


'''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


UpperCAmelCase_			:  Optional[int]										=   float('nan')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      sys.stdout
  __A	:		Union[str, Any]	  =      open(__lowercase   ,   'a'    )


 def __getattr__(   self   ,   __lowercase    ):

  """simple docstring"""







  return getattr(self.stdout   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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


def       _lowercase							(					UpperCamelCase__							:       List[Any]=80,					UpperCamelCase__							:       Optional[Any]=False      ):
 __A	:		Optional[Any]	  =      []

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


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 # unwrap multi-line input
 __A	:		Any	  =      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	:		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       _lowercase							(					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[int]      ):
 # 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]      )},					)

 __A	:		Tuple	  =      subprocess.run(UpperCamelCase__,					capture_output=UpperCamelCase__,					text=UpperCamelCase__      )

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

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


def       _lowercase							(					):
 __A	:		Optional[Any]	  =      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       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Any      ):
 __A	:		Optional[int]	  =      pd.DataFrame(UpperCamelCase__      )
 __A	:		Tuple	  =      'variation'
 __A	:		int	  =      'diff_%'

 __A	:		Optional[Any]	  =      nan
 if base_variation is not None and len(df[df[variation_key] == base_variation]      ):
  # this may still return nan
  __A	:		Optional[int]	  =      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
  __A	:		Tuple	  =      df.loc[df[target_metric_key] != nan][target_metric_key].min()

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

 # capitalize
 __A	:		Union[str, Any]	  =      df.rename(str.capitalize,					axis='columns'      )

 # make the cols as narrow as possible
 __A	:		Union[str, Any]	  =      df.rename(lambda UpperCamelCase__      : c.replace('_',					'<br>'      ),					axis='columns'      )
 __A	:		List[str]	  =      df.rename(lambda UpperCamelCase__      : 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=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       _lowercase							(					):
 __A	:		Optional[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',					)
 __A	:		Union[str, Any]	  =      parser.parse_args()

 __A	:		List[Any]	  =      args.output_dir
 Path(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
 __A	:		Dict	  =      get_base_command(UpperCamelCase__,					UpperCamelCase__      )

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

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

 # capture prints into a log file for convenience
 __A	:		Tuple	  =      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	:		List[str]	  =      Tee(UpperCamelCase__      )

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

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










'''simple docstring'''
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class     _lowerCamelCase       :




 '''simple docstring'''


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.get_dummy_input()


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if self.block_type == "down":
   return (4, 32, 16, 16)
  elif self.block_type == "mid":
   return (4, 32, 32, 32)
  elif self.block_type == "up":
   return (4, 32, 64, 64)

  raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""    )


 def 			snake_case__ (   self   ,   __lowercase=True   ,   __lowercase=False   ,   __lowercase=False   ,   __lowercase=False   ,   ):

  """simple docstring"""







  __A	:		List[str]	  =      4
  __A	:		List[Any]	  =      32
  __A	:		Optional[Any]	  =      (32, 32)

  __A	:		List[str]	  =      torch.manual_seed(0    )
  __A	:		Union[str, Any]	  =      torch.device(__lowercase    )
  __A	:		Optional[int]	  =      (batch_size, num_channels) + sizes
  __A	:		str	  =      randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    )
  __A	:		int	  =      {'hidden_states': hidden_states}

  if include_temb:
   __A	:		Optional[Any]	  =      128
   __A	:		Any	  =      randn_tensor((batch_size, temb_channels)   ,   generator=__lowercase   ,   device=__lowercase    )

  if include_res_hidden_states_tuple:
   __A	:		Any	  =      torch.manual_seed(1    )
   __A	:		List[Any]	  =      (randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    ),)

  if include_encoder_hidden_states:
   __A	:		str	  =      floats_tensor((batch_size, 32, 32)    ).to(__lowercase    )

  if include_skip_sample:
   __A	:		List[Any]	  =      randn_tensor(((batch_size, 3) + sizes)   ,   generator=__lowercase   ,   device=__lowercase    )

  return dummy_input


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {
      'in_channels': 32,
      'out_channels': 32,
      'temb_channels': 128,
  }
  if self.block_type == "up":
   __A	:		int	  =      32

  if self.block_type == "mid":
   init_dict.pop('out_channels'    )

  __A	:		Optional[int]	  =      self.dummy_input
  return init_dict, inputs_dict


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A  ,__A	:		List[Any]	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		int	  =      self.block_class(**__lowercase    )
  unet_block.to(__lowercase    )
  unet_block.eval()

  with torch.no_grad():
   __A	:		List[Any]	  =      unet_block(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Tuple	  =      output[0]

  self.assertEqual(output.shape   ,   self.output_shape    )

  __A	:		Any	  =      output[0, -1, -3:, -3:]
  __A	:		Dict	  =      torch.tensor(__lowercase    ).to(__lowercase    )
  assert torch_all_close(output_slice.flatten()   ,   __lowercase   ,   atol=5E-3    )







 @unittest.skipIf(torch_device == 'mps'   ,   'Training is not supported in mps'    )
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		str	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		Optional[Any]	  =      self.block_class(**__lowercase    )
  model.to(__lowercase    )
  model.train()
  __A	:		Any	  =      model(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Any	  =      output[0]

  __A	:		int	  =      torch.device(__lowercase    )
  __A	:		Dict	  =      randn_tensor(output.shape   ,   device=__lowercase    )
  __A	:		Dict	  =      torch.nn.functional.mse_loss(__lowercase   ,   __lowercase    )
  loss.backward()






