

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 						__magic_name__    :


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


 def _UpperCamelCase	(						self		: List[Any]    )    ->      str:
   UpperCAmelCase					      =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
       lowerCAmelCase__    )

   UpperCAmelCase					      =  torch.ones([self.batch_size, self.min_size, self.max_size]			,						device=lowerCAmelCase__    )

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

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


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Dict:
   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 _UpperCamelCase	(						self		: List[Any]    )    ->      Tuple:
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  self.prepare_config_and_inputs()
   UpperCAmelCase					      =  {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
   return config, inputs_dict


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: Union[str, Any]    )    ->      int:
   UpperCAmelCase					      =  output.encoder_hidden_states
   UpperCAmelCase					      =  output.pixel_decoder_hidden_states
   UpperCAmelCase					      =  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 _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Tuple=False    )    ->      Union[str, Any]:
   with torch.no_grad():
     UpperCAmelCase					      =  MaskFormerModel(config=lowerCAmelCase__    )
     model.to(lowerCAmelCase__    )
     model.eval()

     UpperCAmelCase					      =  model(pixel_values=lowerCAmelCase__			,						pixel_mask=lowerCAmelCase__    )
     UpperCAmelCase					      =  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 _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Optional[Any]    )    ->      Any:
   UpperCAmelCase					      =  MaskFormerForInstanceSegmentation(config=lowerCAmelCase__    )
   model.to(lowerCAmelCase__    )
   model.eval()

   def comm_check_on_output(lowerCAmelCase__		: Union[str, 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():
     UpperCAmelCase					      =  model(pixel_values=lowerCAmelCase__			,						pixel_mask=lowerCAmelCase__    )
     UpperCAmelCase					      =  model(lowerCAmelCase__    )

     comm_check_on_output(lowerCAmelCase__    )

     UpperCAmelCase					      =  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 						__magic_name__    (       _snake_case	,						_snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 UpperCAmelCase							    =						(
     {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False


 def _UpperCamelCase	(						self		: int    )    ->      Optional[Any]:
   UpperCAmelCase					      =  MaskFormerModelTester(self    )
   UpperCAmelCase					      =  ConfigTester(self			,						config_class=lowerCAmelCase__			,						has_text_modality=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Any    )    ->      Tuple:
   self.config_tester.run_common_tests()


 def _UpperCamelCase	(						self		: Tuple    )    ->      Optional[Any]:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.create_and_check_maskformer_model(lowerCAmelCase__			,						**lowerCAmelCase__			,						output_hidden_states=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      List[Any]:
   UpperCAmelCase					      =  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 _UpperCamelCase	(						self		: str    )    ->      Tuple:
   pass


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


 @unittest.skip(reason="MaskFormer is not a generative model"    )
 def _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   pass


 @unittest.skip(reason="MaskFormer does not use token embeddings"    )
 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[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 _UpperCamelCase	(						self		: str    )    ->      Dict:
   pass


 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Dict:
   pass


 def _UpperCamelCase	(						self		: int    )    ->      Union[str, Any]:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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


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


 def _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   UpperCAmelCase					      =  (self.model_tester.min_size,) * 2
   UpperCAmelCase					      =  {
       "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(),
   }

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


 def _UpperCamelCase	(						self		: Any    )    ->      List[Any]:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.create_and_check_maskformer_model(lowerCAmelCase__			,						**lowerCAmelCase__			,						output_hidden_states=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Dict:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

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


 def _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   if not self.model_tester.is_training:
     return
   # only MaskFormerForInstanceSegmentation has the loss
   UpperCAmelCase					      =  self.all_model_classes[1]
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()

   UpperCAmelCase					      =  model_class(lowerCAmelCase__    )
   model.to(lowerCAmelCase__    )
   model.train()

   UpperCAmelCase					      =  model(lowerCAmelCase__			,						mask_labels=lowerCAmelCase__			,						class_labels=lowerCAmelCase__    ).loss
   loss.backward()


 def _UpperCamelCase	(						self		: int    )    ->      Dict:
   # only MaskFormerForInstanceSegmentation has the loss
   UpperCAmelCase					      =  self.all_model_classes[1]
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   UpperCAmelCase					      =  True
   UpperCAmelCase					      =  True

