




'''simple docstring'''






import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


A_			      =		"\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n"

A_			      =		"\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"

A_			      =		"\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"precision\": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'precision@10': 1.0}\n\n"






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      return float((preds == labels).mean()		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   List[str]	      =    simple_accuracy(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      snake_case__       :   Dict	      =    float(fa_score(y_true=__SCREAMING_SNAKE_CASE				,      y_pred=__SCREAMING_SNAKE_CASE		)		)
      return {
          "accuracy": acc,
          "f1": fa,
      }






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   Any	      =    np.array(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Dict	      =    np.array(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Tuple	      =    en_sentvecs.shape[0]

      # mean centering
      snake_case__       :   Tuple	      =    en_sentvecs - np.mean(__SCREAMING_SNAKE_CASE				,      axis=0		)
      snake_case__       :   Optional[int]	      =    in_sentvecs - np.mean(__SCREAMING_SNAKE_CASE				,      axis=0		)

      snake_case__       :   Optional[int]	      =    cdist(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      'cosine'		)
      snake_case__       :   int	      =    np.array(range(__SCREAMING_SNAKE_CASE		)		)
      snake_case__       :   Union[str, Any]	      =    sim.argsort(axis=1		)[:, :10]
      snake_case__       :   Optional[int]	      =    np.any(preds == actual[:, None]				,      axis=1		)
      return float(matches.mean()		)






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION							)
class        lowercase_      (						datasets.Metric							):





     def 			_lowerCAmelCase      (     self     :					int			):
           if self.config_name not in [
               "wnli",
               "copa",
               "sna",
               "csqa",
               "wstp",
               "inltkh",
               "bbca",
               "cvit-mkb-clsr",
               "iitp-mr",
               "iitp-pr",
               "actsa-sc",
               "md",
               "wiki-ner",
           ]:
                 raise KeyError(
                     'You should supply a configuration name selected in '
                     '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
                     '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
                     '"wiki-ner"]'			)
           return datasets.MetricInfo(
               description=_DESCRIPTION     ,							citation=_CITATION     ,							inputs_description=_KWARGS_DESCRIPTION     ,							features=datasets.Features(
                   {
                       'predictions': datasets.Value('int64'			)
                       if self.config_name != 'cvit-mkb-clsr'
                       else datasets.Sequence(datasets.Value('float32'			)			),
                       'references': datasets.Value('int64'			)
                       if self.config_name != 'cvit-mkb-clsr'
                       else datasets.Sequence(datasets.Value('float32'			)			),
                   }			)     ,							codebase_urls=[]     ,							reference_urls=[]     ,							format='numpy' if self.config_name != 'cvit-mkb-clsr' else None     ,							)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Union[str, Any]			):
           if self.config_name == "cvit-mkb-clsr":
                 return {"precision@10": precision_at_aa(__lowerCamelCase     ,							__lowerCamelCase			)}
           elif self.config_name in ["wiki-ner"]:
                 return acc_and_fa(__lowerCamelCase     ,							__lowerCamelCase			)
           elif self.config_name in [
               "wnli",
               "copa",
               "sna",
               "csqa",
               "wstp",
               "inltkh",
               "bbca",
               "iitp-mr",
               "iitp-pr",
               "actsa-sc",
               "md",
           ]:
                 return {"accuracy": simple_accuracy(__lowerCamelCase     ,							__lowerCamelCase			)}
           else:
                 raise KeyError(
                     'You should supply a configuration name selected in '
                     '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
                     '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
                     '"wiki-ner"]'			)






'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A_			      =		logging.get_logger(__name__)






