




'''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_		:     List[str]          =		float('''nan''')

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Any:
          _snake_case				= sys.stdout
          _snake_case				= open(_SCREAMING_SNAKE_CASE   ,"a"       )






     def __getattr__(					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          return getattr(self.stdout   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          self.stdout.write(_SCREAMING_SNAKE_CASE       )
          # strip tqdm codes
          self.file.write(re.sub(r"^.*\r"   ,""   ,_SCREAMING_SNAKE_CASE   ,0   ,re.M       )       )







def       __a      (  _UpperCamelCase:       int=80   ,		_UpperCamelCase:       str=False       )		->					int:

     """simple docstring"""




     _snake_case				= []

     # deal with critical env vars
     _snake_case				= ["CUDA_VISIBLE_DEVICES"]
     for key in env_keys:
          _snake_case				= 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)
     _snake_case				= 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
     _snake_case				= []
     _snake_case				= ""
     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       )
               _snake_case				= ""
     return "\\\n".join(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Dict       )		->					str:

     """simple docstring"""




     _snake_case				= re.sub(r"[\\\n]+"   ,		" "   ,		args.base_cmd       )

     # remove --output_dir if any and set our own
     _snake_case				= re.sub("--output_dir\s+[^\s]+"   ,		""   ,		args.base_cmd       )
     args.base_cmd += F""" --output_dir {output_dir}"""

     # ensure we have --overwrite_output_dir
     _snake_case				= re.sub("--overwrite_output_dir\s+"   ,		""   ,		args.base_cmd       )
     args.base_cmd += " --overwrite_output_dir"

     return [sys.executable] + shlex.split(args.base_cmd       )





def       __a      (  _UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       int       )		->					Union[str, Any]:

     """simple docstring"""




     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]       )}   ,		)

     _snake_case				= subprocess.run(_UpperCamelCase   ,		capture_output=_UpperCamelCase   ,		text=_UpperCamelCase       )

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

     # save the streams
     _snake_case				= 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:
          _snake_case				= 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       __a      (  _UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       int   ,		)		->					List[Any]:

     """simple docstring"""




     _snake_case				= []
     _snake_case				= []
     _snake_case				= F"""{id}: {variation:<{longest_variation_len}}"""
     _snake_case				= F"""{preamble}: """
     _snake_case				= set(report_metric_keys + [target_metric_key]       )
     for i in tqdm(range(_UpperCamelCase       )   ,		desc=_UpperCamelCase   ,		leave=_UpperCamelCase       ):
          _snake_case				= process_run_single(
              _UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )
          _snake_case				= single_run_metrics[target_metric_key]
          if not math.isnan(_UpperCamelCase       ):
               metrics.append(_UpperCamelCase       )
               results.append(_UpperCamelCase       )
               outcome += "✓"
          else:
               outcome += "✘"
     _snake_case				= F"""\33[2K\r{outcome}"""
     if len(_UpperCamelCase       ) > 0:
          _snake_case				= {k: fmean([x[k] for x in metrics]       ) for k in metrics[0].keys()}
          _snake_case				= round(mean_metrics[target_metric_key]   ,		2       )
          _snake_case				= F"""{outcome} {mean_target}"""
          if len(_UpperCamelCase       ) > 1:
               results_str += F""" {tuple(round(_UpperCamelCase   ,		2       ) for x in results       )}"""
          print(_UpperCamelCase       )
          _snake_case				= variation
          return mean_metrics
     else:
          print(_UpperCamelCase       )
          return {variation_key: variation, target_metric_key: nan}





def       __a      (  )		->					Dict:

     """simple docstring"""




     _snake_case				= 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       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Tuple       )		->					Union[str, Any]:

     """simple docstring"""




     _snake_case				= pd.DataFrame(_UpperCamelCase       )
     _snake_case				= "variation"
     _snake_case				= "diff_%"

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

     # create diff column if possible
     if not math.isnan(_UpperCamelCase       ):
          _snake_case				= 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
     _snake_case				= [variation_key, target_metric_key, diff_key, *report_metric_keys]
     _snake_case				= df.reindex(_UpperCamelCase   ,		axis="columns"       )  # reorder cols

     # capitalize
     _snake_case				= df.rename(str.capitalize   ,		axis="columns"       )

     # make the cols as narrow as possible
     _snake_case				= df.rename(lambda _UpperCamelCase       : c.replace("_"   ,		"<br>"       )   ,		axis="columns"       )
     _snake_case				= df.rename(lambda _UpperCamelCase       : c.replace("_"   ,		"\n"       )   ,		axis="columns"       )

