






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


snake_case__   :						int													=   """\
@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}
}
"""

snake_case__   :						Dict													=   """\
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.
"""

snake_case__   :						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      _snake_case     (__lowercase     ,   __lowercase):
   return float((preds == labels).mean())






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






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  {}
   for id_pred, label in zip(__lowercase     ,   __lowercase):
      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(*__lowercase)
      UpperCamelCase_						=  fa_score(y_true=__lowercase     ,   y_pred=__lowercase     ,   average='macro')
      fas.append(__lowercase)
      UpperCamelCase_						=  int(sum(pred == label for pred, label in preds_labels) == len(__lowercase))
      ems.append(__lowercase)
   UpperCamelCase_						=  float(sum(__lowercase) / len(__lowercase))
   UpperCamelCase_						=  sum(__lowercase) / len(__lowercase)
   UpperCamelCase_						=  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     _a							(	datasets.Metric     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Optional[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  _UpperCAmelCase  (  self			)      ->			str:
     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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     if self.config_name == "axb":
        return {"matthews_correlation": matthews_corrcoef(_UpperCAmelCase		,		_UpperCAmelCase			)}
     elif self.config_name == "cb":
        return acc_and_fa(_UpperCAmelCase		,		_UpperCAmelCase		,		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(_UpperCAmelCase		,		_UpperCAmelCase			)[0]
     elif self.config_name == "multirc":
        return evaluate_multirc(_UpperCAmelCase		,		_UpperCAmelCase			)
     elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
        return {"accuracy": simple_accuracy(_UpperCAmelCase		,		_UpperCAmelCase			)}
     else:
        raise KeyError(
            'You should supply a configuration name selected in '
            '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'			)









"""simple docstring"""




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def       a_ (	lowercase__ :Optional[Any],			lowercase__ :List[str]=0.999,			lowercase__ :Optional[int]="cosine",			):
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(lowercase__ :str     ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(lowercase__ :Optional[Any]     ):
									return math.exp(t * -12.0     )

			else:
						raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'     )

			__lowerCamelCase          =      []
			for i in range(lowercase__     ):
						__lowerCamelCase          =      i / num_diffusion_timesteps
						__lowerCamelCase          =      (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(lowercase__     ) / alpha_bar_fn(lowercase__     ),			lowercase__     )     )
			return torch.tensor(lowercase__,			dtype=torch.floataa     )




class 			__snake_case   (lowerCamelCase			,		lowerCamelCase  ):
							__a					 =       [e.name for e in KarrasDiffusionSchedulers]
							__a					 =       2




							@register_to_config
							def __init__(     self:    Any     ,							A_:    int = 10_00     ,							A_:    float = 0.00_085     ,							A_:    float = 0.012     ,							A_:    str = "linear"     ,							A_:    Optional[Union[np.ndarray, List[float]]] = None     ,							A_:    str = "epsilon"     ,							A_:    Optional[bool] = False     ,							A_:    Optional[bool] = False     ,							A_:    float = 1.0     ,							A_:    str = "linspace"     ,							A_:    int = 0     ,							):
										if trained_betas is not None:
													__lowerCamelCase          =      torch.tensor(A_     ,							dtype=torch.floataa   )
										elif beta_schedule == "linear":
													__lowerCamelCase          =      torch.linspace(A_     ,							A_     ,							A_     ,							dtype=torch.floataa   )
										elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCamelCase          =      (
													    torch.linspace(beta_start**0.5     ,							beta_end**0.5     ,							A_     ,							dtype=torch.floataa   ) ** 2
													)
										elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCamelCase          =      betas_for_alpha_bar(A_     ,							alpha_transform_type="""cosine"""   )
										elif beta_schedule == "exp":
													__lowerCamelCase          =      betas_for_alpha_bar(A_     ,							alpha_transform_type="""exp"""   )
										else:
													raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

										__lowerCamelCase          =      1.0 - self.betas
										__lowerCamelCase          =      torch.cumprod(self.alphas     ,							dim=0   )

