






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 		_snake_case  :






							def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= UNetaDConditionModel(
										    sample_size=32		,					layers_per_block=1		,					block_out_channels=[32, 64]		,					down_block_types=[
										        'ResnetDownsampleBlock2D',
										        'SimpleCrossAttnDownBlock2D',
										    ]		,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'		,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']		,					in_channels=3		,					out_channels=6		,					cross_attention_dim=32		,					encoder_hid_dim=32		,					attention_head_dim=8		,					addition_embed_type='text'		,					addition_embed_type_num_heads=2		,					cross_attention_norm='group_norm'		,					resnet_time_scale_shift='scale_shift'		,					act_fn='gelu'		,					)
										unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= DDPMScheduler(
										    num_train_timesteps=1000		,					beta_schedule='squaredcos_cap_v2'		,					beta_start=0.00_01		,					beta_end=0.02		,					thresholding=a		,					dynamic_thresholding_ratio=0.95		,					sample_max_value=1.0		,					prediction_type='epsilon'		,					variance_type='learned_range'		,					)

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}






							def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= UNetaDConditionModel(
										    sample_size=32		,					layers_per_block=[1, 2]		,					block_out_channels=[32, 64]		,					down_block_types=[
										        'ResnetDownsampleBlock2D',
										        'SimpleCrossAttnDownBlock2D',
										    ]		,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'		,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']		,					in_channels=6		,					out_channels=6		,					cross_attention_dim=32		,					encoder_hid_dim=32		,					attention_head_dim=8		,					addition_embed_type='text'		,					addition_embed_type_num_heads=2		,					cross_attention_norm='group_norm'		,					resnet_time_scale_shift='scale_shift'		,					act_fn='gelu'		,					class_embed_type='timestep'		,					mid_block_scale_factor=1.4_14		,					time_embedding_act_fn='gelu'		,					time_embedding_dim=32		,					)
										unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= DDPMScheduler(
										    num_train_timesteps=1000		,					beta_schedule='squaredcos_cap_v2'		,					beta_start=0.00_01		,					beta_end=0.02		,					thresholding=a		,					dynamic_thresholding_ratio=0.95		,					sample_max_value=1.0		,					prediction_type='epsilon'		,					variance_type='learned_range'		,					)

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= DDPMScheduler(
										    num_train_timesteps=1000		,					beta_schedule='squaredcos_cap_v2'		,					beta_start=0.00_01		,					beta_end=0.02		,					)

										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE						= IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "image_noising_scheduler": image_noising_scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}






							def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
										SCREAMING_SNAKE_CASE						= self.get_dummy_components()
										SCREAMING_SNAKE_CASE						= self.pipeline_class(**a)
										pipe.to(a)
										pipe.set_progress_bar_config(disable=a)

										SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)

										SCREAMING_SNAKE_CASE						= inputs['prompt']
										SCREAMING_SNAKE_CASE						= inputs['generator']
										SCREAMING_SNAKE_CASE						= inputs['num_inference_steps']
										SCREAMING_SNAKE_CASE						= inputs['output_type']

										if "image" in inputs:
													SCREAMING_SNAKE_CASE						= inputs['image']
										else:
													SCREAMING_SNAKE_CASE						= None

										if "mask_image" in inputs:
													SCREAMING_SNAKE_CASE						= inputs['mask_image']
										else:
													SCREAMING_SNAKE_CASE						= None

										if "original_image" in inputs:
													SCREAMING_SNAKE_CASE						= inputs['original_image']
										else:
													SCREAMING_SNAKE_CASE						= None

										SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pipe.encode_prompt(a)

										# inputs with prompt converted to embeddings
										SCREAMING_SNAKE_CASE						= {
										    'prompt_embeds': prompt_embeds,
										    'negative_prompt_embeds': negative_prompt_embeds,
										    'generator': generator,
										    'num_inference_steps': num_inference_steps,
										    'output_type': output_type,
										}

										if image is not None:
													SCREAMING_SNAKE_CASE						= image

										if mask_image is not None:
													SCREAMING_SNAKE_CASE						= mask_image

										if original_image is not None:
													SCREAMING_SNAKE_CASE						= original_image

										# set all optional components to None
										for optional_component in pipe._optional_components:
													setattr(a		,					a		,					a)

										SCREAMING_SNAKE_CASE						= pipe(**a)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(a)
													SCREAMING_SNAKE_CASE						= self.pipeline_class.from_pretrained(a)
													pipe_loaded.to(a)
													pipe_loaded.set_progress_bar_config(disable=a)

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

										for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(a		,					a) is None		,					f'''`{optional_component}` did not stay set to None after loading.'''		,					)

										SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)

										SCREAMING_SNAKE_CASE						= inputs['generator']
										SCREAMING_SNAKE_CASE						= inputs['num_inference_steps']
										SCREAMING_SNAKE_CASE						= inputs['output_type']

										# inputs with prompt converted to embeddings
										SCREAMING_SNAKE_CASE						= {
										    'prompt_embeds': prompt_embeds,
										    'negative_prompt_embeds': negative_prompt_embeds,
										    'generator': generator,
										    'num_inference_steps': num_inference_steps,
										    'output_type': output_type,
										}

										if image is not None:
													SCREAMING_SNAKE_CASE						= image

										if mask_image is not None:
													SCREAMING_SNAKE_CASE						= mask_image

										if original_image is not None:
													SCREAMING_SNAKE_CASE						= original_image

										SCREAMING_SNAKE_CASE						= pipe_loaded(**a)[0]

										SCREAMING_SNAKE_CASE						= np.abs(to_np(a) - to_np(a)).max()
										self.assertLess(a		,					1E-4)







							def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
										SCREAMING_SNAKE_CASE						= self.get_dummy_components()
										SCREAMING_SNAKE_CASE						= self.pipeline_class(**a)
										pipe.to(a)
										pipe.set_progress_bar_config(disable=a)

										SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)
										SCREAMING_SNAKE_CASE						= pipe(**a)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(a)
													SCREAMING_SNAKE_CASE						= self.pipeline_class.from_pretrained(a)
													pipe_loaded.to(a)
													pipe_loaded.set_progress_bar_config(disable=a)

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

										SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)
										SCREAMING_SNAKE_CASE						= pipe_loaded(**a)[0]

										SCREAMING_SNAKE_CASE						= np.abs(to_np(a) - to_np(a)).max()
										self.assertLess(a		,					1E-4)












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

import datasets


a_		:						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'

a_		:						Tuple             =    '\\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'

a_		:						Optional[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__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return float((preds == labels).mean())






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= simple_accuracy(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= float(fa_score(y_true=_UpperCAmelCase							,       y_pred=_UpperCAmelCase))
			return {
			    "accuracy": acc,
			    "f1": fa,
			}






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= en_sentvecs.shape[0]

			# mean centering
			SCREAMING_SNAKE_CASE						= en_sentvecs - np.mean(_UpperCAmelCase							,       axis=0)
			SCREAMING_SNAKE_CASE						= in_sentvecs - np.mean(_UpperCAmelCase							,       axis=0)

			SCREAMING_SNAKE_CASE						= cdist(_UpperCAmelCase							,       _UpperCAmelCase							,       'cosine')
			SCREAMING_SNAKE_CASE						= np.array(range(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= sim.argsort(axis=1)[:, :10]
			SCREAMING_SNAKE_CASE						= np.any(preds == actual[:, None]							,       axis=1)
			return float(matches.mean())







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






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




