



'''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 							a__:







    def __init__(      self :  str						,			__snake_case :  Tuple						,			__snake_case :  List[str]=2						,			__snake_case :  Any=True						,			__snake_case :  Optional[int]=False						,			__snake_case :  int=10						,			__snake_case :  List[Any]=3						,			__snake_case :  List[str]=32 * 4						,			__snake_case :  Union[str, Any]=32 * 6						,			__snake_case :  Optional[Any]=4						,			__snake_case :  Dict=32						,			):
      a						:					List[str]        =     parent
      a						:					List[Any]        =     batch_size
      a						:					str        =     is_training
      a						:					List[Any]        =     use_auxiliary_loss
      a						:					int        =     num_queries
      a						:					Optional[Any]        =     num_channels
      a						:					int        =     min_size
      a						:					Union[str, Any]        =     max_size
      a						:					Tuple        =     num_labels
      a						:					Tuple        =     mask_feature_size







    def 			lowercase_       (      self :  Any	):
      a						:					Tuple        =     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
          __lowercase	)

      a						:					Optional[int]        =     torch.ones([self.batch_size, self.min_size, self.max_size]						,			device=__lowercase	)

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

      a						:					Dict        =     self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels







    def 			lowercase_       (      self :  int	):
      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 			lowercase_       (      self :  List[str]	):
      a						:					List[Any]        =     self.prepare_config_and_inputs()
      a						:					Any        =     {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict







    def 			lowercase_       (      self :  Optional[Any]						,			__snake_case :  Any						,			__snake_case :  List[Any]	):
      a						:					List[Any]        =     output.encoder_hidden_states
      a						:					List[Any]        =     output.pixel_decoder_hidden_states
      a						:					Any        =     output.transformer_decoder_hidden_states

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







    def 			lowercase_       (      self :  Tuple						,			__snake_case :  str						,			__snake_case :  str						,			__snake_case :  Optional[Any]						,			__snake_case :  Union[str, Any]=False	):
      with torch.no_grad():
        a						:					str        =     MaskFormerModel(config=__lowercase	)
        model.to(__lowercase	)
        model.eval()

        a						:					Any        =     model(pixel_values=__lowercase						,			pixel_mask=__lowercase	)
        a						:					int        =     model(__lowercase						,			output_hidden_states=__lowercase	)
      # 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(__lowercase						,			__lowercase	)







    def 			lowercase_       (      self :  List[Any]						,			__snake_case :  Optional[Any]						,			__snake_case :  Optional[Any]						,			__snake_case :  Dict						,			__snake_case :  int						,			__snake_case :  Optional[Any]	):
      a						:					List[str]        =     MaskFormerForInstanceSegmentation(config=__lowercase	)
      model.to(__lowercase	)
      model.eval()

      def comm_check_on_output(__snake_case :  Any	):
        # 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():
        a						:					Optional[int]        =     model(pixel_values=__lowercase						,			pixel_mask=__lowercase	)
        a						:					Union[str, Any]        =     model(__lowercase	)

        comm_check_on_output(__lowercase	)

        a						:					Any        =     model(
            pixel_values=__lowercase						,			pixel_mask=__lowercase						,			mask_labels=__lowercase						,			class_labels=__lowercase	)

      comm_check_on_output(__lowercase	)

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

@require_torch
class 							a__(					lowercase__		, lowercase__		, unittest.TestCase	):
    lowercase__ =    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    lowercase__ =    (
        {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    lowercase__ =    False
    lowercase__ =    False
    lowercase__ =    False
    lowercase__ =    False







    def 			lowercase_       (      self :  int	):
      a						:					int        =     MaskFormerModelTester(self	)
      a						:					str        =     ConfigTester(self						,			config_class=__lowercase						,			has_text_modality=__lowercase	)







    def 			lowercase_       (      self :  Dict	):
      self.config_tester.run_common_tests()







    def 			lowercase_       (      self :  Dict	):
      a						:					Dict        =     self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__lowercase						,			**__lowercase						,			output_hidden_states=__lowercase	)







    def 			lowercase_       (      self :  Union[str, Any]	):
      a						:					Union[str, Any]        =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowercase	)







    @unittest.skip(reason='MaskFormer does not use inputs_embeds'	)
    def 			lowercase_       (      self :  Optional[int]	):
      pass







    @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'	)
    def 			lowercase_       (      self :  List[Any]	):
      pass







    @unittest.skip(reason='MaskFormer is not a generative model'	)
    def 			lowercase_       (      self :  int	):
      pass







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







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
    def 			lowercase_       (      self :  Any	):
      pass







    def 			lowercase_       (      self :  List[Any]	):
      a						:					List[Any]        =     self.model_tester.prepare_config_and_inputs_for_common()

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

        a						:					str        =     ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]						,			__lowercase	)







    @slow
    def 			lowercase_       (      self :  int	):
      for model_name in ["facebook/maskformer-swin-small-coco"]:
        a						:					int        =     MaskFormerModel.from_pretrained(__lowercase	)
        self.assertIsNotNone(__lowercase	)







    def 			lowercase_       (      self :  Optional[int]	):
      a						:					Tuple        =     (self.model_tester.min_size,) * 2
      a						:					str        =     {
          """pixel_values""": torch.randn((2, 3, *size)						,			device=__lowercase	),
          """mask_labels""": torch.randn((2, 10, *size)						,			device=__lowercase	),
          """class_labels""": torch.zeros(2						,			10						,			device=__lowercase	).long(),
      }

      a						:					Dict        =     MaskFormerForInstanceSegmentation(MaskFormerConfig()	).to(__lowercase	)
      a						:					Any        =     model(**__lowercase	)
      self.assertTrue(outputs.loss is not None	)







