
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class       _snake_case   :


	def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=99					,  SCREAMING_SNAKE_CASE_=13					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=9					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=5					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_=8					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.0_0_2					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  ):


			'''simple docstring'''




			lowercase__  :   List[str]    		=      parent
			lowercase__  :   Optional[Any]    		=      batch_size
			lowercase__  :   int    		=      encoder_seq_length
			lowercase__  :   List[Any]    		=      decoder_seq_length
			# For common tests
			lowercase__  :   Tuple    		=      self.decoder_seq_length
			lowercase__  :   Optional[int]    		=      is_training
			lowercase__  :   Dict    		=      use_attention_mask
			lowercase__  :   Any    		=      use_labels
			lowercase__  :   Optional[Any]    		=      vocab_size
			lowercase__  :   Dict    		=      hidden_size
			lowercase__  :   List[str]    		=      num_hidden_layers
			lowercase__  :   List[Any]    		=      num_attention_heads
			lowercase__  :   str    		=      d_ff
			lowercase__  :   List[str]    		=      relative_attention_num_buckets
			lowercase__  :   Dict    		=      dropout_rate
			lowercase__  :   Optional[int]    		=      initializer_factor
			lowercase__  :   Optional[Any]    		=      eos_token_id
			lowercase__  :   int    		=      pad_token_id
			lowercase__  :   Union[str, Any]    		=      decoder_start_token_id
			lowercase__  :   List[Any]    		=      None
			lowercase__  :   Any    		=      decoder_layers


	def      lowercase__     (					self):


			'''simple docstring'''




			return TaConfig.from_pretrained("""google/umt5-base""")


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  ):


			'''simple docstring'''




			if attention_mask is None:
					lowercase__  :   Any    		=      input_ids.ne(config.pad_token_id)
			if decoder_attention_mask is None:
					lowercase__  :   List[str]    		=      decoder_input_ids.ne(config.pad_token_id)
			if head_mask is None:
					lowercase__  :   Union[str, Any]    		=      torch.ones(config.num_hidden_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_)
			if decoder_head_mask is None:
					lowercase__  :   Union[str, Any]    		=      torch.ones(config.num_decoder_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_)
			if cross_attn_head_mask is None:
					lowercase__  :   Any    		=      torch.ones(
					    config.num_decoder_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   List[Any]    		=      ids_tensor([self.batch_size, self.encoder_seq_length]					,  self.vocab_size)
			lowercase__  :   List[str]    		=      ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size)

			# we need to clamp the input ids here to avoid having pad token in between
			# this is because for NllbMoe the position_ids are prepared such that
			# all pad tokens have pos id = 2 and rest are between 2..seq_length
			# and the seq_length here is seq_length - num_pad_tokens
			# but when using past, there is no way of knowing if the past input ids had
			# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
			# position_ids being off by num_pad_tokens in past input
			lowercase__  :   List[Any]    		=      input_ids.clamp(self.pad_token_id + 1)
			lowercase__  :   str    		=      decoder_input_ids.clamp(self.pad_token_id + 1)

			lowercase__  :   Optional[Any]    		=      self.get_config()
			lowercase__  :   Optional[int]    		=      config.num_attention_heads
			lowercase__  :   str    		=      self.prepare_inputs_dict(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
			return config, input_dict


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Tuple    		=      self.prepare_config_and_inputs()
			return config, inputs_dict


	def      lowercase__     (					self):


			'''simple docstring'''




			return TaConfig(
			    vocab_size=1_66					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )


	def      lowercase__     (					self):


			'''simple docstring'''




			return TaConfig(
			    vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  ):


			'''simple docstring'''




			lowercase__  :   Optional[Any]    		=      UMTaModel(config=SCREAMING_SNAKE_CASE_)
			model.to(SCREAMING_SNAKE_CASE_)
			model.eval()
			lowercase__  :   List[Any]    		=      model(
			    input_ids=SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  )
			lowercase__  :   Optional[Any]    		=      model(input_ids=SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_)
			lowercase__  :   Dict    		=      result.last_hidden_state
			lowercase__  :   List[str]    		=      result.past_key_values
			lowercase__  :   Optional[Any]    		=      result.encoder_last_hidden_state

			self.parent.assertEqual(encoder_output.size()					,  (self.batch_size, self.encoder_seq_length, self.hidden_size))
			self.parent.assertEqual(decoder_output.size()					,  (self.batch_size, self.decoder_seq_length, self.hidden_size))
			# There should be `num_layers` key value embeddings stored in decoder_past
			self.parent.assertEqual(len(SCREAMING_SNAKE_CASE_)					,  config.num_layers)
			# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
			self.parent.assertEqual(len(decoder_past[0])					,  4)


