



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

							if is_vision_available():
														from transformers import MaskFormerImageProcessor

if is_vision_available():
							from PIL import Image



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=False	,__lowerCamelCase=10	,__lowerCamelCase=3	,__lowerCamelCase=32 * 4	,__lowerCamelCase=32 * 6	,__lowerCamelCase=4	,__lowerCamelCase=32	,)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  parent
						lowerCAmelCase__  :       List[str]				=  batch_size
						lowerCAmelCase__  :       str				=  is_training
						lowerCAmelCase__  :       Dict				=  use_auxiliary_loss
						lowerCAmelCase__  :       List[str]				=  num_queries
						lowerCAmelCase__  :       int				=  num_channels
						lowerCAmelCase__  :       List[str]				=  min_size
						lowerCAmelCase__  :       Any				=  max_size
						lowerCAmelCase__  :       Union[str, Any]				=  num_labels
						lowerCAmelCase__  :       List[Any]				=  mask_feature_size



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
						    __lowerCamelCase				)

						lowerCAmelCase__  :       str				=  torch.ones([self.batch_size, self.min_size, self.max_size]	,device=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[int]				=  (
						    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,device=__lowerCamelCase				) > 0.5
						).float()
						lowerCAmelCase__  :       str				=  (torch.rand((self.batch_size, self.num_labels)	,device=__lowerCamelCase				) > 0.5).long()

						lowerCAmelCase__  :       Optional[Any]				=  self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						return MaskFormerConfig.from_backbone_and_decoder_configs(
						    backbone_config=SwinConfig(
						        depths=[1, 1, 1, 1]	,)	,decoder_config=DetrConfig(
						        decoder_ffn_dim=1_28	,num_queries=self.num_queries	,decoder_attention_heads=2	,d_model=self.mask_feature_size	,)	,mask_feature_size=self.mask_feature_size	,fpn_feature_size=self.mask_feature_size	,num_channels=self.num_channels	,num_labels=self.num_labels	,)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__  :       Optional[int]				=  {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  output.encoder_hidden_states
						lowerCAmelCase__  :       List[Any]				=  output.pixel_decoder_hidden_states
						lowerCAmelCase__  :       str				=  output.transformer_decoder_hidden_states

						self.parent.assertTrue(len(__lowerCamelCase				)	,len(config.backbone_config.depths				)				)
						self.parent.assertTrue(len(__lowerCamelCase				)	,len(config.backbone_config.depths				)				)
						self.parent.assertTrue(len(__lowerCamelCase				)	,config.decoder_config.decoder_layers				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=False				)  ->    List[Any]:

						"""simple docstring"""






						with torch.no_grad():
										lowerCAmelCase__  :       int				=  MaskFormerModel(config=__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()

										lowerCAmelCase__  :       str				=  model(pixel_values=__lowerCamelCase	,pixel_mask=__lowerCamelCase				)
										lowerCAmelCase__  :       Union[str, Any]				=  model(__lowerCamelCase	,output_hidden_states=__lowerCamelCase				)
						# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
						# encoder and pixel decoder
						self.parent.assertEqual(
						    output.transformer_decoder_last_hidden_state.shape	,(self.batch_size, self.num_queries, self.mask_feature_size)	,)
						# let's ensure the other two hidden state exists
						self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
						self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

						if output_hidden_states:
										self.check_output_hidden_state(__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  MaskFormerForInstanceSegmentation(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()

						def comm_check_on_output(__lowerCamelCase				):
										# let's still check that all the required stuff is there
										self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
										self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
										self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
										# okay, now we need to check the logits shape
										# due to the encoder compression, masks have a //4 spatial size
										self.parent.assertEqual(
										    result.masks_queries_logits.shape	,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,)
										# + 1 for null class
										self.parent.assertEqual(
										    result.class_queries_logits.shape	,(self.batch_size, self.num_queries, self.num_labels + 1)				)

						with torch.no_grad():
										lowerCAmelCase__  :       str				=  model(pixel_values=__lowerCamelCase	,pixel_mask=__lowerCamelCase				)
										lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase				)

										comm_check_on_output(__lowerCamelCase				)

										lowerCAmelCase__  :       Union[str, Any]				=  model(
										    pixel_values=__lowerCamelCase	,pixel_mask=__lowerCamelCase	,mask_labels=__lowerCamelCase	,class_labels=__lowerCamelCase				)

						comm_check_on_output(__lowerCamelCase				)

						self.parent.assertTrue(result.loss is not None				)
						self.parent.assertEqual(result.loss.shape	,torch.Size([1]				)				)



