





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 							_UpperCAmelCase       :

  """simple docstring"""


  def __init__(							self					:						str   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Tuple=2   ,    lowerCAmelCase_					:						str=True   ,    lowerCAmelCase_					:						Any=False   ,    lowerCAmelCase_					:						Dict=1_0   ,    lowerCAmelCase_					:						List[str]=3   ,    lowerCAmelCase_					:						Union[str, Any]=3_2 * 4   ,    lowerCAmelCase_					:						int=3_2 * 6   ,    lowerCAmelCase_					:						Optional[Any]=4   ,    lowerCAmelCase_					:						int=3_2   ,    )				->		Union[str, Any]:
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      is_training
     __lowerCAmelCase				      =      use_auxiliary_loss
     __lowerCAmelCase				      =      num_queries
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      min_size
     __lowerCAmelCase				      =      max_size
     __lowerCAmelCase				      =      num_labels
     __lowerCAmelCase				      =      mask_feature_size






  def 							lowercase						(							self					:						Dict	)				->		Any:
     __lowerCAmelCase				      =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
         lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.ones([self.batch_size, self.min_size, self.max_size]   ,    device=lowerCAmelCase_	)

     __lowerCAmelCase				      =      (
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,    device=lowerCAmelCase_	) > 0.5
     ).float()
     __lowerCAmelCase				      =      (torch.rand((self.batch_size, self.num_labels)   ,    device=lowerCAmelCase_	) > 0.5).long()

     __lowerCAmelCase				      =      self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels






  def 							lowercase						(							self					:						str	)				->		List[Any]:
     return MaskFormerConfig.from_backbone_and_decoder_configs(
         backbone_config=SwinConfig(
             depths=[1, 1, 1, 1]   ,    )   ,    decoder_config=DetrConfig(
             decoder_ffn_dim=1_2_8   ,    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 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     __lowerCAmelCase				      =      {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
     return config, inputs_dict






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						List[str]	)				->		Tuple:
     __lowerCAmelCase				      =      output.encoder_hidden_states
     __lowerCAmelCase				      =      output.pixel_decoder_hidden_states
     __lowerCAmelCase				      =      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 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						List[str]=False	)				->		Tuple:
     with torch.no_grad():
        __lowerCAmelCase				      =      MaskFormerModel(config=lowerCAmelCase_	)
        model.to(lowerCAmelCase_	)
        model.eval()

        __lowerCAmelCase				      =      model(pixel_values=lowerCAmelCase_   ,    pixel_mask=lowerCAmelCase_	)
        __lowerCAmelCase				      =      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 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Dict	)				->		Optional[Any]:
     __lowerCAmelCase				      =      MaskFormerForInstanceSegmentation(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()

     def comm_check_on_output(lowerCAmelCase_					:						Tuple	):
        # 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				      =      model(pixel_values=lowerCAmelCase_   ,    pixel_mask=lowerCAmelCase_	)
        __lowerCAmelCase				      =      model(lowerCAmelCase_	)

        comm_check_on_output(lowerCAmelCase_	)

        __lowerCAmelCase				      =      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 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


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

  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False






  def 							lowercase						(							self					:						Tuple	)				->		List[str]:
     __lowerCAmelCase				      =      MaskFormerModelTester(self	)
     __lowerCAmelCase				      =      ConfigTester(self   ,    config_class=lowerCAmelCase_   ,    has_text_modality=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Dict	)				->		Optional[int]:
     self.config_tester.run_common_tests()






  def 							lowercase						(							self					:						Optional[int]	)				->		Tuple:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowerCAmelCase_   ,    **lowerCAmelCase_   ,    output_hidden_states=lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[str]	)				->		Tuple:
     __lowerCAmelCase				      =      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 							lowercase						(							self					:						Union[str, Any]	)				->		int:
     pass






  @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'	)
  def 							lowercase						(							self					:						Tuple	)				->		Union[str, Any]:
     pass






