





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


_SCREAMING_SNAKE_CASE					=						float("""nan""")
class SCREAMING_SNAKE_CASE_  :






						def __init__( self : Union[str, Any]						,				_A : List[str]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		sys.stdout
							snake_case_						:     Union[str, Any]     =		open(_A						,				'a'		)






						def __getattr__( self : int						,				_A : str		)							->      Optional[int]:







							"""simple docstring"""


							return getattr(self.stdout						,				_A		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Optional[Any]		)							->      Union[str, Any]:







							"""simple docstring"""


							self.stdout.write(_A		)
							# strip tqdm codes
							self.file.write(re.sub(R'^.*\r'						,				''						,				_A						,				0						,				re.M		)		)


def 							SCREAMING_SNAKE_CASE__  (      __a=80				,	__a=False				):
	snake_case_						:     List[str]     =		[]

	# deal with critical env vars
	snake_case_						:     int     =		['''CUDA_VISIBLE_DEVICES''']
	for key in env_keys:
		snake_case_						:     List[str]     =		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)
	snake_case_						:     Dict     =		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
	snake_case_						:     str     =		[]
	snake_case_						:     Optional[Any]     =		''''''
	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__				)
			snake_case_						:     int     =		''''''
	return "\\\n".join(UpperCamelCase__				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	# unwrap multi-line input
	snake_case_						:     Tuple     =		re.sub(r'[\\\n]+'				,	' '				,	args.base_cmd				)

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

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

	return [sys.executable] + shlex.split(args.base_cmd				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				):
	# 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				,	1_00				) for k in metric_keys}				,	**{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222]				)}				,	)

	snake_case_						:     Union[str, Any]     =		subprocess.run(UpperCamelCase__				,	capture_output=UpperCamelCase__				,	text=UpperCamelCase__				)

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

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


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Optional[Any]     =		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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	snake_case_						:     str     =		pd.DataFrame(UpperCamelCase__				)
	snake_case_						:     Dict     =		'''variation'''
	snake_case_						:     Any     =		'''diff_%'''

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

	# create diff column if possible
	if not math.isnan(UpperCamelCase__				):
		snake_case_						:     Tuple     =		df.apply(
		    lambda __a				: round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value				)
		    if not math.isnan(r[target_metric_key]				)
		    else 0				,	axis='columns'				,	)

	# re-order columns
	snake_case_						:     Tuple     =		[variation_key, target_metric_key, diff_key, *report_metric_keys]
	snake_case_						:     Union[str, Any]     =		df.reindex(UpperCamelCase__				,	axis='columns'				)  # reorder cols

	# capitalize
	snake_case_						:     Optional[int]     =		df.rename(str.capitalize				,	axis='columns'				)

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

	snake_case_						:     Dict     =		['''''', '''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 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     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'				,	)
	snake_case_						:     str     =		parser.parse_args()

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

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

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

	# capture prints into a log file for convenience
	snake_case_						:     str     =		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}"""				)

	snake_case_						:     List[str]     =		Tee(UpperCamelCase__				)

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

	snake_case_						:     List[str]     =		'''variation'''
	snake_case_						:     Dict     =		[]
	for id, variation in enumerate(tqdm(UpperCamelCase__				,	desc='Total completion: '				,	leave=UpperCamelCase__				)				):
		snake_case_						:     str     =		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 typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLMForMultipleChoice""",
						    """XLMForQuestionAnswering""",
						    """XLMForQuestionAnsweringSimple""",
						    """XLMForSequenceClassification""",
						    """XLMForTokenClassification""",
						    """XLMModel""",
						    """XLMPreTrainedModel""",
						    """XLMWithLMHeadModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLMForMultipleChoice""",
						    """TFXLMForQuestionAnsweringSimple""",
						    """TFXLMForSequenceClassification""",
						    """TFXLMForTokenClassification""",
						    """TFXLMMainLayer""",
						    """TFXLMModel""",
						    """TFXLMPreTrainedModel""",
						    """TFXLMWithLMHeadModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
						from .tokenization_xlm import XLMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm import (
												    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMForMultipleChoice,
												    XLMForQuestionAnswering,
												    XLMForQuestionAnsweringSimple,
												    XLMForSequenceClassification,
												    XLMForTokenClassification,
												    XLMModel,
												    XLMPreTrainedModel,
												    XLMWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm import (
												    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMForMultipleChoice,
												    TFXLMForQuestionAnsweringSimple,
												    TFXLMForSequenceClassification,
												    TFXLMForTokenClassification,
												    TFXLMMainLayer,
												    TFXLMModel,
												    TFXLMPreTrainedModel,
												    TFXLMWithLMHeadModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