@require_torch
class  lowerCamelCase__    (							_UpperCamelCase   ,       _UpperCamelCase   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
		snake_case_			=(
		    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
		    if is_torch_available()
		    else {}
		)

		snake_case_			=False
		snake_case_			=False
		snake_case_			=False
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MaskFormerModelTester(self				)
						lowerCAmelCase__  :       Union[str, Any]				=  ConfigTester(self	,config_class=__lowerCamelCase	,has_text_modality=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskformer_model(__lowerCamelCase	,**__lowerCamelCase	,output_hidden_states=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCamelCase				)



		@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''				)
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''				)
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MaskFormer is not a generative model'''				)
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MaskFormer does not use token embeddings'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						pass



		@require_torch_multi_gpu
		@unittest.skip(
		    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''				)
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						pass



		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       int				=  model_class(__lowerCamelCase				)
										lowerCAmelCase__  :       str				=  inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__  :       Optional[int]				=  [*signature.parameters.keys()]

										lowerCAmelCase__  :       int				=  ["pixel_values"]
										self.assertListEqual(arg_names[:1]	,__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						for model_name in ["facebook/maskformer-swin-small-coco"]:
										lowerCAmelCase__  :       str				=  MaskFormerModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  (self.model_tester.min_size,) * 2
						lowerCAmelCase__  :       Optional[int]				=  {
						    "pixel_values": torch.randn((2, 3, *size)	,device=__lowerCamelCase				),
						    "mask_labels": torch.randn((2, 10, *size)	,device=__lowerCamelCase				),
						    "class_labels": torch.zeros(2	,10	,device=__lowerCamelCase				).long(),
						}

						lowerCAmelCase__  :       List[Any]				=  MaskFormerForInstanceSegmentation(MaskFormerConfig()				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  model(**__lowerCamelCase				)
						self.assertTrue(outputs.loss is not None				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskformer_model(__lowerCamelCase	,**__lowerCamelCase	,output_hidden_states=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       int				=  model_class(__lowerCamelCase				).to(__lowerCamelCase				)
										lowerCAmelCase__  :       Tuple				=  model(**__lowerCamelCase	,output_attentions=__lowerCamelCase				)
										self.assertTrue(outputs.attentions is not None				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						if not self.model_tester.is_training:
										return
						# only MaskFormerForInstanceSegmentation has the loss
						lowerCAmelCase__  :       Any				=  self.all_model_classes[1]
						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()

						lowerCAmelCase__  :       str				=  model_class(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.train()

						lowerCAmelCase__  :       int				=  model(__lowerCamelCase	,mask_labels=__lowerCamelCase	,class_labels=__lowerCamelCase				).loss
						loss.backward()



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.all_model_classes[1]
						lowerCAmelCase__  :       Optional[int]				=  self.model_tester.prepare_config_and_inputs()
						lowerCAmelCase__  :       List[Any]				=  True
						lowerCAmelCase__  :       Tuple				=  True

						lowerCAmelCase__  :       str				=  model_class(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.train()

						lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase	,mask_labels=__lowerCamelCase	,class_labels=__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						lowerCAmelCase__  :       Tuple				=  outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()
						# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
						lowerCAmelCase__  :       Any				=  outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

						lowerCAmelCase__  :       str				=  outputs.attentions[0]
						attentions.retain_grad()

						outputs.loss.backward(retain_graph=__lowerCamelCase				)

						self.assertIsNotNone(encoder_hidden_states.grad				)
						self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
						self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
						self.assertIsNotNone(attentions.grad				)


__snake_case       :	Dict            =1E-4






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image



@require_vision
@slow
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		@cached_property
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						return (
						    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
						    if is_vision_available()
						    else None
						)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  self.default_image_processor
						lowerCAmelCase__  :       int				=  prepare_img()
						lowerCAmelCase__  :       List[Any]				=  image_processor(__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
						# check size
						self.assertEqual(__lowerCamelCase	,(1, 3, 8_00, 10_88)				)

						with torch.no_grad():
										lowerCAmelCase__  :       Dict				=  model(**__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  torch.tensor(
						    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]				).to(__lowerCamelCase				)
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)

						lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(
						    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]				).to(__lowerCamelCase				)
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)

						lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(
						    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]				).to(__lowerCamelCase				)
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  (
						    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
						    .to(__lowerCamelCase				)
						    .eval()
						)
						lowerCAmelCase__  :       str				=  self.default_image_processor
						lowerCAmelCase__  :       Dict				=  prepare_img()
						lowerCAmelCase__  :       List[str]				=  image_processor(__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
						# check size
						self.assertEqual(__lowerCamelCase	,(1, 3, 8_00, 10_88)				)

