
'''simple docstring'''
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowerCAmelCase     							=       'true'






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE=82							,					_SCREAMING_SNAKE_CASE=16      ):
							set_seed(42      )
							_snake_case   =  RegressionModel()
							_snake_case   =  deepcopy(lowercase__      )
							_snake_case   =  RegressionDataset(length=lowercase__      )
							_snake_case   =  DataLoader(lowercase__							,					batch_size=lowercase__      )
							model.to(accelerator.device      )
							_snake_case   =  accelerator.prepare(lowercase__							,					lowercase__      )
							return model, ddp_model, dataloader






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE=False      ):
							_snake_case   =  AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""      )
							_snake_case   =  load_dataset("""glue"""							,					"""mrpc"""							,					split="""validation"""      )

							def tokenize_function(_SCREAMING_SNAKE_CASE      ):
														_snake_case   =  tokenizer(examples["""sentence1"""]							,					examples["""sentence2"""]							,					truncation=lowercase__							,					max_length=lowercase__      )
														return outputs

							with accelerator.main_process_first():
														_snake_case   =  dataset.map(
														    lowercase__							,					batched=lowercase__							,					remove_columns=["""idx""", """sentence1""", """sentence2"""]							,					)

							_snake_case   =  tokenized_datasets.rename_column("""label"""							,					"""labels"""      )

							def collate_fn(_SCREAMING_SNAKE_CASE      ):
														if use_longest:
																					return tokenizer.pad(lowercase__							,					padding="""longest"""							,					return_tensors="""pt"""      )
														return tokenizer.pad(lowercase__							,					padding="""max_length"""							,					max_length=128							,					return_tensors="""pt"""      )

							return DataLoader(lowercase__							,					shuffle=lowercase__							,					collate_fn=lowercase__							,					batch_size=16      )






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE      ):
							_snake_case   =  Accelerator(dispatch_batches=lowercase__							,					split_batches=lowercase__      )
							_snake_case   =  get_dataloader(lowercase__							,					not dispatch_batches      )
							_snake_case   =  AutoModelForSequenceClassification.from_pretrained(
							    """hf-internal-testing/mrpc-bert-base-cased"""							,					return_dict=lowercase__      )
							_snake_case   =  accelerator.prepare(lowercase__							,					lowercase__      )
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE      ):
							_snake_case   =  []
							for batch in dataloader:
														_snake_case   =  batch.values()
														with torch.no_grad():
																					_snake_case   =  model(lowercase__      )
																					_snake_case   =  accelerator.gather_for_metrics((logit, target)      )
																					logits_and_targets.append((logit, target)      )
							_snake_case   =  [], []
							for logit, targ in logits_and_targets:
														logits.append(lowercase__      )
														targs.append(lowercase__      )
							_snake_case   =  torch.cat(lowercase__      ), torch.cat(lowercase__      )
							return logits, targs






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE=82							,					_SCREAMING_SNAKE_CASE=False							,					_SCREAMING_SNAKE_CASE=False							,					_SCREAMING_SNAKE_CASE=16      ):
							_snake_case   =  get_basic_setup(lowercase__							,					lowercase__							,					lowercase__      )
							_snake_case   =  generate_predictions(lowercase__							,					lowercase__							,					lowercase__      )
							assert (
							    len(lowercase__      ) == num_samples
							), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__      )}"""






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE = False							,					_SCREAMING_SNAKE_CASE = False      ):
							_snake_case   =  evaluate.load("""glue"""							,					"""mrpc"""      )
							_snake_case   =  get_mrpc_setup(lowercase__							,					lowercase__      )
							# First do baseline
							_snake_case   =  setup['''no''']
							model.to(lowercase__      )
							model.eval()
							for batch in dataloader:
														batch.to(lowercase__      )
														with torch.inference_mode():
																					_snake_case   =  model(**lowercase__      )
														_snake_case   =  outputs.logits.argmax(dim=-1      )
														metric.add_batch(predictions=lowercase__							,					references=batch["""labels"""]      )
							_snake_case   =  metric.compute()

							# Then do distributed
							_snake_case   =  setup['''ddp''']
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					_snake_case   =  model(**lowercase__      )
														_snake_case   =  outputs.logits.argmax(dim=-1      )
														_snake_case   =  batch['''labels''']
														_snake_case   =  accelerator.gather_for_metrics((preds, references)      )
														metric.add_batch(predictions=lowercase__							,					references=lowercase__      )
							_snake_case   =  metric.compute()

							for key in "accuracy f1".split():
														assert math.isclose(
														    baseline[key]							,					distributed[key]      ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""






def 					__SCREAMING_SNAKE_CASE      (  ):
							_snake_case   =  Accelerator(split_batches=lowercase__							,					dispatch_batches=lowercase__      )
							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_warning()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							# These are a bit slower so they should only be ran on the GPU or TPU
							if torch.cuda.is_available() or is_tpu_available():
														if accelerator.is_local_main_process:
																					print("""**Testing gather_for_metrics**"""      )
														for split_batches in [True, False]:
																					for dispatch_batches in [True, False]:
																												if accelerator.is_local_main_process:
																																			print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""      )
																												test_mrpc(lowercase__							,					lowercase__      )
																												accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test torch metrics**"""      )
							for split_batches in [True, False]:
														for dispatch_batches in [True, False]:
																					_snake_case   =  Accelerator(split_batches=lowercase__							,					dispatch_batches=lowercase__      )
																					if accelerator.is_local_main_process:
																												print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""      )
																					test_torch_metrics(lowercase__							,					99      )
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test last batch is not dropped when perfectly divisible**"""      )
							_snake_case   =  Accelerator()
							test_torch_metrics(lowercase__							,					512      )
							accelerator.state._reset_state()






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE      ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
				main()



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

import datasets


__lowerCAmelCase	: 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'

__lowerCAmelCase	: Optional[Any]  				='\\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	: Dict  				='\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							(      lowercase__    ,       lowercase__      ):
						return float((preds == labels).mean()      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		simple_accuracy(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		float(fa_score(y_true=lowercase__    ,       y_pred=lowercase__      )      )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		en_sentvecs.shape[0]

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

						__SCREAMING_SNAKE_CASE						:     str					=		cdist(lowercase__    ,       lowercase__    ,       '''cosine'''      )
						__SCREAMING_SNAKE_CASE						:     int					=		np.array(range(lowercase__      )      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		sim.argsort(axis=1      )[:, :10]
						__SCREAMING_SNAKE_CASE						:     str					=		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       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Tuple	)		->			Tuple:
										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 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Tuple	)		->			str:
										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"]'''	)
