






"""simple docstring"""







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_ :



  def __init__(      self ,		snake_case__ ,		snake_case__=2 ,		snake_case__=True ,		snake_case__=False ,		snake_case__=10 ,		snake_case__=3 ,		snake_case__=32 * 8 ,		snake_case__=32 * 8 ,		snake_case__=4 ,		snake_case__=64 ,		)			->			Optional[int]:



         """simple docstring"""





         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	    =			hidden_dim
         UpperCAmelCase	    =			hidden_dim



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



         """simple docstring"""





         UpperCAmelCase	    =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
             snake_case__				)

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

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

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



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerConfig(
             hidden_size=self.hidden_dim ,		)
         UpperCAmelCase	    =			self.num_queries
         UpperCAmelCase	    =			self.num_labels

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

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



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



         """simple docstring"""





         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 ,		snake_case__ ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			output.encoder_hidden_states
         UpperCAmelCase	    =			output.pixel_decoder_hidden_states
         UpperCAmelCase	    =			output.transformer_decoder_hidden_states

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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=False				)			->			Optional[Any]:



         """simple docstring"""





         with torch.no_grad():
                UpperCAmelCase	    =			MaskaFormerModel(config=snake_case__				)
                model.to(snake_case__				)
                model.eval()

                UpperCAmelCase	    =			model(pixel_values=snake_case__ ,		pixel_mask=snake_case__				)
                UpperCAmelCase	    =			model(snake_case__ ,		output_hidden_states=snake_case__				)

         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(snake_case__ ,		snake_case__				)



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



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation(config=snake_case__				)
         model.to(snake_case__				)
         model.eval()

         def comm_check_on_output(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	    =			model(pixel_values=snake_case__ ,		pixel_mask=snake_case__				)
                UpperCAmelCase	    =			model(snake_case__				)

                comm_check_on_output(snake_case__				)

                UpperCAmelCase	    =			model(
                    pixel_values=snake_case__ ,		pixel_mask=snake_case__ ,		mask_labels=snake_case__ ,		class_labels=snake_case__				)

         comm_check_on_output(snake_case__				)

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





@require_torch
class   UpperCamelCase_ (       a_   ,					a_   ,					unittest.TestCase  ):
  _A       :					Union[str, Any]				=	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  _A       :					Optional[Any]				=	{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

  _A       :					List[str]				=	False
  _A       :					Union[str, Any]				=	False
  _A       :					Tuple				=	False
  _A       :					int				=	False



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



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerModelTester(self				)
         UpperCAmelCase	    =			ConfigTester(self ,		config_class=snake_case__ ,		has_text_modality=snake_case__				)



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



         """simple docstring"""





         self.config_tester.run_common_tests()



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(snake_case__ ,		**snake_case__ ,		output_hidden_states=snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case__				)



  @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""				)
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         pass



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



         """simple docstring"""





         pass



  @unittest.skip(reason="""Mask2Former is not a generative model"""				)
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         pass



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



         """simple docstring"""





         pass



  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase	    =			model_class(snake_case__				)
                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] ,		snake_case__				)



  @slow
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





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



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



         """simple docstring"""





         UpperCAmelCase	    =			(self.model_tester.min_size,) * 2
         UpperCAmelCase	    =			{
             """pixel_values""": torch.randn((2, 3, *size) ,		device=snake_case__				),
             """mask_labels""": torch.randn((2, 10, *size) ,		device=snake_case__				),
             """class_labels""": torch.zeros(2 ,		10 ,		device=snake_case__				).long(),
         }
         UpperCAmelCase	    =			self.model_tester.get_config()

         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation(snake_case__				).to(snake_case__				)
         UpperCAmelCase	    =			model(**snake_case__				)
         self.assertTrue(outputs.loss is not None				)



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(snake_case__ ,		**snake_case__ ,		output_hidden_states=snake_case__				)



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



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()

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



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



         """simple docstring"""





         if not self.model_tester.is_training:
                return

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

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

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



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         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(snake_case__				).to(snake_case__				)
         model.train()

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

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

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

         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=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				)





lowerCAmelCase_	:    Union[str, Any]            =      1E-4






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image





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



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



         """simple docstring"""





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



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



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(snake_case__				)
         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(snake_case__ ,		return_tensors="""pt"""				).to(snake_case__				)
         UpperCAmelCase	    =			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(snake_case__ ,		(1, 3, 3_84, 3_84)				)

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

         UpperCAmelCase	    =			torch.tensor(
             [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]				).to(snake_case__				)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)

         UpperCAmelCase	    =			torch.tensor(
             [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]				).to(snake_case__				)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)

         UpperCAmelCase	    =			torch.tensor(
             [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]				).to(snake_case__				)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(snake_case__				).eval()
         UpperCAmelCase	    =			self.default_image_processor
         UpperCAmelCase	    =			prepare_img()
         UpperCAmelCase	    =			image_processor(snake_case__ ,		return_tensors="""pt"""				).to(snake_case__				)
         UpperCAmelCase	    =			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(snake_case__ ,		(1, 3, 3_84, 3_84)				)

         with torch.no_grad():
                UpperCAmelCase	    =			model(**snake_case__				)
         # masks_queries_logits
         UpperCAmelCase	    =			outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape ,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
         UpperCAmelCase	    =			[
             [-8.7_839, -9.0_056, -8.8_121],
             [-7.4_104, -7.0_313, -6.5_401],
             [-6.6_105, -6.3_427, -6.4_675],
         ]
         UpperCAmelCase	    =			torch.tensor(snake_case__				).to(snake_case__				)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)
         # class_queries_logits
         UpperCAmelCase	    =			outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape ,		(1, model.config.num_queries, model.config.num_labels + 1)				)
         UpperCAmelCase	    =			torch.tensor(
             [
                 [1.8_324, -8.0_835, -4.1_922],
                 [0.8_450, -9.0_050, -3.6_053],
                 [0.3_045, -7.7_293, -3.0_275],
             ]				).to(snake_case__				)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,		snake_case__ ,		atol=snake_case__				)				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(snake_case__				).eval()
         UpperCAmelCase	    =			self.default_image_processor

         UpperCAmelCase	    =			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""" ,		)

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

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

         self.assertTrue(outputs.loss is not None				)








"""simple docstring"""







from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
