





'''simple docstring'''


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

import datasets


lowerCAmelCase_   :   str       =					"\\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"

lowerCAmelCase_   :   List[str]       =					"\\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"

lowerCAmelCase_   :   Union[str, 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  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     Dict     )   ->    List[str]:
			return float((preds == labels).mean()     )







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







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Dict     )   ->    Optional[Any]:
			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       UpperCamelCase__  (		datasets.Metric     ):







	def 						__a   (				self			:   str		):




				'''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 						__a   (				self			:   Tuple , lowerCamelCase			:   Dict , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				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'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  max_position_embeddings=1024					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  encoder_layerdrop=args.encoder_layerdrop					,  decoder_layerdrop=args.decoder_layerdrop					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



