
import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image







class   lowerCamelCase	:





     def __init__(self   :					Optional[int]       ,					_A   :					List[Any]       ,					_A   :					Union[str, Any]=2       ,					_A   :					Optional[Any]=True       ,					_A   :					Optional[Any]=False       ,					_A   :					List[Any]=1_0       ,					_A   :					Any=3       ,					_A   :					List[Any]=3_2 * 8       ,					_A   :					Optional[Any]=3_2 * 8       ,					_A   :					Optional[Any]=4       ,					_A   :					Optional[Any]=6_4       ,					)		->		Optional[int]:
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      is_training
         snake_case				  =      use_auxiliary_loss
         snake_case				  =      num_queries
         snake_case				  =      num_channels
         snake_case				  =      min_size
         snake_case				  =      max_size
         snake_case				  =      num_labels
         snake_case				  =      hidden_dim
         snake_case				  =      hidden_dim





     def 	UpperCAmelCase(self   :					int     )		->		Optional[Any]:
         snake_case				  =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]     ).to(
             _A     )

         snake_case				  =      torch.ones([self.batch_size, self.min_size, self.max_size]       ,					device=_A     )

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

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





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Tuple:
         snake_case				  =      MaskaFormerConfig(
             hidden_size=self.hidden_dim       ,					)
         snake_case				  =      self.num_queries
         snake_case				  =      self.num_labels

         snake_case				  =      [1, 1, 1, 1]
         snake_case				  =      self.num_channels

         snake_case				  =      6_4
         snake_case				  =      1_2_8
         snake_case				  =      self.hidden_dim
         snake_case				  =      self.hidden_dim
         snake_case				  =      self.hidden_dim
         return config





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





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					List[Any]       ,					_A   :					List[str]     )		->		Union[str, Any]:
         snake_case				  =      output.encoder_hidden_states
         snake_case				  =      output.pixel_decoder_hidden_states
         snake_case				  =      output.transformer_decoder_hidden_states

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





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					Union[str, Any]       ,					_A   :					Dict       ,					_A   :					str       ,					_A   :					Tuple=False     )		->		Dict:
         with torch.no_grad():
             snake_case				  =      MaskaFormerModel(config=_A     )
             model.to(_A     )
             model.eval()

             snake_case				  =      model(pixel_values=_A       ,					pixel_mask=_A     )
             snake_case				  =      model(_A       ,					output_hidden_states=_A     )

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape       ,					(self.batch_size, self.num_queries, self.hidden_dim)       ,					)
         # 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(_A       ,					_A     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					Union[str, Any]       ,					_A   :					Optional[int]       ,					_A   :					int       ,					_A   :					Optional[Any]       ,					_A   :					Tuple     )		->		List[Any]:
         snake_case				  =      MaskaFormerForUniversalSegmentation(config=_A     )
         model.to(_A     )
         model.eval()

         def comm_check_on_output(_A   :					List[str]     ):
             # 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():
             snake_case				  =      model(pixel_values=_A       ,					pixel_mask=_A     )
             snake_case				  =      model(_A     )

             comm_check_on_output(_A     )

             snake_case				  =      model(
                 pixel_values=_A       ,					pixel_mask=_A       ,					mask_labels=_A       ,					class_labels=_A     )

         comm_check_on_output(_A     )

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







@require_torch
class   lowerCamelCase	(						A_							,				A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Dict     =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
     UpperCAmelCase__       :							int     =	{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

     UpperCAmelCase__       :							List[str]     =	False
     UpperCAmelCase__       :							Tuple     =	False
     UpperCAmelCase__       :							Dict     =	False
     UpperCAmelCase__       :							Optional[int]     =	False





     def 	UpperCAmelCase(self   :					str     )		->		List[str]:
         snake_case				  =      MaskaFormerModelTester(self     )
         snake_case				  =      ConfigTester(self       ,					config_class=_A       ,					has_text_modality=_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		List[str]:
         self.config_tester.run_common_tests()





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[Any]:
         snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(_A       ,					**_A       ,					output_hidden_states=_A     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_A     )





     @unittest.skip(reason="Mask2Former does not use inputs_embeds"     )
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[Any]:
         pass





     @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"     )
     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         pass





     @unittest.skip(reason="Mask2Former is not a generative model"     )
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Union[str, Any]:
         pass





     @unittest.skip(reason="Mask2Former does not use token embeddings"     )
     def 	UpperCAmelCase(self   :					List[Any]     )		->		Any:
         pass





     @require_torch_multi_gpu
     @unittest.skip(
         reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"     )
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[Any]:
         pass





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





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[Any]:
         snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case				  =      ["pixel_values"]
             self.assertListEqual(arg_names[:1]       ,					_A     )





