

"""simple docstring"""




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

import datasets


_lowercase   :	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'

_lowercase   :	Optional[int]												=						'\\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'

_lowercase   :	str												=						'\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__						(       snake_case_   :Optional[Any]			,		snake_case_   :Dict    ):
	return float((preds == labels).mean()    )





def       lowercase__						(       snake_case_   :Union[str, Any]			,		snake_case_   :str    ):
	__UpperCAmelCase      		= simple_accuracy(snake_case_			,		snake_case_    )
	__UpperCAmelCase      		= float(fa_score(y_true=snake_case_			,		y_pred=snake_case_    )    )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}





def       lowercase__						(       snake_case_   :str			,		snake_case_   :List[Any]    ):
	__UpperCAmelCase      		= np.array(snake_case_    )
	__UpperCAmelCase      		= np.array(snake_case_    )
	__UpperCAmelCase      		= en_sentvecs.shape[0]

	# mean centering
	__UpperCAmelCase      		= en_sentvecs - np.mean(snake_case_			,		axis=0    )
	__UpperCAmelCase      		= in_sentvecs - np.mean(snake_case_			,		axis=0    )

	__UpperCAmelCase      		= cdist(snake_case_			,		snake_case_			,		'''cosine'''    )
	__UpperCAmelCase      		= np.array(range(snake_case_    )    )
	__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      _UpperCAmelCase   (							datasets.Metric       ):



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

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2