   UpperCAmelCase					      =  model_class(lowerCAmelCase__    )
   model.to(lowerCAmelCase__    )
   model.train()

   UpperCAmelCase					      =  model(lowerCAmelCase__			,						mask_labels=lowerCAmelCase__			,						class_labels=lowerCAmelCase__    )

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

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

   UpperCAmelCase					      =  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    )

lowerCAmelCase__       = 1e-4
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  return image






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


 @cached_property
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Optional[int]:
   return (
       MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"    )
       if is_vision_available()
       else None
   )


 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   UpperCAmelCase					      =  MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  self.default_image_processor
   UpperCAmelCase					      =  prepare_img()
   UpperCAmelCase					      =  image_processor(lowerCAmelCase__			,						return_tensors="pt"    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  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():
     UpperCAmelCase					      =  model(**lowerCAmelCase__    )

   UpperCAmelCase					      =  torch.tensor(
       [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]    ).to(lowerCAmelCase__    )
   self.assertTrue(
       torch.allclose(
           outputs.encoder_last_hidden_state[0, 0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )

   UpperCAmelCase					      =  torch.tensor(
       [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]    ).to(lowerCAmelCase__    )
   self.assertTrue(
       torch.allclose(
           outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )

   UpperCAmelCase					      =  torch.tensor(
       [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]    ).to(lowerCAmelCase__    )
   self.assertTrue(
       torch.allclose(
           outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      List[str]:
   UpperCAmelCase					      =  (
       MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
       .to(lowerCAmelCase__    )
       .eval()
   )
   UpperCAmelCase					      =  self.default_image_processor
   UpperCAmelCase					      =  prepare_img()
   UpperCAmelCase					      =  image_processor(lowerCAmelCase__			,						return_tensors="pt"    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  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():
     UpperCAmelCase					      =  model(**lowerCAmelCase__    )
   # masks_queries_logits
   UpperCAmelCase					      =  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					      =  [
       [-1.3_737_124, -1.7_724_937, -1.9_364_233],
       [-1.5_977_281, -1.9_867_939, -2.1_523_695],
       [-1.5_795_398, -1.9_269_832, -2.093_942],
   ]
   UpperCAmelCase					      =  torch.tensor(lowerCAmelCase__    ).to(lowerCAmelCase__    )
   self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )
   # class_queries_logits
   UpperCAmelCase					      =  outputs.class_queries_logits
   self.assertEqual(
       class_queries_logits.shape			,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
   UpperCAmelCase					      =  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(lowerCAmelCase__    )
   self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Tuple:
   UpperCAmelCase					      =  (
       MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"    )
       .to(lowerCAmelCase__    )
       .eval()
   )
   UpperCAmelCase					      =  self.default_image_processor
   UpperCAmelCase					      =  prepare_img()
   UpperCAmelCase					      =  image_processor(lowerCAmelCase__			,						return_tensors="pt"    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  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():
     UpperCAmelCase					      =  model(**lowerCAmelCase__    )
   # masks_queries_logits
   UpperCAmelCase					      =  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					      =  [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
   UpperCAmelCase					      =  torch.tensor(lowerCAmelCase__    ).to(lowerCAmelCase__    )
   self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )
   # class_queries_logits
   UpperCAmelCase					      =  outputs.class_queries_logits
   self.assertEqual(
       class_queries_logits.shape			,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
   UpperCAmelCase					      =  torch.tensor(
       [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]    ).to(lowerCAmelCase__    )
   self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,						lowerCAmelCase__			,						atol=lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Dict    )    ->      List[Any]:
   UpperCAmelCase					      =  (
       MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
       .to(lowerCAmelCase__    )
       .eval()
   )
   UpperCAmelCase					      =  self.default_image_processor

   UpperCAmelCase					      =  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"			,						)

   UpperCAmelCase					      =  inputs["pixel_values"].to(lowerCAmelCase__    )
   UpperCAmelCase					      =  [el.to(lowerCAmelCase__    ) for el in inputs["mask_labels"]]
   UpperCAmelCase					      =  [el.to(lowerCAmelCase__    ) for el in inputs["class_labels"]]

   with torch.no_grad():
     UpperCAmelCase					      =  model(**lowerCAmelCase__    )

   self.assertTrue(outputs.loss is not None    )






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