  @unittest.skip(reason='MaskFormer is not a generative model'	)
  def 							lowercase						(							self					:						Any	)				->		Optional[Any]:
     pass






  @unittest.skip(reason='MaskFormer does not use token embeddings'	)
  def 							lowercase						(							self					:						Union[str, Any]	)				->		str:
     pass






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






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






  def 							lowercase						(							self					:						Dict	)				->		int:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCAmelCase				      =      ['pixel_values']
        self.assertListEqual(arg_names[:1]   ,    lowerCAmelCase_	)






  @slow
  def 							lowercase						(							self					:						Any	)				->		Optional[int]:
     for model_name in ["facebook/maskformer-swin-small-coco"]:
        __lowerCAmelCase				      =      MaskFormerModel.from_pretrained(lowerCAmelCase_	)
        self.assertIsNotNone(lowerCAmelCase_	)






  def 							lowercase						(							self					:						str	)				->		Optional[int]:
     __lowerCAmelCase				      =      (self.model_tester.min_size,) * 2
     __lowerCAmelCase				      =      {
         'pixel_values': torch.randn((2, 3, *size)   ,    device=lowerCAmelCase_	),
         'mask_labels': torch.randn((2, 1_0, *size)   ,    device=lowerCAmelCase_	),
         'class_labels': torch.zeros(2   ,    1_0   ,    device=lowerCAmelCase_	).long(),
     }

     __lowerCAmelCase				      =      MaskFormerForInstanceSegmentation(MaskFormerConfig()	).to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      model(**lowerCAmelCase_	)
     self.assertTrue(outputs.loss is not None	)






  def 							lowercase						(							self					:						str	)				->		Optional[int]:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowerCAmelCase_   ,    **lowerCAmelCase_   ,    output_hidden_states=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Optional[int]	)				->		Dict:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	).to(lowerCAmelCase_	)
        __lowerCAmelCase				      =      model(**lowerCAmelCase_   ,    output_attentions=lowerCAmelCase_	)
        self.assertTrue(outputs.attentions is not None	)






  def 							lowercase						(							self					:						str	)				->		List[str]:
     if not self.model_tester.is_training:
        return
     # only MaskFormerForInstanceSegmentation has the loss
     __lowerCAmelCase				      =      self.all_model_classes[1]
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()

     __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.train()

     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    mask_labels=lowerCAmelCase_   ,    class_labels=lowerCAmelCase_	).loss
     loss.backward()






  def 							lowercase						(							self					:						str	)				->		Union[str, Any]:
     # only MaskFormerForInstanceSegmentation has the loss
     __lowerCAmelCase				      =      self.all_model_classes[1]
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     __lowerCAmelCase				      =      True
     __lowerCAmelCase				      =      True

     __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.train()

     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    mask_labels=lowerCAmelCase_   ,    class_labels=lowerCAmelCase_	)

     __lowerCAmelCase				      =      outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     __lowerCAmelCase				      =      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				      =      outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     __lowerCAmelCase				      =      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    :       Any		       =    1e-4

def     a_       (					):
   __lowerCAmelCase				      =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image




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

  """simple docstring"""


  @cached_property
  def 							lowercase						(							self					:						Dict	)				->		Optional[Any]:
     return (
         MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'	)
         if is_vision_available()
         else None
     )






  def 							lowercase						(							self					:						Optional[Any]	)				->		Tuple:
     __lowerCAmelCase				      =      MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'	).to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      self.default_image_processor
     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      inputs['pixel_values'].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
     # check size
     self.assertEqual(lowerCAmelCase_   ,    (1, 3, 8_0_0, 1_0_8_8)	)

     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.tensor(
         [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]	).to(lowerCAmelCase_	)
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)

     __lowerCAmelCase				      =      torch.tensor(
         [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]	).to(lowerCAmelCase_	)
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)