										#  set all values
										self.set_timesteps(A_     ,							A_     ,							A_   )
										__lowerCamelCase          =      use_karras_sigmas




							def   __a   (     self:    Optional[Any]     ,							A_:    List[Any]     ,							A_:    Tuple=None   ):
										if schedule_timesteps is None:
													__lowerCamelCase          =      self.timesteps

										__lowerCamelCase          =      (schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter   ) == 0:
													__lowerCamelCase          =      1 if len(A_   ) > 1 else 0
										else:
													__lowerCamelCase          =      timestep.cpu().item() if torch.is_tensor(A_   ) else timestep
													__lowerCamelCase          =      self._index_counter[timestep_int]

										return indices[pos].item()




							@property
							def   __a   (     self:    Tuple   ):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5




							def   __a   (     self:    Union[str, Any]     ,							A_:    torch.FloatTensor     ,							A_:    Union[float, torch.FloatTensor]     ,							):
										__lowerCamelCase          =      self.index_for_timestep(A_   )

										__lowerCamelCase          =      self.sigmas[step_index]
										__lowerCamelCase          =      sample / ((sigma**2 + 1) ** 0.5)
										return sample




							def   __a   (     self:    str     ,							A_:    int     ,							A_:    Union[str, torch.device] = None     ,							A_:    Optional[int] = None     ,							):
										__lowerCamelCase          =      num_inference_steps

										__lowerCamelCase          =      num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
													__lowerCamelCase          =      np.linspace(0     ,							num_train_timesteps - 1     ,							A_     ,							dtype=A_   )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
													__lowerCamelCase          =      num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__lowerCamelCase          =      (np.arange(0     ,							A_   ) * step_ratio).round()[::-1].copy().astype(A_   )
													timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
													__lowerCamelCase          =      num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__lowerCamelCase          =      (np.arange(A_     ,							0     ,							-step_ratio   )).round().copy().astype(A_   )
													timesteps -= 1
										else:
													raise ValueError(
													    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'   )

										__lowerCamelCase          =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
										__lowerCamelCase          =      np.log(A_   )
										__lowerCamelCase          =      np.interp(A_     ,							np.arange(0     ,							len(A_   )   )     ,							A_   )

										if self.config.use_karras_sigmas:
													__lowerCamelCase          =      self._convert_to_karras(in_sigmas=A_     ,							num_inference_steps=self.num_inference_steps   )
													__lowerCamelCase          =      np.array([self._sigma_to_t(A_     ,							A_   ) for sigma in sigmas]   )

										__lowerCamelCase          =      np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
										__lowerCamelCase          =      torch.from_numpy(A_   ).to(device=A_   )
										__lowerCamelCase          =      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2   ), sigmas[-1:]]   )

										__lowerCamelCase          =      torch.from_numpy(A_   )
										__lowerCamelCase          =      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2   )]   )

										if str(A_   ).startswith("""mps"""   ):
													# mps does not support float64
													__lowerCamelCase          =      timesteps.to(A_     ,							dtype=torch.floataa   )
										else:
													__lowerCamelCase          =      timesteps.to(device=A_   )

										# empty dt and derivative
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										__lowerCamelCase          =      defaultdict(A_   )




							def   __a   (     self:    Any     ,							A_:    int     ,							A_:    int   ):
										# get log sigma
										__lowerCamelCase          =      np.log(A_   )

										# get distribution
										__lowerCamelCase          =      log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										__lowerCamelCase          =      np.cumsum((dists >= 0)     ,							axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
										__lowerCamelCase          =      low_idx + 1

										__lowerCamelCase          =      log_sigmas[low_idx]
										__lowerCamelCase          =      log_sigmas[high_idx]

										# interpolate sigmas
										__lowerCamelCase          =      (low - log_sigma) / (low - high)
										__lowerCamelCase          =      np.clip(A_     ,							0     ,							1   )

										# transform interpolation to time range
										__lowerCamelCase          =      (1 - w) * low_idx + w * high_idx
										__lowerCamelCase          =      t.reshape(sigma.shape   )
										return t




							def   __a   (     self:    Dict     ,							A_:    torch.FloatTensor     ,							A_:    Tuple   ):
										__lowerCamelCase          =      in_sigmas[-1].item()
										__lowerCamelCase          =      in_sigmas[0].item()

