


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class   __lowercase				(  __snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       LongformerTokenizer
      UpperCamelCase								=       True
      UpperCamelCase								=       LongformerTokenizerFast
      UpperCamelCase								=       True





      def        _lowercase				(		self						:					Optional[Any]       )    ->   str:






         """simple docstring"""







         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         UpperCAmelCase							=     [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )
         UpperCAmelCase							=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         UpperCAmelCase							=     {"""unk_token""": """<unk>"""}

         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""merges_file"""]       )
         with open(self.vocab_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(__lowerCamelCase       ) + """\n"""       )
         with open(self.merges_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write("""\n""".join(__lowerCamelCase       )       )





      def        _lowercase				(		self						:					Dict					,       **__lowerCamelCase						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         kwargs.update(self.special_tokens_map       )
         return self.tokenizer_class.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Tuple					,       **__lowerCamelCase						:					Any       )    ->   Dict:






         """simple docstring"""







         kwargs.update(self.special_tokens_map       )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Tuple					,       __lowerCamelCase						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     """lower newer"""
         UpperCAmelCase							=     """lower newer"""
         return input_text, output_text





      def        _lowercase				(		self						:					Tuple       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.tokenizer_class(self.vocab_file					,       self.merges_file					,       **self.special_tokens_map       )
         UpperCAmelCase							=     """lower newer"""
         UpperCAmelCase							=     ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         UpperCAmelCase							=     tokenizer.tokenize(__lowerCamelCase       )  # , add_prefix_space=True)
         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )

         UpperCAmelCase							=     tokens + [tokenizer.unk_token]
         UpperCAmelCase							=     [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase       )					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("""Hello world!"""					,       add_special_tokens=__lowerCamelCase       )					,       [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]       )
         self.assertListEqual(
             tokenizer.encode("""Hello world! cécé herlolip 418"""					,       add_special_tokens=__lowerCamelCase       )					,       [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]					,       )





      @slow
      def        _lowercase				(		self						:					Optional[int]       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""       )

         UpperCAmelCase							=     tokenizer.encode("""sequence builders"""					,       add_special_tokens=__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.encode("""multi-sequence build"""					,       add_special_tokens=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer.encode(
             """sequence builders"""					,       add_special_tokens=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.encode(
             """sequence builders"""					,       """multi-sequence build"""					,       add_special_tokens=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer.build_inputs_with_special_tokens(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.build_inputs_with_special_tokens(__lowerCamelCase					,       __lowerCamelCase       )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





      def        _lowercase				(		self						:					int       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     """Encode this sequence."""
         UpperCAmelCase							=     tokenizer.byte_encoder[""" """.encode("""utf-8"""       )[0]]

         # Testing encoder arguments
         UpperCAmelCase							=     tokenizer.encode(__lowerCamelCase					,       add_special_tokens=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
         self.assertNotEqual(__lowerCamelCase					,       __lowerCamelCase       )

         UpperCAmelCase							=     tokenizer.encode(__lowerCamelCase					,       add_special_tokens=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
         self.assertEqual(__lowerCamelCase					,       __lowerCamelCase       )

         tokenizer.add_special_tokens({"""bos_token""": """<s>"""}       )
         UpperCAmelCase							=     tokenizer.encode(__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
         self.assertNotEqual(__lowerCamelCase					,       __lowerCamelCase       )

         # Testing spaces after special tokens
         UpperCAmelCase							=     """<mask>"""
         tokenizer.add_special_tokens(
             {"""mask_token""": AddedToken(__lowerCamelCase					,       lstrip=__lowerCamelCase					,       rstrip=__lowerCamelCase       )}       )  # mask token has a left space
         UpperCAmelCase							=     tokenizer.convert_tokens_to_ids(__lowerCamelCase       )

         UpperCAmelCase							=     """Encode <mask> sequence"""
         UpperCAmelCase							=     """Encode <mask>sequence"""

         UpperCAmelCase							=     tokenizer.encode(__lowerCamelCase       )
         UpperCAmelCase							=     encoded.index(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
         self.assertEqual(__lowerCamelCase					,       __lowerCamelCase       )

         UpperCAmelCase							=     tokenizer.encode(__lowerCamelCase       )
         UpperCAmelCase							=     encoded.index(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
         self.assertNotEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         pass





      def        _lowercase				(		self						:					str       )    ->   Union[str, Any]:






         """simple docstring"""







         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,       **__lowerCamelCase       )
               UpperCAmelCase							=     self.tokenizer_class.from_pretrained(__lowerCamelCase					,       **__lowerCamelCase       )
               UpperCAmelCase							=     """A, <mask> AllenNLP sentence."""
               UpperCAmelCase							=     tokenizer_r.encode_plus(__lowerCamelCase					,       add_special_tokens=__lowerCamelCase					,       return_token_type_ids=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_p.encode_plus(__lowerCamelCase					,       add_special_tokens=__lowerCamelCase					,       return_token_type_ids=__lowerCamelCase       )