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  ):


			'''simple docstring'''




			lowercase__  :   str    		=      UMTaModel(config=SCREAMING_SNAKE_CASE_).get_decoder().to(SCREAMING_SNAKE_CASE_).eval()
			# first forward pass
			lowercase__  :   Any    		=      model(SCREAMING_SNAKE_CASE_					,  use_cache=SCREAMING_SNAKE_CASE_)
			lowercase__  :   str    		=      model(SCREAMING_SNAKE_CASE_)
			lowercase__  :   int    		=      model(SCREAMING_SNAKE_CASE_					,  use_cache=SCREAMING_SNAKE_CASE_)

			self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_))
			self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_) + 1)

			lowercase__  :   Optional[int]    		=      outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			lowercase__  :   Any    		=      ids_tensor((self.batch_size, 1)					,  config.vocab_size)

			# append to next input_ids and
			lowercase__  :   str    		=      torch.cat([input_ids, next_tokens]					,  dim=-1)

			lowercase__  :   int    		=      model(SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
			lowercase__  :   Tuple    		=      model(SCREAMING_SNAKE_CASE_					,  past_key_values=SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]

			# select random slice
			lowercase__  :   Optional[Any]    		=      ids_tensor((1,)					,  output_from_past.shape[-1]).item()
			lowercase__  :   Union[str, Any]    		=      output_from_no_past[:, -1, random_slice_idx].detach()
			lowercase__  :   Union[str, Any]    		=      output_from_past[:, 0, random_slice_idx].detach()

			# test that outputs are equal for slice
			self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  atol=1E-3))



	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  ):


			'''simple docstring'''




			lowercase__  :   List[Any]    		=      UMTaModel(config=SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_).half().eval()
			lowercase__  :   Optional[Any]    		=      model(**SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
			self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE_).any().item())



@require_torch
class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_  ,						UpperCAmelCase_  ,						unittest.TestCase							):
	__lowerCAmelCase  :     int        =			(
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	__lowerCAmelCase  :     Optional[Any]        =			(UMTaForConditionalGeneration,) if is_torch_available() else ()
	__lowerCAmelCase  :     Optional[Any]        =			(
	    {
	        'conversational': UMTaForConditionalGeneration,
	        'feature-extraction': UMTaModel,
	        'summarization': UMTaForConditionalGeneration,
	        'text2text-generation': UMTaForConditionalGeneration,
	        'translation': UMTaForConditionalGeneration,
	        'question-answering': UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	__lowerCAmelCase  :     Union[str, Any]        =			True
	__lowerCAmelCase  :     List[str]        =			False
	__lowerCAmelCase  :     Dict        =			False
	__lowerCAmelCase  :     Tuple        =			True
	__lowerCAmelCase  :     Dict        =			True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	__lowerCAmelCase  :     Dict        =			[0.8, 0.9]


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Optional[int]    		=      UMTaModelTester(self)


	@unittest.skip("""Test has a segmentation fault on torch 1.8.0""")
	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Optional[int]    		=      self.model_tester.prepare_config_and_inputs()
			lowercase__  :   Union[str, Any]    		=      UMTaModel(config_and_inputs[0]).to(SCREAMING_SNAKE_CASE_)
			with tempfile.TemporaryDirectory() as tmpdirname:
					torch.onnx.export(
					    SCREAMING_SNAKE_CASE_					,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,  f'{tmpdirname}/t5_test.onnx'					,  export_params=SCREAMING_SNAKE_CASE_					,  opset_version=9					,  input_names=["""input_ids""", """decoder_input_ids"""]					,  )


	@unittest.skipIf(torch_device == """cpu"""					,  """Cant do half precision""")
	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Optional[int]    		=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE_)


