


'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					lowercase_     (    a__  ,						a__  ,						a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	StableDiffusionInpaintPipeline
    __UpperCAmelCase   			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    __UpperCAmelCase   			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __UpperCAmelCase   			=	frozenset(
        []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __UpperCAmelCase   			=	frozenset([]   )


    def    __a							(     self    ):
          torch.manual_seed(0    )
          UpperCamelCase__										=				UNetaDConditionModel(
              block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=9  ,					out_channels=4  ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,					cross_attention_dim=32  ,					attention_head_dim=(2, 4)  ,					use_linear_projection=a  ,					)
          UpperCamelCase__										=				PNDMScheduler(skip_prk_steps=a    )
          torch.manual_seed(0    )
          UpperCamelCase__										=				AutoencoderKL(
              block_out_channels=[32, 64]  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]  ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]  ,					latent_channels=4  ,					sample_size=1_28  ,					)
          torch.manual_seed(0    )
          UpperCamelCase__										=				CLIPTextConfig(
              bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=32  ,					intermediate_size=37  ,					layer_norm_eps=1e-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=10_00  ,					hidden_act="gelu"  ,					projection_dim=5_12  ,					)
          UpperCamelCase__										=				CLIPTextModel(a    )
          UpperCamelCase__										=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

          UpperCamelCase__										=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components


    def    __a							(     self  ,					a  ,					a=0    ):
          # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
          UpperCamelCase__										=				floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(a    )    ).to(a    )
          UpperCamelCase__										=				image.cpu().permute(0  ,					2  ,					3  ,					1    )[0]
          UpperCamelCase__										=				Image.fromarray(np.uinta(a    )    ).convert("RGB"    ).resize((64, 64)    )
          UpperCamelCase__										=				Image.fromarray(np.uinta(image + 4    )    ).convert("RGB"    ).resize((64, 64)    )
          if str(a    ).startswith("mps"    ):
                UpperCamelCase__										=				torch.manual_seed(a    )
          else:
                UpperCamelCase__										=				torch.Generator(device=a    ).manual_seed(a    )
          UpperCamelCase__										=				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": init_image,
              "mask_image": mask_image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }
          return inputs


    def    __a							(     self    ):
          UpperCamelCase__										=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          UpperCamelCase__										=				self.get_dummy_components()
          UpperCamelCase__										=				StableDiffusionInpaintPipeline(**a    )
          UpperCamelCase__										=				sd_pipe.to(a    )
          sd_pipe.set_progress_bar_config(disable=a    )

          UpperCamelCase__										=				self.get_dummy_inputs(a    )
          UpperCamelCase__										=				sd_pipe(**a    ).images
          UpperCamelCase__										=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase__										=				np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]    )

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


    def    __a							(     self    ):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3    )

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


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


    def    __a							(     self    ):
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"    )
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )
          UpperCamelCase__										=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
              "/yellow_cat_sitting_on_a_park_bench.npy"    )

          UpperCamelCase__										=				"stabilityai/stable-diffusion-2-inpainting"
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(a  ,					safety_checker=a    )
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          pipe.enable_attention_slicing()

          UpperCamelCase__										=				"Face of a yellow cat, high resolution, sitting on a park bench"

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(
              prompt=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					output_type="np"  ,					)
          UpperCamelCase__										=				output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image    ).max() < 9e-3


    def    __a							(     self    ):
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"    )
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )
          UpperCamelCase__										=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
              "/yellow_cat_sitting_on_a_park_bench_fp16.npy"    )

          UpperCamelCase__										=				"stabilityai/stable-diffusion-2-inpainting"
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(
              a  ,					torch_dtype=torch.floataa  ,					safety_checker=a  ,					)
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          pipe.enable_attention_slicing()

          UpperCamelCase__										=				"Face of a yellow cat, high resolution, sitting on a park bench"

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(
              prompt=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					output_type="np"  ,					)
          UpperCamelCase__										=				output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image    ).max() < 5e-1


    def    __a							(     self    ):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"    )
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )

          UpperCamelCase__										=				"stabilityai/stable-diffusion-2-inpainting"
          UpperCamelCase__										=				PNDMScheduler.from_pretrained(a  ,					subfolder="scheduler"    )
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(
              a  ,					safety_checker=a  ,					scheduler=a  ,					torch_dtype=torch.floataa  ,					)
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          pipe.enable_attention_slicing(1    )
          pipe.enable_sequential_cpu_offload()

          UpperCamelCase__										=				"Face of a yellow cat, high resolution, sitting on a park bench"

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(
              prompt=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					num_inference_steps=2  ,					output_type="np"  ,					)

          UpperCamelCase__										=				torch.cuda.max_memory_allocated()
          # make sure that less than 2.65 GB is allocated
          assert mem_bytes < 2.65 * 10**9










"""simple docstring"""






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

import datasets


__snake_case							:   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'

__snake_case							:   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'

__snake_case							:   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					,__snake_case	)						->   Union[str, Any]:
			return float((preds == labels).mean()	)


def 			_lowercase				(  __snake_case					,__snake_case	)						->   str:
			__lowerCAmelCase      :  str												=       simple_accuracy(__snake_case					,__snake_case	)
			__lowerCAmelCase      :  Any												=       float(fa_score(y_true=__snake_case					,y_pred=__snake_case	)	)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}


def 			_lowercase				(  __snake_case					,__snake_case	)						->   int:
			__lowerCAmelCase      :  Union[str, Any]												=       np.array(__snake_case	)
			__lowerCAmelCase      :  Tuple												=       np.array(__snake_case	)
			__lowerCAmelCase      :  List[Any]												=       en_sentvecs.shape[0]

			# mean centering
			__lowerCAmelCase      :  Union[str, Any]												=       en_sentvecs - np.mean(__snake_case					,axis=0	)
			__lowerCAmelCase      :  int												=       in_sentvecs - np.mean(__snake_case					,axis=0	)

			__lowerCAmelCase      :  Optional[Any]												=       cdist(__snake_case					,__snake_case					,"cosine"	)
			__lowerCAmelCase      :  int												=       np.array(range(__snake_case	)	)
			__lowerCAmelCase      :  int												=       sim.argsort(axis=1	)[:, :10]
			__lowerCAmelCase      :  Optional[Any]												=       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							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       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 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       int:

				"""simple docstring"""

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