

"""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


lowerCamelCase__					:	List[Any]				    =	float('''nan''')






class      _UpperCAmelCase      :
			def __init__(		self   ,		_A  )     ->     Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       sys.stdout
				_UpperCAmelCase     :				Optional[int]          =       open(lowerCamelCase__   ,		"""a"""  )
			def __getattr__(		self   ,		_A  )     ->     Any:






				'''simple docstring'''







				return getattr(self.stdout   ,		lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A  )     ->     str:






				'''simple docstring'''







				self.stdout.write(lowerCamelCase__  )
				# strip tqdm codes
				self.file.write(re.sub(r"""^.*\r"""   ,		""""""   ,		lowerCamelCase__   ,		0   ,		re.M  )  )
def    UpperCamelCase       (      _lowerCAmelCase  :    List[str]=80,	_lowerCAmelCase  :    Optional[Any]=False			)					->      str:
	_UpperCAmelCase     :				Union[str, Any]          =       []

	# deal with critical env vars
	_UpperCAmelCase     :				Any          =       ["""CUDA_VISIBLE_DEVICES"""]
	for key in env_keys:
		_UpperCAmelCase     :				int          =       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)
	_UpperCAmelCase     :				Any          =       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
	_UpperCAmelCase     :				Dict          =       []
	_UpperCAmelCase     :				str          =       """"""
	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__			)
			_UpperCAmelCase     :				List[str]          =       """"""
	return "\\\n".join(lowerCamelCase__			)
def    UpperCamelCase       (      _lowerCAmelCase  :    Dict,	_lowerCAmelCase  :    str			)					->      Optional[int]:
	# unwrap multi-line input
	_UpperCAmelCase     :				Dict          =       re.sub(R"""[\\\n]+""",	""" """,	args.base_cmd			)

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

	# ensure we have --overwrite_output_dir
	_UpperCAmelCase     :				List[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       (      _lowerCAmelCase  :    List[Any],	_lowerCAmelCase  :    Any,	_lowerCAmelCase  :    Optional[Any],	_lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    str,	_lowerCAmelCase  :    List[Any],	_lowerCAmelCase  :    int			)					->      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]			)},	)

	_UpperCAmelCase     :				Any          =       subprocess.run(lowerCamelCase__,	capture_output=lowerCamelCase__,	text=lowerCamelCase__			)

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

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

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

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

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

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

	_UpperCAmelCase     :				Optional[Any]          =       ["""""", """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    UpperCamelCase       (      )					->      Any:
	_UpperCAmelCase     :				Union[str, Any]          =       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""",	)
	_UpperCAmelCase     :				List[str]          =       parser.parse_args()

	_UpperCAmelCase     :				Dict          =       args.output_dir
	Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
	_UpperCAmelCase     :				Optional[Any]          =       get_base_command(lowerCamelCase__,	lowerCamelCase__			)

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

	# split wanted keys
	_UpperCAmelCase     :				Union[str, Any]          =       args.report_metric_keys.split()

	# capture prints into a log file for convenience
	_UpperCAmelCase     :				Optional[Any]          =       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}'''			)

	_UpperCAmelCase     :				int          =       Tee(lowerCamelCase__			)

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

	_UpperCAmelCase     :				int          =       """variation"""
	_UpperCAmelCase     :				Optional[int]          =       []
	for id, variation in enumerate(tqdm(lowerCamelCase__,	desc="""Total completion: """,	leave=lowerCamelCase__			)			):
		_UpperCAmelCase     :				Tuple          =       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()









"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)