


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

import datasets


_UpperCAmelCase	:     Optional[Any]   ="""\\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"""

_UpperCAmelCase	:     List[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"""

_UpperCAmelCase	:     List[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 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       List[str]:
				return float((preds == labels).mean()       )



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					Any			 = simple_accuracy(_lowercase       ,      _lowercase       )
				lowerCAmelCase_       :					List[str]			 = float(fa_score(y_true=_lowercase       ,      y_pred=_lowercase       )       )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       str:
				lowerCAmelCase_       :					Union[str, Any]			 = np.array(_lowercase       )
				lowerCAmelCase_       :					Tuple			 = np.array(_lowercase       )
				lowerCAmelCase_       :					Dict			 = en_sentvecs.shape[0]

				# mean centering
				lowerCAmelCase_       :					int			 = en_sentvecs - np.mean(_lowercase       ,      axis=0       )
				lowerCAmelCase_       :					Dict			 = in_sentvecs - np.mean(_lowercase       ,      axis=0       )

				lowerCAmelCase_       :					int			 = cdist(_lowercase       ,      _lowercase       ,      '''cosine'''       )
				lowerCAmelCase_       :					Tuple			 = np.array(range(_lowercase       )       )
				lowerCAmelCase_       :					Optional[Any]			 = sim.argsort(axis=1       )[:, :10]
				lowerCAmelCase_       :					List[str]			 = 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							):







						'''simple docstring'''




						def   lowercase_       ( 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   lowercase_       ( self      ,  __lowercase      ,  __lowercase							)    ->			Tuple:
										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\"]'''							)




"""simple docstring"""
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard
@slow
@require_flax
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Any:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :List[str]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-canny"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :Union[str, Any]						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :Union[str, Any]						=			controlnet_params

											snake_case_ :Union[str, Any]						=			"""bird"""
											snake_case_ :List[Any]						=			jax.device_count()
											snake_case_ :List[Any]						=			pipe.prepare_text_inputs([prompts] * num_samples						)

											snake_case_ :List[str]						=			load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""						)
											snake_case_ :List[str]						=			pipe.prepare_image_inputs([canny_image] * num_samples						)

											snake_case_ :Any						=			jax.random.PRNGKey(0						)
											snake_case_ :List[str]						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :List[Any]						=			replicate(snake_case						)
											snake_case_ :List[str]						=			shard(snake_case						)
											snake_case_ :str						=			shard(snake_case						)

											snake_case_ :Dict						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :Union[str, Any]						=			images[0, 253:256, 253:256, -1]

											snake_case_ :str						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Dict						=			jnp.array(
											    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_,       snake_case_ :List[Any]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-openpose"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :int						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :str						=			controlnet_params

											snake_case_ :Optional[int]						=			"""Chef in the kitchen"""
											snake_case_ :Union[str, Any]						=			jax.device_count()
											snake_case_ :Any						=			pipe.prepare_text_inputs([prompts] * num_samples						)

											snake_case_ :str						=			load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""						)
											snake_case_ :Optional[Any]						=			pipe.prepare_image_inputs([pose_image] * num_samples						)

											snake_case_ :str						=			jax.random.PRNGKey(0						)
											snake_case_ :str						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :Tuple						=			replicate(snake_case						)
											snake_case_ :str						=			shard(snake_case						)
											snake_case_ :int						=			shard(snake_case						)

											snake_case_ :List[str]						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :int						=			images[0, 253:256, 253:256, -1]

											snake_case_ :Dict						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Optional[int]						=			jnp.array(
											    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2
