



"""simple docstring"""
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       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=10      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=32 * 4      ,     _SCREAMING_SNAKE_CASE=32 * 6      ,     _SCREAMING_SNAKE_CASE=4      ,     _SCREAMING_SNAKE_CASE=32      ,     )  ->      Optional[int]:





    '''simple docstring'''




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

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





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
        _SCREAMING_SNAKE_CASE						)

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

    UpperCAmelCase	:					str	=	(
        torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,     device=_SCREAMING_SNAKE_CASE						) > 0.5
    ).float()
    UpperCAmelCase	:					Optional[Any]	=	(torch.rand((self.batch_size, self.num_labels)      ,     device=_SCREAMING_SNAKE_CASE						) > 0.5).long()

    UpperCAmelCase	:					List[str]	=	self.get_config()
    return config, pixel_values, pixel_mask, mask_labels, class_labels

   def 	SCREAMING_SNAKE_CASE     (   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=128      ,     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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	self.prepare_config_and_inputs()
    UpperCAmelCase	:					str	=	{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
    return config, inputs_dict

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	output.encoder_hidden_states
    UpperCAmelCase	:					List[Any]	=	output.pixel_decoder_hidden_states
    UpperCAmelCase	:					List[str]	=	output.transformer_decoder_hidden_states

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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Tuple:





    '''simple docstring'''




    with torch.no_grad():
     UpperCAmelCase	:					List[Any]	=	MaskFormerModel(config=_SCREAMING_SNAKE_CASE						)
     model.to(_SCREAMING_SNAKE_CASE						)
     model.eval()

     UpperCAmelCase	:					Tuple	=	model(pixel_values=_SCREAMING_SNAKE_CASE      ,     pixel_mask=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Tuple	=	model(_SCREAMING_SNAKE_CASE      ,     output_hidden_states=_SCREAMING_SNAKE_CASE						)
    # 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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	MaskFormerForInstanceSegmentation(config=_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    model.eval()

    def comm_check_on_output(_SCREAMING_SNAKE_CASE						):
     # 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():
     UpperCAmelCase	:					Tuple	=	model(pixel_values=_SCREAMING_SNAKE_CASE      ,     pixel_mask=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Tuple	=	model(_SCREAMING_SNAKE_CASE						)

     comm_check_on_output(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					List[Any]	=	model(
         pixel_values=_SCREAMING_SNAKE_CASE      ,     pixel_mask=_SCREAMING_SNAKE_CASE      ,     mask_labels=_SCREAMING_SNAKE_CASE      ,     class_labels=_SCREAMING_SNAKE_CASE						)

    comm_check_on_output(_SCREAMING_SNAKE_CASE						)

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



@require_torch
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Optional[int]						=						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   __lowerCAmelCase  :   Optional[int]						=						(
       {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase  :   Optional[int]						=						False
   __lowerCAmelCase  :   Union[str, Any]						=						False
   __lowerCAmelCase  :   Union[str, Any]						=						False
   __lowerCAmelCase  :   Dict						=						False

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





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	MaskFormerModelTester(self						)
    UpperCAmelCase	:					str	=	ConfigTester(self      ,     config_class=_SCREAMING_SNAKE_CASE      ,     has_text_modality=_SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    self.config_tester.run_common_tests()

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





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskformer_model(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     output_hidden_states=_SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    pass

   @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    pass

   @unittest.skip(reason="""MaskFormer is not a generative model"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    pass

   @unittest.skip(reason="""MaskFormer does not use token embeddings"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    pass

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





    '''simple docstring'''




    pass

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





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Any	=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Tuple	=	model_class(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	inspect.signature(model.forward						)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCAmelCase	:					List[str]	=	[*signature.parameters.keys()]

     UpperCAmelCase	:					Optional[Any]	=	["""pixel_values"""]
     self.assertListEqual(arg_names[:1]      ,     _SCREAMING_SNAKE_CASE						)

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




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

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Any	=	self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskformer_model(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     output_hidden_states=_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					List[str]	=	model_class(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					List[Any]	=	model(**_SCREAMING_SNAKE_CASE      ,     output_attentions=_SCREAMING_SNAKE_CASE						)
     self.assertTrue(outputs.attentions is not None						)

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





    '''simple docstring'''




    if not self.model_tester.is_training:
     return
    # only MaskFormerForInstanceSegmentation has the loss
    UpperCAmelCase	:					Tuple	=	self.all_model_classes[1]
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					str	=	self.model_tester.prepare_config_and_inputs()

    UpperCAmelCase	:					int	=	model_class(_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    model.train()

    UpperCAmelCase	:					Dict	=	model(_SCREAMING_SNAKE_CASE      ,     mask_labels=_SCREAMING_SNAKE_CASE      ,     class_labels=_SCREAMING_SNAKE_CASE						).loss
    loss.backward()




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





    '''simple docstring'''




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

    UpperCAmelCase	:					List[Any]	=	model_class(_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    model.train()

    UpperCAmelCase	:					Tuple	=	model(_SCREAMING_SNAKE_CASE      ,     mask_labels=_SCREAMING_SNAKE_CASE      ,     class_labels=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	outputs.encoder_hidden_states[0]
    encoder_hidden_states.retain_grad()

    UpperCAmelCase	:					str	=	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
    UpperCAmelCase	:					Dict	=	outputs.transformer_decoder_hidden_states[0]
    transformer_decoder_hidden_states.retain_grad()

