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  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=2				,				A_=True				,				A_=False				,				A_=10				,				A_=3				,				A_=32 * 8				,				A_=32 * 8				,				A_=4				,				A_=64				,				) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				is_training
           _lowerCamelCase			    =				use_auxiliary_loss
           _lowerCamelCase			    =				num_queries
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				min_size
           _lowerCamelCase			    =				max_size
           _lowerCamelCase			    =				num_labels
           _lowerCamelCase			    =				hidden_dim
           _lowerCamelCase			    =				hidden_dim



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
               UpperCamelCase__						)

           _lowerCamelCase			    =				torch.ones([self.batch_size, self.min_size, self.max_size]				,				device=UpperCamelCase__						)

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

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



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				MaskaFormerConfig(
               hidden_size=self.hidden_dim				,				)
           _lowerCamelCase			    =				self.num_queries
           _lowerCamelCase			    =				self.num_labels

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

           _lowerCamelCase			    =				64
           _lowerCamelCase			    =				1_28
           _lowerCamelCase			    =				self.hidden_dim
           _lowerCamelCase			    =				self.hidden_dim
           _lowerCamelCase			    =				self.hidden_dim
           return config



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.prepare_config_and_inputs()
           _lowerCamelCase			    =				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
           return config, inputs_dict



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				output.encoder_hidden_states
           _lowerCamelCase			    =				output.pixel_decoder_hidden_states
           _lowerCamelCase			    =				output.transformer_decoder_hidden_states

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_=False						) ->				Dict:






           """simple docstring"""





           with torch.no_grad():
                 _lowerCamelCase			    =				MaskaFormerModel(config=UpperCamelCase__						)
                 model.to(UpperCamelCase__						)
                 model.eval()

                 _lowerCamelCase			    =				model(pixel_values=UpperCamelCase__				,				pixel_mask=UpperCamelCase__						)
                 _lowerCamelCase			    =				model(UpperCamelCase__				,				output_hidden_states=UpperCamelCase__						)

           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(UpperCamelCase__				,				UpperCamelCase__						)





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				MaskaFormerForUniversalSegmentation(config=UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.eval()

           def comm_check_on_output(A_						):
                 # let's still check that all the required stuff is there
                 self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
                 self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
                 self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
                 # okay, now we need to check the logits shape
                 # due to the encoder compression, masks have a //4 spatial size
                 self.parent.assertEqual(
                     result.masks_queries_logits.shape				,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)				,				)
                 # + 1 for null class
                 self.parent.assertEqual(
                     result.class_queries_logits.shape				,				(self.batch_size, self.num_queries, self.num_labels + 1)						)

           with torch.no_grad():
                 _lowerCamelCase			    =				model(pixel_values=UpperCamelCase__				,				pixel_mask=UpperCamelCase__						)
                 _lowerCamelCase			    =				model(UpperCamelCase__						)

                 comm_check_on_output(UpperCamelCase__						)

                 _lowerCamelCase			    =				model(
                     pixel_values=UpperCamelCase__				,				pixel_mask=UpperCamelCase__				,				mask_labels=UpperCamelCase__				,				class_labels=UpperCamelCase__						)

           comm_check_on_output(UpperCamelCase__						)

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





@require_torch
class  UpperCamelCase				( __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
     A_										=  {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

     A_										=  False
     A_										=  False
     A_										=  False
     A_										=  False



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				MaskaFormerModelTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=UpperCamelCase__				,				has_text_modality=UpperCamelCase__						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           self.config_tester.run_common_tests()



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






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__				,				**UpperCamelCase__				,				output_hidden_states=UpperCamelCase__						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCamelCase__						)



     @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''						)
     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           pass



     @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''						)
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           pass



     @unittest.skip(reason='''Mask2Former is not a generative model'''						)
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           pass



     @unittest.skip(reason='''Mask2Former does not use token embeddings'''						)
     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           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						) ->				Dict:






           """simple docstring"""
           pass



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






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 _lowerCamelCase			    =				['''pixel_values''']
                 self.assertListEqual(arg_names[:1]				,				UpperCamelCase__						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""





           for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                 _lowerCamelCase			    =				MaskaFormerModel.from_pretrained(UpperCamelCase__						)
                 self.assertIsNotNone(UpperCamelCase__						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				(self.model_tester.min_size,) * 2
           _lowerCamelCase			    =				{
               '''pixel_values''': torch.randn((2, 3, *size)				,				device=UpperCamelCase__						),
               '''mask_labels''': torch.randn((2, 10, *size)				,				device=UpperCamelCase__						),
               '''class_labels''': torch.zeros(2				,				10				,				device=UpperCamelCase__						).long(),
           }
           _lowerCamelCase			    =				self.model_tester.get_config()