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   List[str]    		=      ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
			lowercase__  :   Union[str, Any]    		=      self.model_tester.prepare_config_and_inputs()
			lowercase__  :   Any    		=      config_and_inputs[0]
			lowercase__  :   List[str]    		=      UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_).eval()
			model.to(SCREAMING_SNAKE_CASE_)

			lowercase__  :   Union[str, Any]    		=      {
			    """head_mask""": torch.zeros(config.num_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_),
			    """decoder_head_mask""": torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_),
			    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_),
			}

			for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE_					,  head_masking.items()):
					lowercase__  :   Optional[Any]    		=      {name: mask}
					# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
					if name == "head_mask":
							lowercase__  :   str    		=      torch.ones(
							    config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_)

					lowercase__  :   Optional[int]    		=      model.generate(
					    config_and_inputs[1]["""input_ids"""]					,  num_beams=1					,  max_length=3					,  output_attentions=SCREAMING_SNAKE_CASE_					,  return_dict_in_generate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_					,  )
					# We check the state of decoder_attentions and cross_attentions just from the last step
					lowercase__  :   Union[str, Any]    		=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
					self.assertEqual(sum([w.sum().item() for w in attn_weights])					,  0.0)



	@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""")
	def      lowercase__     (					self):


			'''simple docstring'''




			pass



@require_torch
@require_sentencepiece
@require_tokenizers
class       _snake_case   (	unittest.TestCase							):


	@slow
	@unittest.skip(
	    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""")
	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   List[str]    		=      UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""					,  return_dict=SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
			lowercase__  :   Dict    		=      AutoTokenizer.from_pretrained("""google/umt5-small"""					,  use_fast=SCREAMING_SNAKE_CASE_					,  legacy=SCREAMING_SNAKE_CASE_)
			lowercase__  :   Optional[int]    		=      [
			    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
			    """No se como puedo <extra_id_0>.""",
			    """This is the reason why we <extra_id_0> them.""",
			    """The <extra_id_0> walks in <extra_id_1>, seats""",
			    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
			]
			lowercase__  :   List[str]    		=      tokenizer(SCREAMING_SNAKE_CASE_					,  return_tensors="""pt"""					,  padding=SCREAMING_SNAKE_CASE_).input_ids
			# fmt: off
			lowercase__  :   str    		=      torch.tensor(
			    [
			        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
			        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
			    ])
			# fmt: on
			torch.testing.assert_allclose(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

			lowercase__  :   Tuple    		=      model.generate(input_ids.to(SCREAMING_SNAKE_CASE_))
			lowercase__  :   str    		=      [
			    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
			    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
			    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
			    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
			    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
			]
			lowercase__  :   List[str]    		=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_)
			self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)








from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCamelCase__    :		Dict          =   """\
@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}
}
"""

lowerCamelCase__    :		Tuple          =   """\
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.
"""

lowerCamelCase__    :		Any          =   """
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	(      lowercase_ , lowercase_   )		->	Optional[Any]:


  '''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_="binary"   )		->	int:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      simple_accuracy(lowercase_ , lowercase_   )
  lowercase__  :   Optional[Any]    		=      float(fa_score(y_true=lowercase_ , y_pred=lowercase_ , average=lowercase_   )   )
  return {
      "accuracy": acc,
      "f1": fa,
  }

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   List[Any]    		=      {}
  for id_pred, label in zip(lowercase_ , lowercase_   ):
    lowercase__  :   Optional[Any]    		=      F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
    lowercase__  :   Optional[Any]    		=      id_pred["""prediction"""]
    if question_id in question_map:
      question_map[question_id].append((pred, label)   )
    else:
      lowercase__  :   Optional[int]    		=      [(pred, label)]
  lowercase__		,    lowercase__  :   List[str]    		=      [], []
  for question, preds_labels in question_map.items():
    lowercase__		,    lowercase__  :   List[str]    		=      zip(*lowercase_   )
    lowercase__  :   Optional[Any]    		=      fa_score(y_true=lowercase_ , y_pred=lowercase_ , average="""macro"""   )
    fas.append(lowercase_   )
    lowercase__  :   List[Any]    		=      int(sum(pred == label for pred, label in preds_labels   ) == len(lowercase_   )   )
    ems.append(lowercase_   )
  lowercase__  :   str    		=      float(sum(lowercase_   ) / len(lowercase_   )   )
  lowercase__  :   str    		=      sum(lowercase_   ) / len(lowercase_   )
  lowercase__  :   List[str]    		=      float(fa_score(y_true=lowercase_ , 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       _snake_case   (	datasets.Metric							):


 def      lowercase__     (					self):


   '''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):


   '''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					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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":
     lowercase__  :   Optional[Any]    		=      [
         {
             """qas""": [
                 {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                 for ref in references
             ]
         }
     ]
     lowercase__  :   str    		=      {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\",]""")