    UpperCAmelCase	:					Optional[int]	=	outputs.attentions[0]
    attentions.retain_grad()

    outputs.loss.backward(retain_graph=_SCREAMING_SNAKE_CASE						)

    self.assertIsNotNone(encoder_hidden_states.grad						)
    self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
    self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
    self.assertIsNotNone(attentions.grad						)
A:   Optional[Any]								=		1E-4

def       _snake_case						(						):
 UpperCAmelCase	:					Tuple	=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
 return image



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

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	self.default_image_processor
    UpperCAmelCase	:					List[Any]	=	prepare_img()
    UpperCAmelCase	:					str	=	image_processor(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	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(_SCREAMING_SNAKE_CASE      ,     (1, 3, 800, 1088)						)

    with torch.no_grad():
     UpperCAmelCase	:					Optional[Any]	=	model(**_SCREAMING_SNAKE_CASE						)

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

    UpperCAmelCase	:					Tuple	=	torch.tensor(
        [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]						).to(_SCREAMING_SNAKE_CASE						)
    self.assertTrue(
        torch.allclose(
            outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,     _SCREAMING_SNAKE_CASE      ,     atol=_SCREAMING_SNAKE_CASE						)						)

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	(
        MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
        .to(_SCREAMING_SNAKE_CASE						)
        .eval()
    )
    UpperCAmelCase	:					List[Any]	=	self.default_image_processor
    UpperCAmelCase	:					List[str]	=	prepare_img()
    UpperCAmelCase	:					Optional[Any]	=	image_processor(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					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(_SCREAMING_SNAKE_CASE      ,     (1, 3, 800, 1088)						)

    with torch.no_grad():
     UpperCAmelCase	:					List[str]	=	model(**_SCREAMING_SNAKE_CASE						)
    # masks_queries_logits
    UpperCAmelCase	:					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)      ,     )
    UpperCAmelCase	:					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],
    ]
    UpperCAmelCase	:					int	=	torch.tensor(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,     _SCREAMING_SNAKE_CASE      ,     atol=_SCREAMING_SNAKE_CASE						)						)
    # class_queries_logits
    UpperCAmelCase	:					Dict	=	outputs.class_queries_logits
    self.assertEqual(
        class_queries_logits.shape      ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
    UpperCAmelCase	:					Tuple	=	torch.tensor(
        [
            [1.6512E00, -5.2572E00, -3.3519E00],
            [3.6169E-02, -5.9025E00, -2.9313E00],
            [1.0766E-04, -7.7630E00, -5.1263E00],
        ]						).to(_SCREAMING_SNAKE_CASE						)
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,     _SCREAMING_SNAKE_CASE      ,     atol=_SCREAMING_SNAKE_CASE						)						)

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





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	(
        MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""						)
        .to(_SCREAMING_SNAKE_CASE						)
        .eval()
    )
    UpperCAmelCase	:					Optional[Any]	=	self.default_image_processor
    UpperCAmelCase	:					int	=	prepare_img()
    UpperCAmelCase	:					Union[str, Any]	=	image_processor(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					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(_SCREAMING_SNAKE_CASE      ,     (1, 3, 800, 1088)						)

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




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	(
        MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
        .to(_SCREAMING_SNAKE_CASE						)
        .eval()
    )
    UpperCAmelCase	:					Union[str, Any]	=	self.default_image_processor

    UpperCAmelCase	:					Union[str, Any]	=	image_processor(
        [np.zeros((3, 800, 1333)						), np.zeros((3, 800, 1333)						)]      ,     segmentation_maps=[np.zeros((384, 384)						).astype(np.floataa						), np.zeros((384, 384)						).astype(np.floataa						)]      ,     return_tensors="""pt"""      ,     )

    UpperCAmelCase	:					str	=	inputs["""pixel_values"""].to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	[el.to(_SCREAMING_SNAKE_CASE						) for el in inputs["""mask_labels"""]]
    UpperCAmelCase	:					int	=	[el.to(_SCREAMING_SNAKE_CASE						) for el in inputs["""class_labels"""]]

    with torch.no_grad():
     UpperCAmelCase	:					str	=	model(**_SCREAMING_SNAKE_CASE						)

    self.assertTrue(outputs.loss is not None						)





"""simple docstring"""
import datasets

from .evaluate import evaluate


A:   Optional[Any]								=		"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A:   Optional[int]								=		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A:   int								=		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"



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

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





    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION      ,     citation=_CITATION      ,     inputs_description=_KWARGS_DESCRIPTION      ,     features=datasets.Features(
            {
                """predictions""": {
                    """id""": datasets.Value("""string"""						),
                    """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""						)						),
                },
                """references""": {
                    """id""": datasets.Value("""string"""						),
                    """answers""": datasets.features.Sequence(
                        {
                            """text""": datasets.Value("""string"""						),
                            """answer_start""": datasets.Value("""int32"""						),
                        }						),
                },
            }						)      ,     codebase_urls=["""https://www.atticusprojectai.org/cuad"""]      ,     reference_urls=["""https://www.atticusprojectai.org/cuad"""]      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
    UpperCAmelCase	:					Tuple	=	[
        {
            """paragraphs""": [
                {
                    """qas""": [
                        {
                            """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                            """id""": ref["""id"""],
                        }
                        for ref in references
                    ]
                }
            ]
        }
    ]
    UpperCAmelCase	:					Optional[Any]	=	evaluate(dataset=_SCREAMING_SNAKE_CASE      ,     predictions=_SCREAMING_SNAKE_CASE						)
    return score