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					float     ,							**__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   int	      =    feature_size
           snake_case__       :   List[str]	      =    sampling_rate
           snake_case__       :   Any	      =    padding_value

           snake_case__       :   Union[str, Any]	      =    kwargs.pop('padding_side'     ,							'right'			)
           snake_case__       :   List[Any]	      =    kwargs.pop('return_attention_mask'     ,							__lowerCamelCase			)

           super().__init__(**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ]     ,							__lowerCamelCase     :					Union[bool, str, PaddingStrategy] = True     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					bool = False     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[bool] = None     ,							__lowerCamelCase     :					Optional[Union[str, TensorType]] = None     ,							):
           # If we have a list of dicts, let's convert it in a dict of lists
           # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
           if isinstance(__lowerCamelCase     ,							(list, tuple)			) and isinstance(processed_features[0]     ,							(dict, BatchFeature)			):
                 snake_case__       :   int	      =    {
                     key: [example[key] for example in processed_features] for key in processed_features[0].keys()
                 }

           # The model's main input name, usually `input_values`, has be passed for padding
           if self.model_input_names[0] not in processed_features:
                 raise ValueError(
                     'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                     F" to this method that includes {self.model_input_names[0]}, but you provided"
                     F" {list(processed_features.keys()			)}"			)

           snake_case__       :   Tuple	      =    processed_features[self.model_input_names[0]]
           snake_case__       :   Union[str, Any]	      =    (
               return_attention_mask if return_attention_mask is not None else self.return_attention_mask
           )

           if len(__lowerCamelCase			) == 0:
                 if return_attention_mask:
                       snake_case__       :   str	      =    []
                 return processed_features

           # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
           # and rebuild them afterwards if no return_tensors is specified
           # Note that we lose the specific device the tensor may be on for PyTorch

           snake_case__       :   Dict	      =    required_input[0]
           if isinstance(__lowerCamelCase     ,							(list, tuple)			):
                 # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
                 snake_case__       :   List[Any]	      =    0
                 while len(required_input[index]			) == 0:
                       index += 1
                 if index < len(__lowerCamelCase			):
                       snake_case__       :   List[Any]	      =    required_input[index][0]

           if return_tensors is None:
                 if is_tf_tensor(__lowerCamelCase			):
                       snake_case__       :   int	      =    'tf'
                 elif is_torch_tensor(__lowerCamelCase			):
                       snake_case__       :   str	      =    'pt'
                 elif isinstance(__lowerCamelCase     ,							(int, float, list, tuple, np.ndarray)			):
                       snake_case__       :   Any	      =    'np'
                 else:
                       raise ValueError(
                           F"type of {first_element} unknown: {type(__lowerCamelCase			)}. "
                           'Should be one of a python, numpy, pytorch or tensorflow object.'			)

           for key, value in processed_features.items():
                 if isinstance(value[0]     ,							(int, float)			):
                       snake_case__       :   List[Any]	      =    to_numpy(__lowerCamelCase			)
                 else:
                       snake_case__       :   List[str]	      =    [to_numpy(__lowerCamelCase			) for v in value]

        # Convert padding_strategy in PaddingStrategy
           snake_case__       :   str	      =    self._get_padding_strategies(padding=__lowerCamelCase     ,							max_length=__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    processed_features[self.model_input_names[0]]

           snake_case__       :   Tuple	      =    len(__lowerCamelCase			)
           if not all(len(__lowerCamelCase			) == batch_size for v in processed_features.values()			):
                 raise ValueError('Some items in the output dictionary have a different batch size than others.'			)

           snake_case__       :   str	      =    []
           for i in range(__lowerCamelCase			):
                 snake_case__       :   str	      =    {k: v[i] for k, v in processed_features.items()}
                 # truncation
                 snake_case__       :   Optional[Any]	      =    self._truncate(
                     __lowerCamelCase     ,							max_length=__lowerCamelCase     ,							pad_to_multiple_of=__lowerCamelCase     ,							truncation=__lowerCamelCase     ,							)
                 truncated_inputs.append(__lowerCamelCase			)

           if padding_strategy == PaddingStrategy.LONGEST:
                 # make sure that `max_length` cannot be longer than the longest truncated length
                 snake_case__       :   Any	      =    max(len(input_slice[self.model_input_names[0]]			) for input_slice in truncated_inputs			)
                 snake_case__       :   Union[str, Any]	      =    PaddingStrategy.MAX_LENGTH