						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)
						# masks_queries_logits
						lowerCAmelCase__  :       Any				=  outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape	,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,)
						lowerCAmelCase__  :       Optional[int]				=  [
						    [-1.373_7124, -1.772_4937, -1.936_4233],
						    [-1.597_7281, -1.986_7939, -2.152_3695],
						    [-1.579_5398, -1.926_9832, -2.09_3942],
						]
						lowerCAmelCase__  :       Tuple				=  torch.tensor(__lowerCamelCase				).to(__lowerCamelCase				)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)
						# class_queries_logits
						lowerCAmelCase__  :       Union[str, Any]				=  outputs.class_queries_logits
						self.assertEqual(
						    class_queries_logits.shape	,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
						lowerCAmelCase__  :       Tuple				=  torch.tensor(
						    [
						        [1.6512e00, -5.2572e00, -3.3519e00],
						        [3.6169e-02, -5.9025e00, -2.9313e00],
						        [1.0766e-04, -7.7630e00, -5.1263e00],
						    ]				).to(__lowerCamelCase				)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  (
						    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''				)
						    .to(__lowerCamelCase				)
						    .eval()
						)
						lowerCAmelCase__  :       int				=  self.default_image_processor
						lowerCAmelCase__  :       Optional[Any]				=  prepare_img()
						lowerCAmelCase__  :       Union[str, Any]				=  image_processor(__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  inputs["pixel_values"].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
						# check size
						self.assertEqual(__lowerCamelCase	,(1, 3, 8_00, 10_88)				)

						with torch.no_grad():
										lowerCAmelCase__  :       Any				=  model(**__lowerCamelCase				)
						# masks_queries_logits
						lowerCAmelCase__  :       List[Any]				=  outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape	,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,)
						lowerCAmelCase__  :       Optional[Any]				=  [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
						lowerCAmelCase__  :       List[Any]				=  torch.tensor(__lowerCamelCase				).to(__lowerCamelCase				)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)
						# class_queries_logits
						lowerCAmelCase__  :       Any				=  outputs.class_queries_logits
						self.assertEqual(
						    class_queries_logits.shape	,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
						lowerCAmelCase__  :       Dict				=  torch.tensor(
						    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]				).to(__lowerCamelCase				)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,__lowerCamelCase	,atol=__lowerCamelCase				)				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  (
						    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
						    .to(__lowerCamelCase				)
						    .eval()
						)
						lowerCAmelCase__  :       Dict				=  self.default_image_processor

						lowerCAmelCase__  :       List[str]				=  image_processor(
						    [np.zeros((3, 8_00, 13_33)				), np.zeros((3, 8_00, 13_33)				)]	,segmentation_maps=[np.zeros((3_84, 3_84)				).astype(np.floataa				), np.zeros((3_84, 3_84)				).astype(np.floataa				)]	,return_tensors='''pt'''	,)

						lowerCAmelCase__  :       Tuple				=  inputs["pixel_values"].to(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  [el.to(__lowerCamelCase				) for el in inputs["mask_labels"]]
						lowerCAmelCase__  :       Optional[Any]				=  [el.to(__lowerCamelCase				) for el in inputs["class_labels"]]

						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)

						self.assertTrue(outputs.loss is not None				)






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__snake_case       :	str            ='\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

__snake_case       :	Tuple            ='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

__snake_case       :	int            ='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				return float((preds == labels).mean())






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  simple_accuracy(lowerCamelCase_ ,lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  float(fa_score(y_true=lowerCamelCase_ ,y_pred=lowerCamelCase_))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  float(pearsonr(lowerCamelCase_ ,lowerCamelCase_)[0])
				lowerCAmelCase__  :       int				=  float(spearmanr(lowerCamelCase_ ,lowerCamelCase_)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						if self.config_name not in [
						    "sst2",
						    "mnli",
						    "mnli_mismatched",
						    "mnli_matched",
						    "cola",
						    "stsb",
						    "mrpc",
						    "qqp",
						    "qnli",
						    "rte",
						    "wnli",
						    "hans",
						]:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
										    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
						return datasets.MetricInfo(
						    description=_DESCRIPTION	,citation=_CITATION	,inputs_description=_KWARGS_DESCRIPTION	,features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
						            '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
						        }				)	,codebase_urls=[]	,reference_urls=[]	,format='''numpy'''	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						if self.config_name == "cola":
										return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase	,__lowerCamelCase				)}
						elif self.config_name == "stsb":
										return pearson_and_spearman(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["mrpc", "qqp"]:
										return acc_and_fa(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
										return {"accuracy": simple_accuracy(__lowerCamelCase	,__lowerCamelCase				)}
						else:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
										    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)

