



"""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       :    Union[str, Any]				,						lowerCAmelCase       :    Tuple				,						lowerCAmelCase       :    Tuple=2				,						lowerCAmelCase       :    str=True				,						lowerCAmelCase       :    List[str]=False				,						lowerCAmelCase       :    List[Any]=10				,						lowerCAmelCase       :    Union[str, Any]=3				,						lowerCAmelCase       :    Tuple=32 * 4				,						lowerCAmelCase       :    int=32 * 6				,						lowerCAmelCase       :    Any=4				,						lowerCAmelCase       :    Optional[int]=32				,						)   ->    List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=parent
     SCREAMING_SNAKE_CASE_:       Dict   	=batch_size
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=is_training
     SCREAMING_SNAKE_CASE_:       Any   	=use_auxiliary_loss
     SCREAMING_SNAKE_CASE_:       str   	=num_queries
     SCREAMING_SNAKE_CASE_:       Dict   	=num_channels
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=min_size
     SCREAMING_SNAKE_CASE_:       Tuple   	=max_size
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=num_labels
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=mask_feature_size



  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
         lowercase_						)

     SCREAMING_SNAKE_CASE_:       int   	=torch.ones([self.batch_size, self.min_size, self.max_size]				,						device=lowercase_						)

     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=(
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]				,						device=lowercase_						) > 0.5
     ).float()
     SCREAMING_SNAKE_CASE_:       Tuple   	=(torch.rand((self.batch_size, self.num_labels)				,						device=lowercase_						) > 0.5).long()

     SCREAMING_SNAKE_CASE_:       List[str]   	=self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels



  def 		lowerCamelCase__				(  self       :    Dict						)   ->    Dict:


     '''simple docstring'''




     return MaskFormerConfig.from_backbone_and_decoder_configs(
         backbone_config=SwinConfig(
             depths=[1, 1, 1, 1]				,						)				,						decoder_config=DetrConfig(
             decoder_ffn_dim=128				,						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 		lowerCamelCase__				(  self       :    Dict						)   ->    Any:


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    Optional[Any]				,						lowerCAmelCase       :    str				,						lowerCAmelCase       :    List[str]						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=output.encoder_hidden_states
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=output.pixel_decoder_hidden_states
     SCREAMING_SNAKE_CASE_:       Union[str, 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 		lowerCamelCase__				(  self       :    int				,						lowerCAmelCase       :    Tuple				,						lowerCAmelCase       :    Union[str, Any]				,						lowerCAmelCase       :    Tuple				,						lowerCAmelCase       :    int=False						)   ->    List[str]:


     '''simple docstring'''




     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:       str   	=MaskFormerModel(config=lowercase_						)
        model.to(lowercase_						)
        model.eval()

        SCREAMING_SNAKE_CASE_:       int   	=model(pixel_values=lowercase_				,						pixel_mask=lowercase_						)
        SCREAMING_SNAKE_CASE_:       List[Any]   	=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 		lowerCamelCase__				(  self       :    List[Any]				,						lowerCAmelCase       :    int				,						lowerCAmelCase       :    List[Any]				,						lowerCAmelCase       :    Any				,						lowerCAmelCase       :    List[Any]				,						lowerCAmelCase       :    List[str]						)   ->    List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=MaskFormerForInstanceSegmentation(config=lowercase_						)
     model.to(lowercase_						)
     model.eval()

     def comm_check_on_output(lowerCAmelCase       :    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():
        SCREAMING_SNAKE_CASE_:       List[str]   	=model(pixel_values=lowercase_				,						pixel_mask=lowercase_						)
        SCREAMING_SNAKE_CASE_:       Optional[int]   	=model(lowercase_						)

        comm_check_on_output(lowercase_						)