    def 			lowercase_       (      self :  Union[str, Any]	):
      a						:					Tuple        =     self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__lowercase						,			**__lowercase						,			output_hidden_states=__lowercase	)







    def 			lowercase_       (      self :  Any	):
      a						:					Optional[int]        =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						:					Tuple        =     model_class(__lowercase	).to(__lowercase	)
        a						:					Union[str, Any]        =     model(**__lowercase						,			output_attentions=__lowercase	)
        self.assertTrue(outputs.attentions is not None	)







    def 			lowercase_       (      self :  Dict	):
      if not self.model_tester.is_training:
        return
      # only MaskFormerForInstanceSegmentation has the loss
      a						:					str        =     self.all_model_classes[1]
      a						:					Optional[int]        =     self.model_tester.prepare_config_and_inputs()

      a						:					str        =     model_class(__lowercase	)
      model.to(__lowercase	)
      model.train()

      a						:					Union[str, Any]        =     model(__lowercase						,			mask_labels=__lowercase						,			class_labels=__lowercase	).loss
      loss.backward()







    def 			lowercase_       (      self :  int	):
      # only MaskFormerForInstanceSegmentation has the loss
      a						:					Optional[Any]        =     self.all_model_classes[1]
      a						:					List[Any]        =     self.model_tester.prepare_config_and_inputs()
      a						:					int        =     True
      a						:					Optional[Any]        =     True

      a						:					Union[str, Any]        =     model_class(__lowercase	)
      model.to(__lowercase	)
      model.train()

      a						:					Optional[Any]        =     model(__lowercase						,			mask_labels=__lowercase						,			class_labels=__lowercase	)

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

      a						:					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
      a						:					List[Any]        =     outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      a						:					Tuple        =     outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=__lowercase	)

      self.assertIsNotNone(encoder_hidden_states.grad	)
      self.assertIsNotNone(pixel_decoder_hidden_states.grad	)
      self.assertIsNotNone(transformer_decoder_hidden_states.grad	)
      self.assertIsNotNone(attentions.grad	)
lowerCAmelCase:					str						       =						1e-4
def       lowerCamelCase__       (						):
  a						:					Union[str, Any]        =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
  return image

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







    @cached_property
    def 			lowercase_       (      self :  List[str]	):
      return (
          MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'	)
          if is_vision_available()
          else None
      )







    def 			lowercase_       (      self :  Optional[int]	):
      a						:					Optional[int]        =     MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'	).to(__lowercase	)
      a						:					Optional[Any]        =     self.default_image_processor
      a						:					Optional[int]        =     prepare_img()
      a						:					Union[str, Any]        =     image_processor(__lowercase						,			return_tensors='pt'	).to(__lowercase	)
      a						:					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(__lowercase						,			(1, 3, 8_00, 10_88)	)

      with torch.no_grad():
        a						:					Union[str, Any]        =     model(**__lowercase	)

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

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

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







    def 			lowercase_       (      self :  List[str]	):
      a						:					Tuple        =     (
          MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'	)
          .to(__lowercase	)
          .eval()
      )
      a						:					List[Any]        =     self.default_image_processor
      a						:					Union[str, Any]        =     prepare_img()
      a						:					List[Any]        =     image_processor(__lowercase						,			return_tensors='pt'	).to(__lowercase	)
      a						:					List[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(__lowercase						,			(1, 3, 8_00, 10_88)	)

      with torch.no_grad():
        a						:					List[Any]        =     model(**__lowercase	)
      # masks_queries_logits
      a						:					str        =     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)						,			)
      a						:					Tuple        =     [
          [-1.3737124, -1.7724937, -1.9364233],
          [-1.5977281, -1.9867939, -2.1523695],
          [-1.5795398, -1.9269832, -2.093942],
      ]
      a						:					Union[str, Any]        =     torch.tensor(__lowercase	).to(__lowercase	)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,			__lowercase						,			atol=__lowercase	)	)
      # class_queries_logits
      a						:					Optional[int]        =     outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape						,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)	)
      a						:					List[str]        =     torch.tensor(
          [
              [1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
              [3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
              [1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
          ]	).to(__lowercase	)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,			__lowercase						,			atol=__lowercase	)	)







    def 			lowercase_       (      self :  List[str]	):
      a						:					Optional[Any]        =     (
          MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'	)
          .to(__lowercase	)
          .eval()
      )
      a						:					List[str]        =     self.default_image_processor
      a						:					Any        =     prepare_img()
      a						:					List[str]        =     image_processor(__lowercase						,			return_tensors='pt'	).to(__lowercase	)
      a						:					List[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(__lowercase						,			(1, 3, 8_00, 10_88)	)

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







    def 			lowercase_       (      self :  List[str]	):
      a						:					Any        =     (
          MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'	)
          .to(__lowercase	)
          .eval()
      )
      a						:					Dict        =     self.default_image_processor

      a						:					Optional[int]        =     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'						,			)

      a						:					str        =     inputs["""pixel_values"""].to(__lowercase	)
      a						:					Dict        =     [el.to(__lowercase	) for el in inputs["""mask_labels"""]]
      a						:					str        =     [el.to(__lowercase	) for el in inputs["""class_labels"""]]

      with torch.no_grad():
        a						:					str        =     model(**__lowercase	)

      self.assertTrue(outputs.loss is not None	)







from functools import lru_cache






@lru_cache
def 				lowerCamelCase__      (    __lowerCamelCase			:     int		):
  if num < 0:
    raise ValueError("""Number should not be negative."""		)

  return 1 if num in (0, 1) else num * factorial(num - 1		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


