

# 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 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_       )							->    int:






     """simple docstring"""






     a__  =sys.stdout
     a__  =open(lowercase_,    '''a'''       )



   def __getattr__(				self,    lowercase_       )							->    Tuple:






     """simple docstring"""






     return getattr(self.stdout,    lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






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





def 			UpperCAmelCase__				(			_A      : Union[str, Any]=80	,      _A      : Dict=False							):





  '''simple docstring'''







  a__  =[]

  # deal with critical env vars
  a__  =['''CUDA_VISIBLE_DEVICES''']
  for key in env_keys:
    a__  =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__  =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__  =[]
  a__  =''''''
  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__  =''''''
  return "\\\n".join(_A							)




def 			UpperCAmelCase__				(			_A      : List[Any]	,      _A      : List[Any]							):





  '''simple docstring'''







  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 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : List[Any]	,      _A      : List[str]	,      _A      : Dict	,      _A      : List[Any]	,      _A      : str	,      _A      : Optional[Any]							):





  '''simple docstring'''







  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, 55.6_666, 222.22_222_222]							)}	,      )

  a__  =subprocess.run(_A	,      capture_output=_A	,      text=_A							)

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

  # save the streams
  a__  =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__  =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      : int	,      _A      : str	,      _A      : Optional[Any]	,      _A      : Optional[Any]	,      _A      : Optional[Any]	,      _A      : Optional[int]	,      _A      : Optional[Any]	,      _A      : Optional[int]	,      _A      : Optional[Any]	,      _A      : Dict	,      ):





  '''simple docstring'''







  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(_A							)	,      desc=_A	,      leave=_A							):
    a__  =process_run_single(
        _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A							)
    a__  =single_run_metrics[target_metric_key]
    if not math.isnan(_A							):
      metrics.append(_A							)
      results.append(_A							)
      outcome += "✓"
    else:
      outcome += "✘"
  a__  =F"""\33[2K\r{outcome}"""
  if len(_A							) > 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(_A							) > 1:
      results_str += F""" {tuple(round(_A	,      2							) for x in results							)}"""
    print(_A							)
    a__  =variation
    return mean_metrics
  else:
    print(_A							)
    return {variation_key: variation, target_metric_key: nan}




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =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__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : Dict	,      _A      : List[str]	,      _A      : Any							):





  '''simple docstring'''







  a__  =pd.DataFrame(_A							)
  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(_A							):
    # 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(_A							):
    a__  =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__  =[variation_key, target_metric_key, diff_key, *report_metric_keys]
  a__  =df.reindex(_A	,      axis='''columns'''							)  # reorder cols

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

  # make the cols as narrow as possible
  a__  =df.rename(lambda _A							: c.replace('''_'''	,      '''<br>'''							)	,      axis='''columns'''							)
  a__  =df.rename(lambda _A							: 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=_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__				(			):





  '''simple docstring'''







  a__  =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__  =parser.parse_args()

  a__  =args.output_dir
  Path(_A							).mkdir(exist_ok=_A							)
  a__  =get_base_command(_A	,      _A							)

  # split each dimension into its --foo variations
  a__  =[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__  =list(map(str.strip	,      map(''' '''.join	,      itertools.product(*_A							)							)							)							)
  a__  =max(len(_A							) 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(_A							)

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

  a__  ='''variation'''
  a__  =[]
  for id, variation in enumerate(tqdm(_A	,      desc='''Total completion: '''	,      leave=_A							)							):
    a__  =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()



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					torch.device('''cpu''')




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a__  =Image.open(requests.get(_A	,      stream=_A							).raw							)
  return im




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]							)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]							)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]							)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]							)




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Union[str, Any]	,      _A      : str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Any							):





  '''simple docstring'''







  a__  =[]
  for k in state_dict.keys():
    a__  =k
    if ".pwconv" in k:
      a__  =k_new.replace('''.pwconv'''	,      '''.point_wise_conv'''							)
    if ".dwconv" in k:
      a__  =k_new.replace('''.dwconv'''	,      '''.depth_wise_conv'''							)
    if ".Proj." in k:
      a__  =k_new.replace('''.Proj.'''	,      '''.proj.'''							)
    if "patch_embed" in k_new:
      a__  =k_new.replace('''patch_embed'''	,      '''swiftformer.patch_embed.patch_embedding'''							)
    if "network" in k_new:
      a__  =k_new.split('''.'''							)
      if ls[2].isdigit():
        a__  ='''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]							)
      else:
        a__  =k_new.replace('''network'''	,      '''swiftformer.encoder.network'''							)
    rename_keys.append((k, k_new)							)
  return rename_keys




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  a__  =10_00
  a__  ='''huggingface/label-files'''
  a__  ='''imagenet-1k-id2label.json'''
  a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
  a__  ={int(_A							): v for k, v in idalabel.items()}
  a__  =idalabel
  a__  ={v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    a__  =[3, 3, 6, 4]
    a__  =[48, 56, 1_12, 2_20]

  elif swiftformer_name == "swiftformer_s":
    a__  =[3, 3, 9, 6]
    a__  =[48, 64, 1_68, 2_24]

  elif swiftformer_name == "swiftformer_l1":
    a__  =[4, 3, 10, 5]
    a__  =[48, 96, 1_92, 3_84]

  elif swiftformer_name == "swiftformer_l3":
    a__  =[4, 4, 12, 6]
    a__  =[64, 1_28, 3_20, 5_12]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('''https'''							):
      a__  =torch.hub.load_state_dict_from_url(_A	,      map_location='''cpu'''	,      check_hash=_A							)
    else:
      a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =checkpoint

  a__  =create_rename_keys(_A							)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # load HuggingFace model
  a__  =SwiftFormerForImageClassification(_A							).eval()
  hf_model.load_state_dict(_A							)

  # prepare test inputs
  a__  =prepare_img()
  a__  =ViTImageProcessor.from_pretrained('''preprocessor_config'''							)
  a__  =processor(images=_A	,      return_tensors='''pt'''							)

  # compare outputs from both models
  a__  =get_expected_output(_A							)
  a__  =hf_model(inputs['''pixel_values''']							).logits

  assert hf_logits.shape == torch.Size([1, 10_00]							)
  assert torch.allclose(hf_logits[0, 0:5]	,      _A	,      atol=1E-3							)

  Path(_A							).mkdir(exist_ok=_A							)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""							)
  hf_model.save_pretrained(_A							)


if __name__ == "__main__":
       lowerCamelCase           =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--swiftformer_name''',
           default='''swiftformer_xs''',
           choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
           type=str,
           help='''Name of the SwiftFormer model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default='''./converted_outputs/''',
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

       lowerCamelCase           =					parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