        SCREAMING_SNAKE_CASE_:       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		(     UpperCamelCase__		,					UpperCamelCase__		,					unittest.TestCase				):
  UpperCamelCase		: Optional[int]     =    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  UpperCamelCase		: Any     =    (
      {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  UpperCamelCase		: List[Any]     =    False
  UpperCamelCase		: List[Any]     =    False
  UpperCamelCase		: Tuple     =    False
  UpperCamelCase		: List[Any]     =    False



  def 		lowerCamelCase__				(  self       :    Optional[Any]						)   ->    Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=MaskFormerModelTester(self						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=ConfigTester(self				,						config_class=lowercase_				,						has_text_modality=lowercase_						)



  def 		lowerCamelCase__				(  self       :    List[str]						)   ->    int:


     '''simple docstring'''




     self.config_tester.run_common_tests()



  def 		lowerCamelCase__				(  self       :    int						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowercase_				,						**lowercase_				,						output_hidden_states=lowercase_						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[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 		lowerCamelCase__				(  self       :    Optional[int]						)   ->    Tuple:


     '''simple docstring'''




     pass



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


     '''simple docstring'''




     pass



  @unittest.skip(reason="""MaskFormer is not a generative model"""						)
  def 		lowerCamelCase__				(  self       :    int						)   ->    Any:


     '''simple docstring'''




     pass



  @unittest.skip(reason="""MaskFormer does not use token embeddings"""						)
  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    Optional[Any]:


     '''simple docstring'''




     pass



  @require_torch_multi_gpu
  @unittest.skip(
      reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""						)
  def 		lowerCamelCase__				(  self       :    Optional[int]						)   ->    List[Any]:


     '''simple docstring'''




     pass



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


     '''simple docstring'''




     pass



  def 		lowerCamelCase__				(  self       :    Dict						)   ->    Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Tuple   	=self.model_tester.prepare_config_and_inputs_for_common()

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

        SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=["""pixel_values"""]
        self.assertListEqual(arg_names[:1]				,						lowercase_						)



  @slow
  def 		lowerCamelCase__				(  self       :    Dict						)   ->    Union[str, Any]:


     '''simple docstring'''




     for model_name in ["facebook/maskformer-swin-small-coco"]:
        SCREAMING_SNAKE_CASE_:       Any   	=MaskFormerModel.from_pretrained(lowercase_						)
        self.assertIsNotNone(lowercase_						)



  def 		lowerCamelCase__				(  self       :    int						)   ->    Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       str   	=(self.model_tester.min_size,) * 2
     SCREAMING_SNAKE_CASE_:       Optional[int]   	={
         """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(),
     }

     SCREAMING_SNAKE_CASE_:       Tuple   	=MaskFormerForInstanceSegmentation(MaskFormerConfig()						).to(lowercase_						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=model(**lowercase_						)
     self.assertTrue(outputs.loss is not None						)



  def 		lowerCamelCase__				(  self       :    Optional[Any]						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       List[Any]   	=self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowercase_				,						**lowercase_				,						output_hidden_states=lowercase_						)



  def 		lowerCamelCase__				(  self       :    List[str]						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Any   	=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:       List[str]   	=model_class(lowercase_						).to(lowercase_						)
        SCREAMING_SNAKE_CASE_:       str   	=model(**lowercase_				,						output_attentions=lowercase_						)
        self.assertTrue(outputs.attentions is not None						)



  def 		lowerCamelCase__				(  self       :    str						)   ->    Tuple:


     '''simple docstring'''




     if not self.model_tester.is_training:
        return
     # only MaskFormerForInstanceSegmentation has the loss
     SCREAMING_SNAKE_CASE_:       int   	=self.all_model_classes[1]
     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.model_tester.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE_:       Dict   	=model_class(lowercase_						)
     model.to(lowercase_						)
     model.train()

     SCREAMING_SNAKE_CASE_:       Dict   	=model(lowercase_				,						mask_labels=lowercase_				,						class_labels=lowercase_						).loss
     loss.backward()





  def 		lowerCamelCase__				(  self       :    Any						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self.all_model_classes[1]
     SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       Dict   	=self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE_:       int   	=True
     SCREAMING_SNAKE_CASE_:       Tuple   	=True

     SCREAMING_SNAKE_CASE_:       int   	=model_class(lowercase_						)
     model.to(lowercase_						)
     model.train()

