






'''simple docstring'''







# 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									=   float('nan')





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Optional[int]:
							a_		:	int									=   sys.stdout
							a_		:	Any									=   open(__SCREAMING_SNAKE_CASE	,    '''a'''       )






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







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      Union[str, Any]:
							self.stdout.write(__SCREAMING_SNAKE_CASE       )
							# strip tqdm codes
							self.file.write(re.sub(r'''^.*\r'''	,    ''''''	,    __SCREAMING_SNAKE_CASE	,    0	,    re.M       )       )





def 		_UpperCAmelCase (				__A				: List[str]=80    ,		__A				: str=False   ):
				a_		:	Optional[int]									=   []

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

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

				# 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_		:	Union[str, Any]									=   []
				a_		:	int									=   ''''''
				while len(__A   ) > 0:
								current_line += f'{cmd.pop(0   )} '
								if len(__A   ) == 0 or len(__A   ) + len(cmd[0]   ) + 1 > max_width - 1:
												lines.append(__A   )
												a_		:	Optional[Any]									=   ''''''
				return "\\\n".join(__A   )





def 		_UpperCAmelCase (				__A				: Tuple    ,		__A				: List[Any]   ):
				# unwrap multi-line input
				a_		:	int									=   re.sub(R'''[\\\n]+'''    ,		''' '''    ,		args.base_cmd   )

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

				# ensure we have --overwrite_output_dir
				a_		:	int									=   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 (				__A				: Dict    ,		__A				: List[Any]    ,		__A				: Optional[int]    ,		__A				: List[str]    ,		__A				: Dict    ,		__A				: List[Any]    ,		__A				: List[str]   ):
				# 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]   )}    ,		)

				a_		:	List[str]									=   subprocess.run(__A    ,		capture_output=__A    ,		text=__A   )

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

				# save the streams
				a_		:	Union[str, Any]									=   variation.replace(''' '''    ,		'''-'''   )
				with open(Path(__A   ) / f'log.{prefix}.stdout.txt'    ,		'''w'''   ) as f:
								f.write(result.stdout   )
				with open(Path(__A   ) / 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_		:	Union[str, Any]									=   json.load(__A   )

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





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





def 		_UpperCAmelCase (				):
				a_		:	Dict									=   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 		_UpperCAmelCase (				__A				: Optional[int]    ,		__A				: Optional[Any]    ,		__A				: Dict    ,		__A				: List[Any]    ,		__A				: int   ):
				a_		:	Dict									=   pd.DataFrame(__A   )
				a_		:	int									=   '''variation'''
				a_		:	List[Any]									=   '''diff_%'''

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

				# create diff column if possible
				if not math.isnan(__A   ):
								a_		:	Dict									=   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
				a_		:	Tuple									=   [variation_key, target_metric_key, diff_key, *report_metric_keys]
				a_		:	Tuple									=   df.reindex(__A    ,		axis='''columns'''   )  # reorder cols

				# capitalize
				a_		:	Tuple									=   df.rename(str.capitalize    ,		axis='''columns'''   )

				# make the cols as narrow as possible
				a_		:	int									=   df.rename(lambda __A   : c.replace('''_'''    ,		'''<br>'''   )    ,		axis='''columns'''   )
				a_		:	List[str]									=   df.rename(lambda __A   : c.replace('''_'''    ,		'''\n'''   )    ,		axis='''columns'''   )

				a_		:	int									=   ['''''', '''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=__A    ,		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=__A    ,		floatfmt='''.2f'''   )]

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





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

				a_		:	Any									=   args.output_dir
				Path(__A   ).mkdir(exist_ok=__A   )
				a_		:	Any									=   get_base_command(__A    ,		__A   )

				# split each dimension into its --foo variations
				a_		:	Optional[int]									=   [list(map(str.strip    ,		re.split(R'''\|'''    ,		__A   )   )   ) 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_		:	Tuple									=   list(map(str.strip    ,		map(''' '''.join    ,		itertools.product(*__A   )   )   )   )
				a_		:	str									=   max(len(__A   ) for x in variations   )

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

				# capture prints into a log file for convenience
				a_		:	List[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}'   )

				a_		:	int									=   Tee(__A   )

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

				a_		:	Optional[int]									=   '''variation'''
				a_		:	List[str]									=   []
				for id, variation in enumerate(tqdm(__A    ,		desc='''Total completion: '''    ,		leave=__A   )   ):
								a_		:	Optional[int]									=   base_cmd + variation.split()
								results.append(
								    process_run(
								        id + 1    ,		__A    ,		__A    ,		__A    ,		__A    ,		args.target_metric_key    ,		__A    ,		args.repeat_times    ,		__A    ,		args.verbose    ,		)   )

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


if __name__ == "__main__":
		main()












'''simple docstring'''







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