               # token_type_ids should put 0 everywhere
               self.assertEqual(sum(tokens_r["""token_type_ids"""]       )					,       sum(tokens_p["""token_type_ids"""]       )       )

               # attention_mask should put 1 everywhere, so sum over length should be 1
               self.assertEqual(
                   sum(tokens_r["""attention_mask"""]       ) / len(tokens_r["""attention_mask"""]       )					,       sum(tokens_p["""attention_mask"""]       ) / len(tokens_p["""attention_mask"""]       )					,       )

               UpperCAmelCase							=     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]       )
               UpperCAmelCase							=     tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]       )

               # Rust correctly handles the space before the mask while python doesnt
               self.assertSequenceEqual(tokens_p["""input_ids"""]					,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
               self.assertSequenceEqual(tokens_r["""input_ids"""]					,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )

               self.assertSequenceEqual(
                   __lowerCamelCase					,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )
               self.assertSequenceEqual(
                   __lowerCamelCase					,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )





      def        _lowercase				(		self						:					Tuple       )    ->   List[Any]:






         """simple docstring"""







         for trim_offsets, add_prefix_space in itertools.product([True, False]					,       repeat=2       ):
            UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                self.tmpdirname					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )

            UpperCAmelCase							=     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
            UpperCAmelCase							=     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

            self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]					,       __lowerCamelCase       )

            self.assertEqual(post_processor_state["""add_prefix_space"""]					,       __lowerCamelCase       )
            self.assertEqual(post_processor_state["""trim_offsets"""]					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Any       )    ->   Tuple:






         """simple docstring"""







         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
               UpperCAmelCase							=     """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
               UpperCAmelCase							=     F"""{text_of_1_token} {text_of_1_token}"""

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (0, len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (len(__lowerCamelCase       ) + 1, len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (0, len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (len(__lowerCamelCase       ) + 1, len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (0, len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (len(__lowerCamelCase       ), len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (0, len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (len(__lowerCamelCase       ), len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )

               UpperCAmelCase							=     F""" {text}"""

               # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
               #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
               # )
               # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
               # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
               # self.assertEqual(
               #     encoding.offset_mapping[1],
               #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
               # )

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (1, 1 + len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (1 + len(__lowerCamelCase       ) + 1, 1 + len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (0, 1 + len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (1 + len(__lowerCamelCase       ), 1 + len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )

               UpperCAmelCase							=     self.rust_tokenizer_class.from_pretrained(
                   __lowerCamelCase					,       use_fast=__lowerCamelCase					,       add_prefix_space=__lowerCamelCase					,       trim_offsets=__lowerCamelCase       )
               UpperCAmelCase							=     tokenizer_r(__lowerCamelCase					,       return_offsets_mapping=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase       )
               self.assertEqual(encoding.offset_mapping[0]					,       (0, 1 + len(__lowerCamelCase       ))       )
               self.assertEqual(
                   encoding.offset_mapping[1]					,       (1 + len(__lowerCamelCase       ), 1 + len(__lowerCamelCase       ) + 1 + len(__lowerCamelCase       ))					,       )







from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__a     =			"""\
@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}
}
"""

__a     =			"""\
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.
"""

__a     =			"""
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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   return float((preds == labels).mean()   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_="binary"   )						->Union[str, Any]:
   UpperCAmelCase							=     simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
   UpperCAmelCase							=     float(fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_  ,						average=lowerCAmelCase_   )   )
   return {
       "accuracy": acc,
       "f1": fa,
   }
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[Any]:
   UpperCAmelCase							=     {}
   for id_pred, label in zip(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      UpperCAmelCase							=     F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
      UpperCAmelCase							=     id_pred["""prediction"""]
      if question_id in question_map:
         question_map[question_id].append((pred, label)   )
      else:
         UpperCAmelCase							=     [(pred, label)]
   UpperCAmelCase				,  UpperCAmelCase							=     [], []
   for question, preds_labels in question_map.items():
      UpperCAmelCase				,  UpperCAmelCase							=     zip(*lowerCAmelCase_   )
      UpperCAmelCase							=     fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_  ,						average="""macro"""   )
      fas.append(lowerCAmelCase_   )
      UpperCAmelCase							=     int(sum(pred == label for pred, label in preds_labels   ) == len(lowerCAmelCase_   )   )
      ems.append(lowerCAmelCase_   )
   UpperCAmelCase							=     float(sum(lowerCAmelCase_   ) / len(lowerCAmelCase_   )   )
   UpperCAmelCase							=     sum(lowerCAmelCase_   ) / len(lowerCAmelCase_   )
   UpperCAmelCase							=     float(fa_score(y_true=lowerCAmelCase_  ,						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   __lowercase				(  datasets.Metric  ):





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         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						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         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						:					Dict					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Optional[int]       )    ->   List[Any]:






         """simple docstring"""







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