										__lowerCamelCase          =      7.0  # 7.0 is the value used in the paper
										__lowerCamelCase          =      np.linspace(0     ,							1     ,							A_   )
										__lowerCamelCase          =      sigma_min ** (1 / rho)
										__lowerCamelCase          =      sigma_max ** (1 / rho)
										__lowerCamelCase          =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas




							@property
							def   __a   (     self:    List[Any]   ):
										return self.dt is None




							def   __a   (     self:    Union[str, Any]     ,							A_:    Union[torch.FloatTensor, np.ndarray]     ,							A_:    Union[float, torch.FloatTensor]     ,							A_:    Union[torch.FloatTensor, np.ndarray]     ,							A_:    bool = True     ,							):
										__lowerCamelCase          =      self.index_for_timestep(A_   )

										# advance index counter by 1
										__lowerCamelCase          =      timestep.cpu().item() if torch.is_tensor(A_   ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
													__lowerCamelCase          =      self.sigmas[step_index]
													__lowerCamelCase          =      self.sigmas[step_index + 1]
										else:
													# 2nd order / Heun's method
													__lowerCamelCase          =      self.sigmas[step_index - 1]
													__lowerCamelCase          =      self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										__lowerCamelCase          =      0
										__lowerCamelCase          =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
													__lowerCamelCase          =      sigma_hat if self.state_in_first_order else sigma_next
													__lowerCamelCase          =      sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
													__lowerCamelCase          =      sigma_hat if self.state_in_first_order else sigma_next
													__lowerCamelCase          =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
										elif self.config.prediction_type == "sample":
													__lowerCamelCase          =      model_output
										else:
													raise ValueError(
													    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'   )

										if self.config.clip_sample:
													__lowerCamelCase          =      pred_original_sample.clamp(
													    -self.config.clip_sample_range     ,							self.config.clip_sample_range   )

										if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													__lowerCamelCase          =      (sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													__lowerCamelCase          =      sigma_next - sigma_hat

													# store for 2nd order step
													__lowerCamelCase          =      derivative
													__lowerCamelCase          =      dt
													__lowerCamelCase          =      sample
										else:
													# 2. 2nd order / Heun's method
													__lowerCamelCase          =      (sample - pred_original_sample) / sigma_next
													__lowerCamelCase          =      (self.prev_derivative + derivative) / 2

													# 3. take prev timestep & sample
													__lowerCamelCase          =      self.dt
													__lowerCamelCase          =      self.sample

													# free dt and derivative
													# Note, this puts the scheduler in "first order mode"
													__lowerCamelCase          =      None
													__lowerCamelCase          =      None
													__lowerCamelCase          =      None

										__lowerCamelCase          =      sample + derivative * dt

										if not return_dict:
													return (prev_sample,)

										return SchedulerOutput(prev_sample=A_   )




							def   __a   (     self:    str     ,							A_:    torch.FloatTensor     ,							A_:    torch.FloatTensor     ,							A_:    torch.FloatTensor     ,							):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										__lowerCamelCase          =      self.sigmas.to(device=original_samples.device     ,							dtype=original_samples.dtype   )
										if original_samples.device.type == "mps" and torch.is_floating_point(A_   ):
													# mps does not support float64
													__lowerCamelCase          =      self.timesteps.to(original_samples.device     ,							dtype=torch.floataa   )
													__lowerCamelCase          =      timesteps.to(original_samples.device     ,							dtype=torch.floataa   )
										else:
													__lowerCamelCase          =      self.timesteps.to(original_samples.device   )
													__lowerCamelCase          =      timesteps.to(original_samples.device   )

										__lowerCamelCase          =      [self.index_for_timestep(A_     ,							A_   ) for t in timesteps]

										__lowerCamelCase          =      sigmas[step_indices].flatten()
										while len(sigma.shape   ) < len(original_samples.shape   ):
													__lowerCamelCase          =      sigma.unsqueeze(-1   )

										__lowerCamelCase          =      original_samples + noise * sigma
										return noisy_samples




							def __len__(     self:    Tuple   ):
										return self.config.num_train_timesteps