     @slow
     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             snake_case				  =      MaskaFormerModel.from_pretrained(_A     )
             self.assertIsNotNone(_A     )





     def 	UpperCAmelCase(self   :					Any     )		->		int:
         snake_case				  =      (self.model_tester.min_size,) * 2
         snake_case				  =      {
             "pixel_values": torch.randn((2, 3, *size)       ,					device=_A     ),
             "mask_labels": torch.randn((2, 1_0, *size)       ,					device=_A     ),
             "class_labels": torch.zeros(2       ,					1_0       ,					device=_A     ).long(),
         }
         snake_case				  =      self.model_tester.get_config()

         snake_case				  =      MaskaFormerForUniversalSegmentation(_A     ).to(_A     )
         snake_case				  =      model(**_A     )
         self.assertTrue(outputs.loss is not None     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(_A       ,					**_A       ,					output_hidden_states=_A     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		Optional[int]:
         snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()

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





     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         if not self.model_tester.is_training:
             return

         snake_case				  =      self.all_model_classes[1]
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs()

         snake_case				  =      model_class(_A     )
         model.to(_A     )
         model.train()

         snake_case				  =      model(_A       ,					mask_labels=_A       ,					class_labels=_A     ).loss
         loss.backward()





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[int]:
         snake_case				  =      self.all_model_classes[1]
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs()
         snake_case				  =      True
         snake_case				  =      True

         snake_case				  =      model_class(_A     ).to(_A     )
         model.train()

         snake_case				  =      model(_A       ,					mask_labels=_A       ,					class_labels=_A     )

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

         snake_case				  =      outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

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

         snake_case				  =      outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=_A     )

         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						      =				1e-4



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
    return image







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





     @cached_property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         return "facebook/mask2former-swin-small-coco-instance"





     @cached_property
     def 	UpperCAmelCase(self   :					Any     )		->		Optional[int]:
         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints     ) if is_vision_available() else None





     def 	UpperCAmelCase(self   :					str     )		->		Tuple:
         snake_case				  =      MaskaFormerModel.from_pretrained(self.model_checkpoints     ).to(_A     )
         snake_case				  =      self.default_image_processor
         snake_case				  =      prepare_img()
         snake_case				  =      image_processor(_A       ,					return_tensors="pt"     ).to(_A     )
         snake_case				  =      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(_A       ,					(1, 3, 3_8_4, 3_8_4)     )

         with torch.no_grad():
             snake_case				  =      model(**_A     )

         snake_case				  =      torch.tensor(
             [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]     ).to(_A     )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,					_A       ,					atol=_A     )     )

         snake_case				  =      torch.tensor(
             [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]     ).to(_A     )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,					_A       ,					atol=_A     )     )

         snake_case				  =      torch.tensor(
             [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]     ).to(_A     )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,					_A       ,					atol=_A     )     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         snake_case				  =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(_A     ).eval()
         snake_case				  =      self.default_image_processor
         snake_case				  =      prepare_img()
         snake_case				  =      image_processor(_A       ,					return_tensors="pt"     ).to(_A     )
         snake_case				  =      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(_A       ,					(1, 3, 3_8_4, 3_8_4)     )

         with torch.no_grad():
             snake_case				  =      model(**_A     )
         # masks_queries_logits
         snake_case				  =      outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape       ,					(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     )
         snake_case				  =      [
             [-8.78_39, -9.00_56, -8.81_21],
             [-7.41_04, -7.03_13, -6.54_01],
             [-6.61_05, -6.34_27, -6.46_75],
         ]
         snake_case				  =      torch.tensor(_A     ).to(_A     )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,					_A       ,					atol=_A     )     )
         # class_queries_logits
         snake_case				  =      outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape       ,					(1, model.config.num_queries, model.config.num_labels + 1)     )
         snake_case				  =      torch.tensor(
             [
                 [1.83_24, -8.08_35, -4.19_22],
                 [0.84_50, -9.00_50, -3.60_53],
                 [0.30_45, -7.72_93, -3.02_75],
             ]     ).to(_A     )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,					_A       ,					atol=_A     )     )





     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(_A     ).eval()
         snake_case				  =      self.default_image_processor

         snake_case				  =      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"       ,					)

         snake_case				  =      inputs["pixel_values"].to(_A     )
         snake_case				  =      [el.to(_A     ) for el in inputs["mask_labels"]]
         snake_case				  =      [el.to(_A     ) for el in inputs["class_labels"]]

         with torch.no_grad():
             snake_case				  =      model(**_A     )

         self.assertTrue(outputs.loss is not None     )



def 		lowercase_    (     A__ = 1000	)							->							int:
    """simple docstring"""

    snake_case				  =      -1
    snake_case				  =      0
    for a in range(1				,	n // 3	):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        snake_case				  =      (n * n - 2 * a * n) // (2 * n - 2 * a)
        snake_case				  =      n - a - b
        if c * c == (a * a + b * b):
            snake_case				  =      a * b * c
            if candidate >= product:
                snake_case				  =      candidate
    return product


if __name__ == "__main__":
   print(f"{solution() = }")

