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

import datasets


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

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

lowerCamelCase_    :		str							=	"""
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"precision\": Precision@10
Examples:

    >>> 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\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'precision@10': 1.0}

"""
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					return float((preds == labels).mean()						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       simple_accuracy(__lowerCamelCase				,    __lowerCamelCase						)
					__a		 =       float(fa_score(y_true=__lowerCamelCase				,    y_pred=__lowerCamelCase						)						)
					return {
					    "accuracy": acc,
					    "f1": fa,
					}
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       np.array(__lowerCamelCase						)
					__a		 =       np.array(__lowerCamelCase						)
					__a		 =       en_sentvecs.shape[0]

					# mean centering
					__a		 =       en_sentvecs - np.mean(__lowerCamelCase				,    axis=0						)
					__a		 =       in_sentvecs - np.mean(__lowerCamelCase				,    axis=0						)

					__a		 =       cdist(__lowerCamelCase				,    __lowerCamelCase				,    'cosine'						)
					__a		 =       np.array(range(__lowerCamelCase						)						)
					__a		 =       sim.argsort(axis=1						)[:, :10]
					__a		 =       np.any(preds == actual[:, None]				,    axis=1						)
					return float(matches.mean()						)



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   int:
								if self.config_name == "cvit-mkb-clsr":
													return {"precision@10": precision_at_aa(UpperCAmelCase				,						UpperCAmelCase   )}
								elif self.config_name in ["wiki-ner"]:
													return acc_and_fa(UpperCAmelCase				,						UpperCAmelCase   )
								elif self.config_name in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								]:
													return {"accuracy": simple_accuracy(UpperCAmelCase				,						UpperCAmelCase   )}
								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"]'   )





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




