






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
a_		:						List[Any]             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__							,       config=UpperCamelCase__)
			SCREAMING_SNAKE_CASE						= downstream_dict["""projector.weight"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""projector.bias"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.post_net.linear.weight"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.post_net.linear.bias"""]
			return model






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__							,       config=UpperCamelCase__)
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.linear.weight"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.linear.bias"""]
			return model






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= WavaVecaForXVector.from_pretrained(UpperCamelCase__							,       config=UpperCamelCase__)
			SCREAMING_SNAKE_CASE						= downstream_dict["""connector.weight"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""connector.bias"""]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel):
						SCREAMING_SNAKE_CASE						= downstream_dict[
						    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
						]
						SCREAMING_SNAKE_CASE						= downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

			SCREAMING_SNAKE_CASE						= downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
			SCREAMING_SNAKE_CASE						= downstream_dict["""objective.W"""]
			return model






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= torch.load(UpperCamelCase__							,       map_location='cpu')

			SCREAMING_SNAKE_CASE						= checkpoint["""Downstream"""]

			SCREAMING_SNAKE_CASE						= WavaVecaConfig.from_pretrained(UpperCamelCase__)
			SCREAMING_SNAKE_CASE						= WavaVecaFeatureExtractor.from_pretrained(
			    UpperCamelCase__							,       return_attention_mask=UpperCamelCase__							,       do_normalize=UpperCamelCase__)

			SCREAMING_SNAKE_CASE						= hf_config.architectures[0]
			if arch.endswith('ForSequenceClassification'):
						SCREAMING_SNAKE_CASE						= convert_classification(UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__)
			elif arch.endswith('ForAudioFrameClassification'):
						SCREAMING_SNAKE_CASE						= convert_diarization(UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__)
			elif arch.endswith('ForXVector'):
						SCREAMING_SNAKE_CASE						= convert_xvector(UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__)
			else:
						raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''')

			if hf_config.use_weighted_layer_sum:
						SCREAMING_SNAKE_CASE						= checkpoint["""Featurizer"""]["""weights"""]

			hf_feature_extractor.save_pretrained(UpperCamelCase__)
			hf_model.save_pretrained(UpperCamelCase__)


if __name__ == "__main__":
				a_		:						int             =    argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				a_		:						Union[str, Any]             =    parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












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

import datasets


a_		:						Optional[int]             =    '\\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_		:						Tuple             =    '\\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_		:						Optional[Any]             =    '\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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return float((preds == labels).mean())






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= simple_accuracy(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= float(fa_score(y_true=_UpperCAmelCase							,       y_pred=_UpperCAmelCase))
			return {
			    "accuracy": acc,
			    "f1": fa,
			}






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= en_sentvecs.shape[0]

			# mean centering
			SCREAMING_SNAKE_CASE						= en_sentvecs - np.mean(_UpperCAmelCase							,       axis=0)
			SCREAMING_SNAKE_CASE						= in_sentvecs - np.mean(_UpperCAmelCase							,       axis=0)

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







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






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