     __lowerCAmelCase				      =      torch.tensor(
         [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]	).to(lowerCAmelCase_	)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)






  def 							lowercase						(							self					:						List[str]	)				->		Optional[Any]:
     __lowerCAmelCase				      =      (
         MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'	)
         .to(lowerCAmelCase_	)
         .eval()
     )
     __lowerCAmelCase				      =      self.default_image_processor
     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      inputs['pixel_values'].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
     # check size
     self.assertEqual(lowerCAmelCase_   ,    (1, 3, 8_0_0, 1_0_8_8)	)

     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)
     # masks_queries_logits
     __lowerCAmelCase				      =      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				      =      [
         [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
         [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
         [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
     ]
     __lowerCAmelCase				      =      torch.tensor(lowerCAmelCase_	).to(lowerCAmelCase_	)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)
     # class_queries_logits
     __lowerCAmelCase				      =      outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape   ,    (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)	)
     __lowerCAmelCase				      =      torch.tensor(
         [
             [1.65_12e00, -5.25_72e00, -3.35_19e00],
             [3.61_69e-02, -5.90_25e00, -2.93_13e00],
             [1.07_66e-04, -7.76_30e00, -5.12_63e00],
         ]	).to(lowerCAmelCase_	)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)






  def 							lowercase						(							self					:						Optional[int]	)				->		str:
     __lowerCAmelCase				      =      (
         MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'	)
         .to(lowerCAmelCase_	)
         .eval()
     )
     __lowerCAmelCase				      =      self.default_image_processor
     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      inputs['pixel_values'].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
     # check size
     self.assertEqual(lowerCAmelCase_   ,    (1, 3, 8_0_0, 1_0_8_8)	)

     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)
     # masks_queries_logits
     __lowerCAmelCase				      =      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				      =      [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
     __lowerCAmelCase				      =      torch.tensor(lowerCAmelCase_	).to(lowerCAmelCase_	)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)
     # class_queries_logits
     __lowerCAmelCase				      =      outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape   ,    (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)	)
     __lowerCAmelCase				      =      torch.tensor(
         [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]	).to(lowerCAmelCase_	)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,    lowerCAmelCase_   ,    atol=lowerCAmelCase_	)	)






  def 							lowercase						(							self					:						Dict	)				->		Any:
     __lowerCAmelCase				      =      (
         MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'	)
         .to(lowerCAmelCase_	)
         .eval()
     )
     __lowerCAmelCase				      =      self.default_image_processor

     __lowerCAmelCase				      =      image_processor(
         [np.zeros((3, 8_0_0, 1_3_3_3)	), np.zeros((3, 8_0_0, 1_3_3_3)	)]   ,    segmentation_maps=[np.zeros((3_8_4, 3_8_4)	).astype(np.floataa	), np.zeros((3_8_4, 3_8_4)	).astype(np.floataa	)]   ,    return_tensors='pt'   ,    )

     __lowerCAmelCase				      =      inputs['pixel_values'].to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      [el.to(lowerCAmelCase_	) for el in inputs['mask_labels']]
     __lowerCAmelCase				      =      [el.to(lowerCAmelCase_	) for el in inputs['class_labels']]

     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)

     self.assertTrue(outputs.loss is not None	)









'''simple docstring'''



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

import datasets


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

__UpperCamelCase   :							Tuple         =   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__UpperCamelCase   :							Optional[int]         =   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str     )	->      List[Any]:

   """simple docstring"""
   return float((preds == labels).mean()     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     simple_accuracy(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     float(fa_score(y_true=SCREAMING_SNAKE_CASE__,       y_pred=SCREAMING_SNAKE_CASE__     )     )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      int:

   """simple docstring"""
   __a					  =     float(pearsonr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   __a					  =     float(spearmanr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }



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




      def 			__UpperCamelCase				(	self   )				->int:



         '''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 			__UpperCamelCase				(	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"]'   )