           snake_case__       :   List[Any]	      =    {}
           for i in range(__lowerCamelCase			):
                 # padding
                 snake_case__       :   Tuple	      =    self._pad(
                     truncated_inputs[i]     ,							max_length=__lowerCamelCase     ,							padding_strategy=__lowerCamelCase     ,							pad_to_multiple_of=__lowerCamelCase     ,							return_attention_mask=__lowerCamelCase     ,							)

                 for key, value in outputs.items():
                       if key not in batch_outputs:
                             snake_case__       :   Union[str, Any]	      =    []
                       if value.dtype is np.dtype(np.floataa			):
                             snake_case__       :   int	      =    value.astype(np.floataa			)
                       batch_outputs[key].append(__lowerCamelCase			)

           return BatchFeature(__lowerCamelCase     ,							tensor_type=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]     ,							__lowerCamelCase     :					Union[Dict[str, np.ndarray], BatchFeature]     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[bool] = None     ,							):
           snake_case__       :   Optional[int]	      =    processed_features[self.model_input_names[0]]

           if padding_strategy == PaddingStrategy.LONGEST:
                 snake_case__       :   Tuple	      =    len(__lowerCamelCase			)

           if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                 snake_case__       :   Dict	      =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           snake_case__       :   List[str]	      =    padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowerCamelCase			) < max_length

           if return_attention_mask and "attention_mask" not in processed_features:
                 snake_case__       :   Optional[int]	      =    np.ones(len(__lowerCamelCase			)     ,							dtype=np.intaa			)

           if needs_to_be_padded:
                 snake_case__       :   List[Any]	      =    max_length - len(__lowerCamelCase			)
                 if self.padding_side == "right":
                       if return_attention_mask:
                             snake_case__       :   List[str]	      =    np.pad(
                                 processed_features['attention_mask']     ,							(0, difference)			)
                       snake_case__       :   List[Any]	      =    ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                       snake_case__       :   int	      =    np.pad(
                           __lowerCamelCase     ,							__lowerCamelCase     ,							'constant'     ,							constant_values=self.padding_value			)
                 elif self.padding_side == "left":
                       if return_attention_mask:
                             snake_case__       :   str	      =    np.pad(
                                 processed_features['attention_mask']     ,							(difference, 0)			)
                       snake_case__       :   int	      =    ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                       snake_case__       :   int	      =    np.pad(
                           __lowerCamelCase     ,							__lowerCamelCase     ,							'constant'     ,							constant_values=self.padding_value			)
                 else:
                       raise ValueError('Invalid padding strategy:' + str(self.padding_side			)			)

           return processed_features





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Union[Dict[str, np.ndarray], BatchFeature]     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[bool] = None     ,							):
           if not truncation:
                 return processed_features
           elif truncation and max_length is None:
                 raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'			)

           snake_case__       :   Optional[int]	      =    processed_features[self.model_input_names[0]]

           # find `max_length` that fits `pad_to_multiple_of`
           if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                 snake_case__       :   List[str]	      =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           snake_case__       :   str	      =    len(__lowerCamelCase			) > max_length

           if needs_to_be_truncated:
                 snake_case__       :   Optional[int]	      =    processed_features[self.model_input_names[0]][:max_length]
                 if "attention_mask" in processed_features:
                       snake_case__       :   List[Any]	      =    processed_features['attention_mask'][:max_length]

           return processed_features





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Optional[int]=False     ,							__lowerCamelCase     :					Optional[Any]=None			):

           # Get padding strategy
           if padding is not False:
                 if padding is True:
                       snake_case__       :   int	      =    PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                 elif not isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                       snake_case__       :   str	      =    PaddingStrategy(__lowerCamelCase			)
                 elif isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                       snake_case__       :   str	      =    padding
           else:
                 snake_case__       :   Union[str, Any]	      =    PaddingStrategy.DO_NOT_PAD

           # Set max length if needed
           if max_length is None:
                 if padding_strategy == PaddingStrategy.MAX_LENGTH:
                       raise ValueError(
                           F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"			)

        # Test if we have a padding value
           if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
                 raise ValueError(
                     'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                     ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'			)

           return padding_strategy
