

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"  )
      _lowercase      :				Union[str, Any]      					=       sd_pipe.to(_UpperCamelCase  )
      sd_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      sd_pipe.set_scheduler("sample_euler"  )

      _lowercase      :				List[Any]      					=       "A painting of a squirrel eating a burger"
      _lowercase      :				Union[str, Any]      					=       torch.manual_seed(0  )
      _lowercase      :				Optional[int]      					=       sd_pipe([prompt]      ,    generator=_UpperCamelCase      ,    guidance_scale=9.0      ,    num_inference_steps=20      ,    output_type="np"  )

      _lowercase      :				Any      					=       output.images

      _lowercase      :				List[Any]      					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowercase      :				int      					=       np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]  )

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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"  )
      _lowercase      :				List[str]      					=       sd_pipe.to(_UpperCamelCase  )
      sd_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      sd_pipe.set_scheduler("sample_euler"  )

      _lowercase      :				List[str]      					=       "A painting of a squirrel eating a burger"
      _lowercase      :				Union[str, Any]      					=       torch.manual_seed(0  )
      _lowercase      :				Any      					=       sd_pipe([prompt]      ,    generator=_UpperCamelCase      ,    guidance_scale=9.0      ,    num_inference_steps=20      ,    output_type="np"  )

      _lowercase      :				Tuple      					=       output.images

      _lowercase      :				List[Any]      					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowercase      :				Union[str, Any]      					=       np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-1






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"  )
      _lowercase      :				Any      					=       sd_pipe.to(_UpperCamelCase  )
      sd_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      sd_pipe.set_scheduler("sample_dpmpp_2m"  )

      _lowercase      :				Union[str, Any]      					=       "A painting of a squirrel eating a burger"
      _lowercase      :				Dict      					=       torch.manual_seed(0  )
      _lowercase      :				List[str]      					=       sd_pipe(
          [prompt]      ,    generator=_UpperCamelCase      ,    guidance_scale=7.5      ,    num_inference_steps=15      ,    output_type="np"      ,    use_karras_sigmas=_UpperCamelCase      ,    )

      _lowercase      :				int      					=       output.images

      _lowercase      :				int      					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowercase      :				int      					=       np.array(
          [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]  )

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









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

import datasets


_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'

_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'

_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  _A		(     snake_case		,							snake_case					)      ->     int:
   return float((preds == labels).mean()					)







def  _A		(     snake_case		,							snake_case					)      ->     Union[str, Any]:
   _lowercase      :				Any      					=       simple_accuracy(snake_case		,							snake_case					)
   _lowercase      :				List[Any]      					=       float(fa_score(y_true=snake_case		,							y_pred=snake_case					)					)
   return {
       "accuracy": acc,
       "f1": fa,
   }







def  _A		(     snake_case		,							snake_case					)      ->     List[str]:
   _lowercase      :				Any      					=       np.array(snake_case					)
   _lowercase      :				Any      					=       np.array(snake_case					)
   _lowercase      :				str      					=       en_sentvecs.shape[0]

   # mean centering
   _lowercase      :				List[Any]      					=       en_sentvecs - np.mean(snake_case		,							axis=0					)
   _lowercase      :				Tuple      					=       in_sentvecs - np.mean(snake_case		,							axis=0					)

   _lowercase      :				Any      					=       cdist(snake_case		,							snake_case		,							"cosine"					)
   _lowercase      :				Any      					=       np.array(range(snake_case					)					)
   _lowercase      :				Dict      					=       sim.argsort(axis=1					)[:, :10]
   _lowercase      :				List[str]      					=       np.any(preds == actual[:, None]		,							axis=1					)
   return float(matches.mean()					)


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


   def 						_lowerCamelCase       (			self  ):





      """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      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





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






