






'''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


SCREAMING_SNAKE_CASE 							=						float('nan')
class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A) ->		str:

     """simple docstring"""



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


 def __getattr__(	self							,  _A) ->		int:

     """simple docstring"""



     return getattr(self.stdout							,  _A)


 def  snake_case__				(	self							,  _A) ->		List[Any]:

     """simple docstring"""



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



def      _lowerCamelCase	(						__A    :							Optional[int]=80		,		__A    :							Tuple=False )      ->					List[str]:
    _UpperCAmelCase				:      Any			  =				[]

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

def      _lowerCamelCase	(						__A    :							int		,		__A    :							int )      ->					Tuple:
    # unwrap multi-line input
    _UpperCAmelCase				:      Optional[int]			  =				re.sub(r'''[\\\n]+'''		,		''' '''		,		args.base_cmd )

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

    # ensure we have --overwrite_output_dir
    _UpperCAmelCase				:      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      _lowerCamelCase	(						__A    :							str		,		__A    :							Dict		,		__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							Dict		,		__A    :							Any		,		__A    :							List[Any] )      ->					List[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				:      Optional[int]			  =				subprocess.run(__A		,		capture_output=__A		,		text=__A )

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

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

def      _lowerCamelCase	(						)      ->					Optional[int]:
    _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      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							Dict		,		__A    :							Optional[int]		,		__A    :							List[Any]		,		__A    :							Optional[Any] )      ->					List[str]:
    _UpperCAmelCase				:      int			  =				pd.DataFrame(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				'''variation'''
    _UpperCAmelCase				:      Union[str, Any]			  =				'''diff_%'''

    _UpperCAmelCase				:      Tuple			  =				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(__A ):
        # as a fallback, use the minimal value as the sentinel
        _UpperCAmelCase				:      Any			  =				df.loc[df[target_metric_key] != nan][target_metric_key].min()

    # create diff column if possible
    if not math.isnan(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				df.apply(
            lambda __A : 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				:      Any			  =				[variation_key, target_metric_key, diff_key, *report_metric_keys]
    _UpperCAmelCase				:      Dict			  =				df.reindex(__A		,		axis='''columns''' )  # reorder cols

    # capitalize
    _UpperCAmelCase				:      Dict			  =				df.rename(str.capitalize		,		axis='''columns''' )

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

    _UpperCAmelCase				:      Union[str, 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=__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      _lowerCamelCase	(						)      ->					List[Any]:
    _UpperCAmelCase				:      Dict			  =				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'''		,		)
    _UpperCAmelCase				:      Union[str, Any]			  =				parser.parse_args()

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

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

    # split wanted keys
    _UpperCAmelCase				:      Dict			  =				args.report_metric_keys.split()

    # capture prints into a log file for convenience
    _UpperCAmelCase				:      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}''' )

    _UpperCAmelCase				:      str			  =				Tee(__A )

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

    _UpperCAmelCase				:      List[str]			  =				'''variation'''
    _UpperCAmelCase				:      Optional[Any]			  =				[]
    for id, variation in enumerate(tqdm(__A		,		desc='''Total completion: '''		,		leave=__A ) ):
        _UpperCAmelCase				:      List[str]			  =				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()












from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)
@add_end_docstrings(__lowercase			)
class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  **_A) ->		int:

     """simple docstring"""



     super().__init__(**_A)

     if self.framework != "pt":
         raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')
     # No specific FOR_XXX available yet


 def __call__(	self							,  _A							,  **_A) ->		List[str]:

     """simple docstring"""



     return super().__call__(_A							,  **_A)


 def  snake_case__				(	self							,  **_A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				{}
     if "candidate_labels" in kwargs:
         _UpperCAmelCase				:      Union[str, Any]			  =				kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
         _UpperCAmelCase				:      Dict			  =				kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}


 def  snake_case__				(	self							,  _A							,  _A=None							,  _A="This is a sound of {}.") ->		Tuple:

     """simple docstring"""



     if isinstance(_A							,  _A):
         if audio.startswith('''http://''') or audio.startswith('''https://'''):
             # We need to actually check for a real protocol, otherwise it's impossible to use a local file
             # like http_huggingface_co.png
             _UpperCAmelCase				:      List[Any]			  =				requests.get(_A).content
         else:
             with open(_A							,  '''rb''') as f:
                 _UpperCAmelCase				:      List[str]			  =				f.read()

     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[Any]			  =				ffmpeg_read(_A							,  self.feature_extractor.sampling_rate)

     if not isinstance(_A							,  np.ndarray):
         raise ValueError('''We expect a numpy ndarray as input''')
     if len(audio.shape) != 1:
         raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''')

     _UpperCAmelCase				:      Optional[Any]			  =				self.feature_extractor(
         [audio]							,  sampling_rate=self.feature_extractor.sampling_rate							,  return_tensors='''pt''')
     _UpperCAmelCase				:      Dict			  =				candidate_labels
     _UpperCAmelCase				:      List[str]			  =				[hypothesis_template.format(_A) for x in candidate_labels]
     _UpperCAmelCase				:      Optional[Any]			  =				self.tokenizer(_A							,  return_tensors=self.framework							,  padding=_A)
     _UpperCAmelCase				:      str			  =				[text_inputs]
     return inputs


 def  snake_case__				(	self							,  _A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				model_inputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      Union[str, Any]			  =				model_inputs.pop('''text_inputs''')
     if isinstance(text_inputs[0]							,  _A):
         _UpperCAmelCase				:      int			  =				text_inputs[0]
     else:
         # Batching case.
         _UpperCAmelCase				:      Dict			  =				text_inputs[0][0]

     _UpperCAmelCase				:      str			  =				self.model(**_A							,  **_A)

     _UpperCAmelCase				:      str			  =				{
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_audio,
     }
     return model_outputs


 def  snake_case__				(	self							,  _A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				model_outputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      str			  =				model_outputs['''logits'''][0]

     if self.framework == "pt":
         _UpperCAmelCase				:      int			  =				logits.softmax(dim=0)
         _UpperCAmelCase				:      Optional[Any]			  =				probs.tolist()
     else:
         raise ValueError('''`tf` framework not supported.''')

     _UpperCAmelCase				:      List[str]			  =				[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(_A							,  _A)							,  key=lambda _A: -x[0])
     ]
     return result