     SCREAMING_SNAKE_CASE_:       int   	=model(lowercase_				,						mask_labels=lowercase_				,						class_labels=lowercase_						)

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

     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=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
     SCREAMING_SNAKE_CASE_:       Tuple   	=outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=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						)







_UpperCAmelCase   =	1e-4





def    __magic_name__ (							):
   SCREAMING_SNAKE_CASE_:       Tuple   	=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
   return image
@require_vision
@slow
class     a		(     unittest.TestCase				):



  @cached_property
  def 		lowerCamelCase__				(  self       :    Any						)   ->    str:


     '''simple docstring'''




     return (
         MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
         if is_vision_available()
         else None
     )



  def 		lowerCamelCase__				(  self       :    List[str]						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""						).to(lowercase_						)
     SCREAMING_SNAKE_CASE_:       int   	=self.default_image_processor
     SCREAMING_SNAKE_CASE_:       str   	=prepare_img()
     SCREAMING_SNAKE_CASE_:       List[str]   	=image_processor(lowercase_				,						return_tensors="""pt"""						).to(lowercase_						)
     SCREAMING_SNAKE_CASE_:       Union[str, 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, 800, 1088)						)

     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:       Dict   	=model(**lowercase_						)

     SCREAMING_SNAKE_CASE_:       str   	=torch.tensor(
         [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]						).to(lowercase_						)
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]				,						lowercase_				,						atol=lowercase_						)						)

     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=torch.tensor(
         [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]						).to(lowercase_						)
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,						lowercase_				,						atol=lowercase_						)						)

     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=torch.tensor(
         [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]						).to(lowercase_						)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,						lowercase_				,						atol=lowercase_						)						)



  def 		lowerCamelCase__				(  self       :    str						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=(
         MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
         .to(lowercase_						)
         .eval()
     )
     SCREAMING_SNAKE_CASE_:       Any   	=self.default_image_processor
     SCREAMING_SNAKE_CASE_:       Tuple   	=prepare_img()
     SCREAMING_SNAKE_CASE_:       int   	=image_processor(lowercase_				,						return_tensors="""pt"""						).to(lowercase_						)
     SCREAMING_SNAKE_CASE_:       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, 800, 1088)						)

     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:       List[Any]   	=model(**lowercase_						)
     # masks_queries_logits
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=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)				,						)
     SCREAMING_SNAKE_CASE_:       Dict   	=[
         [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
         [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
         [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
     ]
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=torch.tensor(lowercase_						).to(lowercase_						)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,						lowercase_				,						atol=lowercase_						)						)
     # class_queries_logits
     SCREAMING_SNAKE_CASE_:       Any   	=outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape				,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=torch.tensor(
         [
             [1.6_512E00, -5.2_572E00, -3.3_519E00],
             [3.6_169E-02, -5.9_025E00, -2.9_313E00],
             [1.0_766E-04, -7.7_630E00, -5.1_263E00],
         ]						).to(lowercase_						)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,						lowercase_				,						atol=lowercase_						)						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=(
         MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""						)
         .to(lowercase_						)
         .eval()
     )
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.default_image_processor
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=prepare_img()
     SCREAMING_SNAKE_CASE_:       List[Any]   	=image_processor(lowercase_				,						return_tensors="""pt"""						).to(lowercase_						)
     SCREAMING_SNAKE_CASE_:       Union[str, 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, 800, 1088)						)

     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:       List[str]   	=model(**lowercase_						)
     # masks_queries_logits
     SCREAMING_SNAKE_CASE_:       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)				,						)
     SCREAMING_SNAKE_CASE_:       Any   	=[[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -10.7711]]
     SCREAMING_SNAKE_CASE_:       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
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape				,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
     SCREAMING_SNAKE_CASE_:       str   	=torch.tensor(
         [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]						).to(lowercase_						)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,						lowercase_				,						atol=lowercase_						)						)





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=(
         MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
         .to(lowercase_						)
         .eval()
     )
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.default_image_processor