           _lowerCamelCase			    =				MaskaFormerForUniversalSegmentation(UpperCamelCase__						).to(UpperCamelCase__						)
           _lowerCamelCase			    =				model(**UpperCamelCase__						)
           self.assertTrue(outputs.loss is not None						)



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






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__				,				**UpperCamelCase__				,				output_hidden_states=UpperCamelCase__						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

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



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""





           if not self.model_tester.is_training:
                 return

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

           _lowerCamelCase			    =				model_class(UpperCamelCase__						)
           model.to(UpperCamelCase__						)
           model.train()

           _lowerCamelCase			    =				model(UpperCamelCase__				,				mask_labels=UpperCamelCase__				,				class_labels=UpperCamelCase__						).loss
           loss.backward()





     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.all_model_classes[1]
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				True

           _lowerCamelCase			    =				model_class(UpperCamelCase__						).to(UpperCamelCase__						)
           model.train()

           _lowerCamelCase			    =				model(UpperCamelCase__				,				mask_labels=UpperCamelCase__				,				class_labels=UpperCamelCase__						)

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

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

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

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

           outputs.loss.backward(retain_graph=UpperCamelCase__						)

           self.assertIsNotNone(encoder_hidden_states.grad						)
           self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
           self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
           self.assertIsNotNone(attentions.grad						)





snake_case__				= 1E-4
def       __magic_name__(  ):



      '''simple docstring'''

      _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image




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

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           return "facebook/mask2former-swin-small-coco-instance"



     @cached_property
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(UpperCamelCase__						)
           _lowerCamelCase			    =				self.default_image_processor
           _lowerCamelCase			    =				prepare_img()
           _lowerCamelCase			    =				image_processor(UpperCamelCase__				,				return_tensors='''pt'''						).to(UpperCamelCase__						)
           _lowerCamelCase			    =				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(UpperCamelCase__				,				(1, 3, 3_84, 3_84)						)

           with torch.no_grad():
                 _lowerCamelCase			    =				model(**UpperCamelCase__						)

           _lowerCamelCase			    =				torch.tensor(
               [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]						).to(UpperCamelCase__						)
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]				,				UpperCamelCase__				,				atol=UpperCamelCase__						)						)

           _lowerCamelCase			    =				torch.tensor(
               [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]						).to(UpperCamelCase__						)
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,				UpperCamelCase__				,				atol=UpperCamelCase__						)						)

           _lowerCamelCase			    =				torch.tensor(
               [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]						).to(UpperCamelCase__						)
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,				UpperCamelCase__				,				atol=UpperCamelCase__						)						)



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






           """simple docstring"""
           _lowerCamelCase			    =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(UpperCamelCase__						).eval()
           _lowerCamelCase			    =				self.default_image_processor
           _lowerCamelCase			    =				prepare_img()
           _lowerCamelCase			    =				image_processor(UpperCamelCase__				,				return_tensors='''pt'''						).to(UpperCamelCase__						)
           _lowerCamelCase			    =				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(UpperCamelCase__				,				(1, 3, 3_84, 3_84)						)

           with torch.no_grad():
                 _lowerCamelCase			    =				model(**UpperCamelCase__						)
           # masks_queries_logits
           _lowerCamelCase			    =				outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape				,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
           _lowerCamelCase			    =				[
               [-8.7839, -9.0056, -8.8121],
               [-7.4104, -7.0313, -6.5401],
               [-6.6105, -6.3427, -6.4675],
           ]
           _lowerCamelCase			    =				torch.tensor(UpperCamelCase__						).to(UpperCamelCase__						)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,				UpperCamelCase__				,				atol=UpperCamelCase__						)						)
           # class_queries_logits
           _lowerCamelCase			    =				outputs.class_queries_logits
           self.assertEqual(class_queries_logits.shape				,				(1, model.config.num_queries, model.config.num_labels + 1)						)
           _lowerCamelCase			    =				torch.tensor(
               [
                   [1.8324, -8.0835, -4.1922],
                   [0.8450, -9.0050, -3.6053],
                   [0.3045, -7.7293, -3.0275],
               ]						).to(UpperCamelCase__						)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,				UpperCamelCase__				,				atol=UpperCamelCase__						)						)





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






           """simple docstring"""
           _lowerCamelCase			    =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(UpperCamelCase__						).eval()
           _lowerCamelCase			    =				self.default_image_processor

           _lowerCamelCase			    =				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'''				,				)

           _lowerCamelCase			    =				inputs['''pixel_values'''].to(UpperCamelCase__						)
           _lowerCamelCase			    =				[el.to(UpperCamelCase__						) for el in inputs['''mask_labels''']]
           _lowerCamelCase			    =				[el.to(UpperCamelCase__						) for el in inputs['''class_labels''']]

           with torch.no_grad():
                 _lowerCamelCase			    =				model(**UpperCamelCase__						)

           self.assertTrue(outputs.loss is not None						)
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip