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

import datasets


_SCREAMING_SNAKE_CASE      						=	"\\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"

_SCREAMING_SNAKE_CASE      						=	"\\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"

_SCREAMING_SNAKE_CASE      						=	"\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        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     Optional[Any]:







 '''simple docstring'''
 return float((preds == labels).mean()    )





def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase          =	simple_accuracy(_lowerCAmelCase				, _lowerCAmelCase    )
 UpperCamelCase          =	float(fa_score(y_true=_lowerCAmelCase				, y_pred=_lowerCAmelCase    )    )
 return {
     "accuracy": acc,
     "f1": fa,
 }





def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     Dict:







 '''simple docstring'''
 UpperCamelCase          =	np.array(_lowerCAmelCase    )
 UpperCamelCase          =	np.array(_lowerCAmelCase    )
 UpperCamelCase          =	en_sentvecs.shape[0]

 # mean centering
 UpperCamelCase          =	en_sentvecs - np.mean(_lowerCAmelCase				, axis=0    )
 UpperCamelCase          =	in_sentvecs - np.mean(_lowerCAmelCase				, axis=0    )

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


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







   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


    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		:		Optional[int]					,					lowerCamelCase_		:		int					,					lowerCamelCase_		:		Any							):





    """simple docstring"""


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






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCamelCase__:   Optional[int]								=			"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
						import torch

						print("Torch version:", torch.__version__)
						print("Cuda available:", torch.cuda.is_available())
						print("Cuda version:", torch.version.cuda)
						print("CuDNN version:", torch.backends.cudnn.version())
						print("Number of GPUs available:", torch.cuda.device_count())
						print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
						print("Torch version:", None)

try:
						import deepspeed

						print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
						print("DeepSpeed version:", None)

try:
						import tensorflow as tf

						print("TensorFlow version:", tf.__version__)
						print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
						print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
						print("TensorFlow version:", None)





