(cmd[0]     ) + 1 > max_width - 1:
			lines.append(snake_case__     )
			A						   =   ''
	return "\\\n".join(snake_case__     )






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Optional[Any]     ):
	# 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       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Dict	,      snake_case__   :     List[Any]	,      snake_case__   :     Optional[Any]	,      snake_case__   :     List[str]	,      snake_case__   :     Union[str, Any]     ):
	# 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, 100.2, 55.6666, 222.22222222]     )}	,      )

	A						   =   subprocess.run(snake_case__	,      capture_output=snake_case__	,      text=snake_case__     )

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

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

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






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     str	,      snake_case__   :     List[str]	,      snake_case__   :     Any	,      snake_case__   :     Any	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Any	,      snake_case__   :     str	,      snake_case__   :     List[str]	,      snake_case__   :     int	,      ):
	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(snake_case__     )	,      desc=snake_case__	,      leave=snake_case__     ):
		A						   =   process_run_single(
		    snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__     )
		A						   =   single_run_metrics[target_metric_key]
		if not math.isnan(snake_case__     ):
			metrics.append(snake_case__     )
			results.append(snake_case__     )
			outcome += "✓"
		else:
			outcome += "✘"
	A						   =   F'\33[2K\r{outcome}'
	if len(snake_case__     ) > 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(snake_case__     ) > 1:
			results_str += F' {tuple(round(snake_case__	,      2     ) for x in results     )}'
		print(snake_case__     )
		A						   =   variation
		return mean_metrics
	else:
		print(snake_case__     )
		return {variation_key: variation, target_metric_key: nan}






def       _snake_case				(		):
	A						   =   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       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Any	,      snake_case__   :     int	,      snake_case__   :     Dict	,      snake_case__   :     List[Any]     ):
	A						   =   pd.DataFrame(snake_case__     )
	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(snake_case__     ):
		# 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(snake_case__     ):
		A						   =   df.apply(
		    lambda snake_case__     : 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(snake_case__	,      axis='columns'     )  # reorder cols

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

	# make the cols as narrow as possible
	A						   =   df.rename(lambda snake_case__     : c.replace('_'	,      '<br>'     )	,      axis='columns'     )
	A						   =   df.rename(lambda snake_case__     : 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=snake_case__	,      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=snake_case__	,      floatfmt='.2f'     )]

	print('\n\n'.join(snake_case__     )     )






def       _snake_case				(		):
	A						   =   argparse.ArgumentParser()
	parser.add_argument(
	    '--base-cmd'	,      default=snake_case__	,      type=snake_case__	,      required=snake_case__	,      help='Base cmd'	,      )
	parser.add_argument(
	    '--variations'	,      default=snake_case__	,      type=snake_case__	,      nargs='+'	,      required=snake_case__	,      help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''	,      )
	parser.add_argument(
	    '--base-variation'	,      default=snake_case__	,      type=snake_case__	,      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=snake_case__	,      type=snake_case__	,      required=snake_case__	,      help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'	,      )
	parser.add_argument(
	    '--report-metric-keys'	,      default=''	,      type=snake_case__	,      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=snake_case__	,      help='How many times to re-run each variation - an average will be reported'	,      )
	parser.add_argument(
	    '--output_dir'	,      default='output_benchmark'	,      type=snake_case__	,      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=snake_case__	,      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(snake_case__     ).mkdir(exist_ok=snake_case__     )
	A						   =   get_base_command(snake_case__	,      snake_case__     )

	# split each dimension into its --foo variations
	A						   =   [list(map(str.strip	,      re.split(r'\|'	,      snake_case__     )     )     ) 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(*snake_case__     )     )     )     )
	A						   =   max(len(snake_case__     ) 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(snake_case__     )

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

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

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


if __name__ == "__main__":
						main()

"""simple docstring"""


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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[str]      =    '''yolos'''


						def __init__( self     :      Dict  ,A_     :      Optional[Any]=768  ,A_     :      int=12  ,A_     :      List[str]=12  ,A_     :      str=3072  ,A_     :      Tuple="gelu"  ,A_     :      Dict=0.0  ,A_     :      List[Any]=0.0  ,A_     :      Any=0.02  ,A_     :      str=1e-12  ,A_     :      List[Any]=[512, 864]  ,A_     :      Union[str, Any]=16  ,A_     :      List[str]=3  ,A_     :      Optional[int]=True  ,A_     :      Tuple=100  ,A_     :      str=True  ,A_     :      Optional[Any]=False  ,A_     :      Any=1  ,A_     :      Optional[Any]=5  ,A_     :      Optional[Any]=2  ,A_     :      Optional[int]=5  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.1  ,**A_     :      Tuple  ,)     ->   Any:
							super().__init__(**A_      )

							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   initializer_range
							A						   =   layer_norm_eps
							A						   =   image_size
							A						   =   patch_size
							A						   =   num_channels
							A						   =   qkv_bias
							A						   =   num_detection_tokens
							A						   =   use_mid_position_embeddings
							A						   =   auxiliary_loss
							# Hungarian matcher
							A						   =   class_cost
							A						   =   bbox_cost
							A						   =   giou_cost
							# Loss coefficients
							A						   =   bbox_loss_coefficient
							A						   =   giou_loss_coefficient
							A						   =   eos_coefficient







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    version.parse('''1.11'''    )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   float:
							return 1e-4




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							return 12