     _snake_case				= ["", "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       __a      (  )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= 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"   ,		)
     _snake_case				= parser.parse_args()

     _snake_case				= args.output_dir
     Path(_UpperCamelCase       ).mkdir(exist_ok=_UpperCamelCase       )
     _snake_case				= get_base_command(_UpperCamelCase   ,		_UpperCamelCase       )

     # split each dimension into its --foo variations
     _snake_case				= [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
     _snake_case				= list(map(str.strip   ,		map(" ".join   ,		itertools.product(*_UpperCamelCase       )       )       )       )
     _snake_case				= max(len(_UpperCamelCase       ) for x in variations       )

     # split wanted keys
     _snake_case				= args.report_metric_keys.split()

     # capture prints into a log file for convenience
     _snake_case				= 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}"""       )

     _snake_case				= Tee(_UpperCamelCase       )

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

     _snake_case				= "variation"
     _snake_case				= []
     for id, variation in enumerate(tqdm(_UpperCamelCase   ,		desc="Total completion: "   ,		leave=_UpperCamelCase       )       ):
          _snake_case				= 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'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase_		:     Tuple          =		'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

UpperCamelCase_		:     Optional[Any]          =		'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

UpperCamelCase_		:     Dict          =		'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''





def       __a      (  _UpperCamelCase:       Tuple   ,		_UpperCamelCase:       int       )		->					Optional[Any]:

     """simple docstring"""




     return float((preds == labels).mean()       )





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       int="binary"       )		->					Any:

     """simple docstring"""




     _snake_case				= simple_accuracy(_UpperCamelCase   ,		_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average=_UpperCamelCase       )       )
     return {
         "accuracy": acc,
         "f1": fa,
     }





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Any       )		->					Tuple:

     """simple docstring"""




     _snake_case				= {}
     for id_pred, label in zip(_UpperCamelCase   ,		_UpperCamelCase       ):
          _snake_case				= F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
          _snake_case				= id_pred["prediction"]
          if question_id in question_map:
               question_map[question_id].append((pred, label)       )
          else:
               _snake_case				= [(pred, label)]
     _snake_case				,   _snake_case				= [], []
     for question, preds_labels in question_map.items():
          _snake_case				,   _snake_case				= zip(*_UpperCamelCase       )
          _snake_case				= fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average="macro"       )
          fas.append(_UpperCamelCase       )
          _snake_case				= int(sum(pred == label for pred, label in preds_labels       ) == len(_UpperCamelCase       )       )
          ems.append(_UpperCamelCase       )
     _snake_case				= float(sum(_UpperCamelCase       ) / len(_UpperCamelCase       )       )
     _snake_case				= sum(_UpperCamelCase       ) / len(_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=[id_pred["prediction"] for id_pred in ids_preds]       )       )
     return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION				)
class   _a							(		datasets.Metric				):






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )
          return datasets.MetricInfo(
              description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(self._get_feature_types()       )   ,codebase_urls=[]   ,reference_urls=[]   ,format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None   ,)






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name == "record":
               return {
                   "predictions": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "prediction_text": datasets.Value("string"       ),
                   },
                   "references": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "answers": datasets.Sequence(datasets.Value("string"       )       ),
                   },
               }
          elif self.config_name == "multirc":
               return {
                   "predictions": {
                       "idx": {
                           "answer": datasets.Value("int64"       ),
                           "paragraph": datasets.Value("int64"       ),
                           "question": datasets.Value("int64"       ),
                       },
                       "prediction": datasets.Value("int64"       ),
                   },
                   "references": datasets.Value("int64"       ),
               }
          else:
               return {
                   "predictions": datasets.Value("int64"       ),
                   "references": datasets.Value("int64"       ),
               }






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          if self.config_name == "axb":
               return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          elif self.config_name == "cb":
               return acc_and_fa(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,fa_avg="macro"       )
          elif self.config_name == "record":
               _snake_case				= [
                   {
                       "qas": [
                           {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                           for ref in references
                       ]
                   }
               ]
               _snake_case				= {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
               return evaluate_record(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )[0]
          elif self.config_name == "multirc":
               return evaluate_multirc(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
               return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          else:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )


