



'''simple docstring'''





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_lowerCAmelCase      						=	HfArgumentParser(InitializationArguments)
_lowerCAmelCase      						=	parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_lowerCAmelCase      						=	AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_lowerCAmelCase      						=	{
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
_lowerCAmelCase      						=	AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_lowerCAmelCase      						=	AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)







'''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        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=2  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=10  ,__UpperCAmelCase=3  ,__UpperCAmelCase=32 * 4  ,__UpperCAmelCase=32 * 6  ,__UpperCAmelCase=4  ,__UpperCAmelCase=32  ,)    ->		str:
      lowerCAmelCase__ :  Optional[int]							      =					parent
      lowerCAmelCase__ :  Optional[int]							      =					batch_size
      lowerCAmelCase__ :  Optional[int]							      =					is_training
      lowerCAmelCase__ :  Dict							      =					use_auxiliary_loss
      lowerCAmelCase__ :  Union[str, Any]							      =					num_queries
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  List[str]							      =					min_size
      lowerCAmelCase__ :  int							      =					max_size
      lowerCAmelCase__ :  Optional[Any]							      =					num_labels
      lowerCAmelCase__ :  List[Any]							      =					mask_feature_size




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  str							      =					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
          __UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					torch.ones([self.batch_size, self.min_size, self.max_size]  ,device=__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					(
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,device=__UpperCAmelCase      ) > 0.5
      ).float()
      lowerCAmelCase__ :  Optional[int]							      =					(torch.rand((self.batch_size, self.num_labels)  ,device=__UpperCAmelCase      ) > 0.5).long()

      lowerCAmelCase__ :  Any							      =					self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      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 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.prepare_config_and_inputs()
      lowerCAmelCase__ :  List[str]							      =					{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Any:
      lowerCAmelCase__ :  Optional[int]							      =					output.encoder_hidden_states
      lowerCAmelCase__ :  Optional[int]							      =					output.pixel_decoder_hidden_states
      lowerCAmelCase__ :  Dict							      =					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_config.decoder_layers      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=False      )    ->		Optional[Any]:
      with torch.no_grad():
          lowerCAmelCase__ :  int							      =					MaskFormerModel(config=__UpperCAmelCase      )
          model.to(__UpperCAmelCase      )
          model.eval()

          lowerCAmelCase__ :  str							      =					model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					model(__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )
      # 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(__UpperCAmelCase  ,__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  Dict							      =					MaskFormerForInstanceSegmentation(config=__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.eval()

      def comm_check_on_output(__UpperCAmelCase      ):
          # 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__ :  List[Any]							      =					model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					model(__UpperCAmelCase      )

          comm_check_on_output(__UpperCAmelCase      )

          lowerCAmelCase__ :  Optional[int]							      =					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        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  __lowercase							:      int				=      (
      {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  __lowercase							:      Union[str, Any]				=      False
  __lowercase							:      Dict				=      False
  __lowercase							:      Tuple				=      False
  __lowercase							:      List[Any]				=      False




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  str							      =					MaskFormerModelTester(self      )
      lowerCAmelCase__ :  List[Any]							      =					ConfigTester(self  ,config_class=__UpperCAmelCase  ,has_text_modality=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__UpperCAmelCase      )




  @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      pass




  @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""      )
  def 							UpperCAmelCase_	(    self      )    ->		str:
      pass




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




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




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




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




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self.model_tester.prepare_config_and_inputs_for_common()

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

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




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      for model_name in ["facebook/maskformer-swin-small-coco"]:
          lowerCAmelCase__ :  List[str]							      =					MaskFormerModel.from_pretrained(__UpperCAmelCase      )
          self.assertIsNotNone(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					(self.model_tester.min_size,) * 2
      lowerCAmelCase__ :  Any							      =					{
          """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__ :  Tuple							      =					MaskFormerForInstanceSegmentation(MaskFormerConfig()      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase      )
      self.assertTrue(outputs.loss is not None      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase__ :  int							      =					model_class(__UpperCAmelCase      ).to(__UpperCAmelCase      )
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase  ,output_attentions=__UpperCAmelCase      )
          self.assertTrue(outputs.attentions is not None      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      if not self.model_tester.is_training:
          return
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase__ :  Dict							      =					self.all_model_classes[1]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.model_tester.prepare_config_and_inputs()

      lowerCAmelCase__ :  List[Any]							      =					model_class(__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.train()

      lowerCAmelCase__ :  List[str]							      =					model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      ).loss
      loss.backward()






  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase__ :  Tuple							      =					self.all_model_classes[1]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase__ :  Union[str, Any]							      =					True
      lowerCAmelCase__ :  Tuple							      =					True

      lowerCAmelCase__ :  Optional[Any]							      =					model_class(__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.train()

      lowerCAmelCase__ :  Dict							      =					model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

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

      lowerCAmelCase__ :  List[Any]							      =					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      )







_lowerCAmelCase      						=	1e-4
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
    return image






@require_vision
@slow
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      return (
          MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          if is_vision_available()
          else None
      )




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Any							      =					MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					self.default_image_processor
      lowerCAmelCase__ :  str							      =					prepare_img()
      lowerCAmelCase__ :  Optional[int]							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					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, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					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(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )

      lowerCAmelCase__ :  Dict							      =					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(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )

      lowerCAmelCase__ :  Optional[int]							      =					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(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  List[Any]							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.default_image_processor
      lowerCAmelCase__ :  List[str]							      =					prepare_img()
      lowerCAmelCase__ :  str							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					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, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase      )
      # masks_queries_logits
      lowerCAmelCase__ :  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)  ,)
      lowerCAmelCase__ :  Optional[int]							      =					[
          [-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],
      ]
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor(__UpperCAmelCase      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )
      # class_queries_logits
      lowerCAmelCase__ :  Tuple							      =					outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [
              [1.65_12E00, -5.25_72E00, -3.35_19E00],
              [3.61_69E-02, -5.90_25E00, -2.93_13E00],
              [1.07_66E-04, -7.76_30E00, -5.12_63E00],
          ]      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.default_image_processor
      lowerCAmelCase__ :  int							      =					prepare_img()
      lowerCAmelCase__ :  Optional[Any]							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					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, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  str							      =					model(**__UpperCAmelCase      )
      # masks_queries_logits
      lowerCAmelCase__ :  Optional[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)  ,)
      lowerCAmelCase__ :  int							      =					[[-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, -1_0.7_7_1_1]]
      lowerCAmelCase__ :  List[str]							      =					torch.tensor(__UpperCAmelCase      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )
      # class_queries_logits
      lowerCAmelCase__ :  Optional[Any]							      =					outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
      lowerCAmelCase__ :  Tuple							      =					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(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  str							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Dict							      =					self.default_image_processor

      lowerCAmelCase__ :  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"""  ,)

      lowerCAmelCase__ :  Tuple							      =					inputs["""pixel_values"""].to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					[el.to(__UpperCAmelCase      ) for el in inputs["""mask_labels"""]]
      lowerCAmelCase__ :  Union[str, Any]							      =					[el.to(__UpperCAmelCase      ) for el in inputs["""class_labels"""]]

      with torch.no_grad():
          lowerCAmelCase__ :  Any							      =					model(**__UpperCAmelCase      )

      self.assertTrue(outputs.loss is not None      )