     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=image_processor(
         [np.zeros((3, 800, 1333)						), np.zeros((3, 800, 1333)						)]				,						segmentation_maps=[np.zeros((384, 384)						).astype(np.floataa						), np.zeros((384, 384)						).astype(np.floataa						)]				,						return_tensors="""pt"""				,						)

     SCREAMING_SNAKE_CASE_:       List[str]   	=inputs["""pixel_values"""].to(lowercase_						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=[el.to(lowercase_						) for el in inputs["""mask_labels"""]]
     SCREAMING_SNAKE_CASE_:       List[str]   	=[el.to(lowercase_						) for el in inputs["""class_labels"""]]

     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:       Any   	=model(**lowercase_						)

     self.assertTrue(outputs.loss is not None						)










'''simple docstring'''

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""char"""
 __lowercase   		=				"""bpe"""
 __lowercase   		=				"""wp"""

__lowerCAmelCase							:	Union[str, Any]			=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """char_tokenizer"""]
 __lowercase   		=				"""ViTImageProcessor"""
 __lowercase   		=				"""MgpstrTokenizer"""
 def __init__(				self      :int						,       lowercase_      :int=None						,       lowercase_      :List[str]=None						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         A__				=			kwargs.pop("feature_extractor"							)

     A__				=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."							)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."							)

     A__				=			tokenizer
     A__				=			AutoTokenizer.from_pretrained("gpt2"							)
     A__				=			AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :Any=None						,       lowercase_      :Tuple=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Union[str, Any]							)->							Optional[Any]:
     if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."							)

     if images is not None:
         A__				=			self.image_processor(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.char_tokenizer(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int							)->							int:
     A__,	A__,	A__				=			sequences
     A__				=			char_preds.size(0							)

     A__,	A__				=			self._decode_helper(lowercase_						,       "char"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "bpe"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "wp"							)

     A__				=			[]
     A__				=			[]
     for i in range(lowercase_							):
         A__				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         A__				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         A__				=			scores.index(max(lowercase_							)							)
         final_strs.append(strs[max_score_index]							)
         final_scores.append(scores[max_score_index]							)

     A__				=			{}
     A__				=			final_strs
     A__				=			final_scores
     A__				=			char_strs
     A__				=			bpe_strs
     A__				=			wp_strs
     return out
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :str							)->							Optional[Any]:
     if format == DecodeType.CHARACTER:
         A__				=			self.char_decode
         A__				=			1
         A__				=			"[s]"
     elif format == DecodeType.BPE:
         A__				=			self.bpe_decode
         A__				=			2
         A__				=			"#"
     elif format == DecodeType.WORDPIECE:
         A__				=			self.wp_decode
         A__				=			1_02
         A__				=			"[SEP]"
     else:
         raise ValueError(F"Format {format} is not supported."							)

     A__,	A__				=			[], []
     A__				=			pred_logits.size(0							)
     A__				=			pred_logits.size(1							)
     A__,	A__				=			pred_logits.topk(1						,       dim=-1						,       largest=lowercase_						,       sorted=lowercase_							)
     A__				=			preds_index.view(-1						,       lowercase_							)[:, 1:]
     A__				=			decoder(lowercase_							)
     A__,	A__				=			torch.nn.functional.softmax(lowercase_						,       dim=2							).max(dim=2							)
     A__				=			preds_max_prob[:, 1:]

     for index in range(lowercase_							):
         A__				=			preds_str[index].find(lowercase_							)
         A__				=			preds_str[index][:pred_eos]
         A__				=			preds_index[index].cpu().tolist()
         A__				=			pred_index.index(lowercase_							) if eos_token in pred_index else -1
         A__				=			preds_max_prob[index][: pred_eos_index + 1]
         A__				=			pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(lowercase_							)
         conf_scores.append(lowercase_							)

     return dec_strs, conf_scores
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							int:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.char_tokenizer.batch_decode(lowercase_							)]
     return decode_strs
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							List[str]:
     return self.bpe_tokenizer.batch_decode(lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :List[str]							)->							Union[str, Any]:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.wp_tokenizer.batch_decode(lowercase_							)]
     return decode_strs



