



"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available






@dataclass
class 			UpperCAmelCase_						(       lowercase_		):
      __SCREAMING_SNAKE_CASE		:     Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
 from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
 from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





"""simple docstring"""




import argparse
import json
import re
import string
import sys
from collections import Counter

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:





     '''simple docstring'''

     def remove_articles(SCREAMING_SNAKE_CASE__			:		Tuple       ):
          return re.sub(R"\b(a|an|the)\b"		,						" "		,						SCREAMING_SNAKE_CASE__       )

     def white_space_fix(SCREAMING_SNAKE_CASE__			:		Any       ):
          return " ".join(text.split()       )

     def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
          _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
          return "".join(ch for ch in text if ch not in exclude       )

     def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			List[Any]              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
     _UpperCAmelCase    :			Tuple              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
     _UpperCAmelCase    :			List[Any]              =					Counter(SCREAMING_SNAKE_CASE__       ) & Counter(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			List[str]              =					sum(common.values()       )
     if num_same == 0:
          return 0
     _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Union[str, Any]              =					(2 * precision * recall) / (precision + recall)
     return fa

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:





     '''simple docstring'''

     return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:





     '''simple docstring'''

     _UpperCAmelCase    :			int              =					[]
     for ground_truth in ground_truths:
          _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
     return max(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:





     '''simple docstring'''

     _UpperCAmelCase    :			str              =					0
     _UpperCAmelCase    :			int              =					[]
     for passage in dataset:
          for qa in passage["qas"]:
               total += 1
               if qa["id"] not in predictions:
                    _UpperCAmelCase    :			List[Any]              =					f'Unanswered question {qa["id"]} will receive score 0.'
                    print(SCREAMING_SNAKE_CASE__		,						file=sys.stderr       )
                    continue

               _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
               _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]

               _UpperCAmelCase    :			int              =					metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
               if int(_exact_match       ) == 1:
                    correct_ids.append(qa["id"]       )
               exact_match += _exact_match

               fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			List[str]              =					exact_match / total
     _UpperCAmelCase    :			str              =					fa / total

     return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
 _lowerCAmelCase       :      Any							=							"1.0"
 _lowerCAmelCase       :      Any							=							argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
 parser.add_argument("data_file", help="The dataset file in JSON format.")
 parser.add_argument("pred_file", help="The model prediction file in JSON format.")
 parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
 _lowerCAmelCase       :      Tuple							=							parser.parse_args()

 with open(args.data_file) as data_file:
  _lowerCAmelCase       :      int							=							json.load(data_file)
  if dataset_json["version"] != expected_version:
   print(
       F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
       file=sys.stderr,
   )
  _lowerCAmelCase       :      List[str]							=							dataset_json["data"]

 with open(args.pred_file) as pred_file:
  _lowerCAmelCase       :      str							=							json.load(pred_file)

 _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							evaluate(dataset, predictions)

 if args.output_correct_ids:
  print(F"Output {len(correct_ids)} correctly answered question IDs.")
  with open("correct_ids.json", "w") as f:
   json.dump(correct_ids, f)
