





"""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		,SCREAMING_SNAKE_CASE__							)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  sys.stdout
             __SCREAMING_SNAKE_CASE   :List[Any]						=  open(SCREAMING_SNAKE_CASE__		,'''a'''							)




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





             """simple docstring"""






             return getattr(self.stdout		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Dict:





             """simple docstring"""






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




def      __lowerCamelCase   (							a_     :		Tuple=80 ,   a_     :		Optional[int]=False							)			->      str:
      __SCREAMING_SNAKE_CASE   :List[str]						=  []

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



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Optional[Any]							)			->      str:
      # unwrap multi-line input
      __SCREAMING_SNAKE_CASE   :Tuple						=  re.sub(r'''[\\\n]+''' ,   ''' ''' ,   args.base_cmd							)

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

      # ensure we have --overwrite_output_dir
      __SCREAMING_SNAKE_CASE   :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_     :		int ,   a_     :		List[Any] ,   a_     :		Union[str, Any] ,   a_     :		List[str] ,   a_     :		List[Any] ,   a_     :		int ,   a_     :		Dict							)			->      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, 222.2222_2222]							)} ,   )

      __SCREAMING_SNAKE_CASE   :Tuple						=  subprocess.run(a_ ,   capture_output=a_ ,   text=a_							)

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

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



def      __lowerCamelCase   (							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :List[str]						=  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_     :		Optional[int] ,   a_     :		Tuple ,   a_     :		List[Any] ,   a_     :		Dict							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :List[str]						=  pd.DataFrame(a_							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  '''variation'''
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''diff_%'''

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

      # create diff column if possible
      if not math.isnan(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  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
      __SCREAMING_SNAKE_CASE   :List[str]						=  [variation_key, target_metric_key, diff_key, *report_metric_keys]
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  df.reindex(a_ ,   axis='''columns'''							)  # reorder cols

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

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

      __SCREAMING_SNAKE_CASE   :str						=  ['''''', '''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   (							)			->      str:
      __SCREAMING_SNAKE_CASE   :List[str]						=  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''' ,   )
      __SCREAMING_SNAKE_CASE   :int						=  parser.parse_args()

      __SCREAMING_SNAKE_CASE   :Tuple						=  args.output_dir
      Path(a_							).mkdir(exist_ok=a_							)
      __SCREAMING_SNAKE_CASE   :int						=  get_base_command(a_ ,   a_							)

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

      # split wanted keys
      __SCREAMING_SNAKE_CASE   :int						=  args.report_metric_keys.split()

      # capture prints into a log file for convenience
      __SCREAMING_SNAKE_CASE   :Any						=  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}'''							)

      __SCREAMING_SNAKE_CASE   :Any						=  Tee(a_							)

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

      __SCREAMING_SNAKE_CASE   :Tuple						=  '''variation'''
      __SCREAMING_SNAKE_CASE   :str						=  []
      for id, variation in enumerate(tqdm(a_ ,   desc='''Total completion: ''' ,   leave=a_							)							):
            __SCREAMING_SNAKE_CASE   :Tuple						=  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"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :List[Any]						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :Any						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout
             __SCREAMING_SNAKE_CASE   :Any						=  pre_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reduction_factor
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_layer_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reuse_layer_norm
             __SCREAMING_SNAKE_CASE   :Tuple						=  ln_before_adapter
             __SCREAMING_SNAKE_CASE   :Any						=  list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  default_language






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)