




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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

   """simple docstring"""


   def remove_articles(lowercase_ ):
      return re.sub(r'''\b(a|an|the)\b'''   ,       ''' '''   ,       lowercase_ )

   def white_space_fix(lowercase_ ):
      return " ".join(text.split() )

   def remove_punc(lowercase_ ):
      __UpperCamelCase											=  set(string.punctuation )
      return "".join(ch for ch in text if ch not in exclude )

   def lower(lowercase_ ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )




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

   """simple docstring"""


   __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
   __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
   __UpperCamelCase											=  Counter(lowercase_ ) & Counter(lowercase_ )
   __UpperCamelCase											=  sum(common.values() )
   if num_same == 0:
      return 0
   __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
   __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
   __UpperCamelCase											=  (2 * precision * recall) / (precision + recall)
   return fa




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Any:

   """simple docstring"""


   return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

   """simple docstring"""


   __UpperCamelCase											=  []
   for ground_truth in ground_truths:
      __UpperCamelCase											=  metric_fn(lowercase_   ,       lowercase_ )
      scores_for_ground_truths.append(lowercase_ )
   return max(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Optional[int]:

   """simple docstring"""


   __UpperCamelCase											=  __UpperCamelCase											=  __UpperCamelCase											=  0
   __UpperCamelCase											=  []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            __UpperCamelCase											=  F"Unanswered question {qa['id']} will receive score 0."
            print(lowercase_   ,       file=sys.stderr )
            continue

         __UpperCamelCase											=  [x['''text'''] for x in qa['''answers''']]
         __UpperCamelCase											=  predictions[qa['''id''']]

         __UpperCamelCase											=  metric_max_over_ground_truths(lowercase_   ,       lowercase_   ,       lowercase_ )
         if int(_exact_match ) == 1:
            correct_ids.append(qa['''id'''] )
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(lowercase_   ,       lowercase_   ,       lowercase_ )

   __UpperCamelCase											=  exact_match / total
   __UpperCamelCase											=  fa / total

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


if __name__ == "__main__":
 a_										=       "1.0"
 a_										=       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.")
 a_										=       parser.parse_args()

 with open(args.data_file) as data_file:
  a_										=       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,
   )
  a_										=       dataset_json["data"]

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

 a_						,      a_										=       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)












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


a_										=       "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	Union[str, Any]      =   (
          "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the "
          "text where to find the answer, and `question`, which is the question, and returns the answer to the question."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

          return output_ids.reshape(snake_case	,    out_b // in_b	,    *output_ids.shape[1:]			)[0][0]







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






