

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        a__       :







  """simple docstring"""




  def __init__(			self    : int				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : List[Any]=2				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Optional[int]=False				,				UpperCAmelCase__    : List[Any]=1_0				,				UpperCAmelCase__    : Tuple=3				,				UpperCAmelCase__    : Optional[int]=3_2 * 8				,				UpperCAmelCase__    : str=3_2 * 8				,				UpperCAmelCase__    : Tuple=4				,				UpperCAmelCase__    : Any=6_4				,				)					->List[str]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Any		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
            UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.ones([self.batch_size, self.min_size, self.max_size]				,				device=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]				,				device=UpperCAmelCase__		) > 0.5
        ).float()
        SCREAMING_SNAKE_CASE :      Dict       =   (torch.rand((self.batch_size, self.num_labels)				,				device=UpperCAmelCase__		) > 0.5).long()

        SCREAMING_SNAKE_CASE :      int       =   self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels


  def  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   MaskaFormerConfig(
            hidden_size=self.hidden_dim				,				)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.num_queries
        SCREAMING_SNAKE_CASE :      int       =   self.num_labels

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   [1, 1, 1, 1]
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.num_channels

        SCREAMING_SNAKE_CASE :      List[str]       =   6_4
        SCREAMING_SNAKE_CASE :      str       =   1_2_8
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.hidden_dim
        SCREAMING_SNAKE_CASE :      List[str]       =   self.hidden_dim
        SCREAMING_SNAKE_CASE :      int       =   self.hidden_dim
        return config


  def  _lowercase      (			self    : Optional[Any]		)					->Optional[int]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Any		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   output.encoder_hidden_states
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   output.pixel_decoder_hidden_states
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   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  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : List[Any]=False		)					->Union[str, Any]:
        """simple docstring"""







        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      Optional[int]       =   MaskaFormerModel(config=UpperCAmelCase__		)
              model.to(UpperCAmelCase__		)
              model.eval()

              SCREAMING_SNAKE_CASE :      Optional[Any]       =   model(pixel_values=UpperCAmelCase__				,				pixel_mask=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      int       =   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  _lowercase      (			self    : int				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Union[str, Any]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   MaskaFormerForUniversalSegmentation(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()

        def comm_check_on_output(UpperCAmelCase__    : Tuple		):
              # 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 :      Optional[Any]       =   model(pixel_values=UpperCAmelCase__				,				pixel_mask=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(UpperCAmelCase__		)

              comm_check_on_output(UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE :      Any       =   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        a__       (   UpperCAmelCase	,			UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




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

  UpperCAmelCase__      : Optional[int]			       =False
  UpperCAmelCase__      : int			       =False
  UpperCAmelCase__      : List[Any]			       =False
  UpperCAmelCase__      : List[Any]			       =False


  def  _lowercase      (			self    : int		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   MaskaFormerModelTester(self		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				has_text_modality=UpperCAmelCase__		)


  def  _lowercase      (			self    : Any		)					->List[Any]:
        """simple docstring"""







        self.config_tester.run_common_tests()


  def  _lowercase      (			self    : List[Any]		)					->List[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_maskaformer_model(UpperCAmelCase__				,				**UpperCAmelCase__				,				output_hidden_states=UpperCAmelCase__		)


  def  _lowercase      (			self    : Tuple		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   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  _lowercase      (			self    : Union[str, Any]		)					->List[str]:
        """simple docstring"""







        pass


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







        pass


  @unittest.skip(reason="""Mask2Former is not a generative model"""		)
  def  _lowercase      (			self    : List[Any]		)					->Any:
        """simple docstring"""







        pass


  @unittest.skip(reason="""Mask2Former does not use token embeddings"""		)
  def  _lowercase      (			self    : int		)					->str:
        """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  _lowercase      (			self    : Tuple		)					->Union[str, Any]:
        """simple docstring"""







        pass


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







        pass


  def  _lowercase      (			self    : int		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE :      int       =   ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]				,				UpperCAmelCase__		)


  @slow
  def  _lowercase      (			self    : Union[str, Any]		)					->Dict:
        """simple docstring"""







        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   MaskaFormerModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (self.model_tester.min_size,) * 2
        SCREAMING_SNAKE_CASE :      List[Any]       =   {
            """pixel_values""": torch.randn((2, 3, *size)				,				device=UpperCAmelCase__		),
            """mask_labels""": torch.randn((2, 1_0, *size)				,				device=UpperCAmelCase__		),
            """class_labels""": torch.zeros(2				,				1_0				,				device=UpperCAmelCase__		).long(),
        }
        SCREAMING_SNAKE_CASE :      List[Any]       =   self.model_tester.get_config()

        SCREAMING_SNAKE_CASE :      Any       =   MaskaFormerForUniversalSegmentation(UpperCAmelCase__		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   model(**UpperCAmelCase__		)
        self.assertTrue(outputs.loss is not None		)


  def  _lowercase      (			self    : Any		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(UpperCAmelCase__				,				**UpperCAmelCase__				,				output_hidden_states=UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[int]		)					->List[str]:
        """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 :      str       =   model_class(UpperCAmelCase__		).to(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Optional[int]       =   model(**UpperCAmelCase__				,				output_attentions=UpperCAmelCase__		)
              self.assertTrue(outputs.attentions is not None		)


  def  _lowercase      (			self    : Optional[Any]		)					->Dict:
        """simple docstring"""







        if not self.model_tester.is_training:
              return

        SCREAMING_SNAKE_CASE :      List[str]       =   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 :      int       =   model_class(UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.train()

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(UpperCAmelCase__				,				mask_labels=UpperCAmelCase__				,				class_labels=UpperCAmelCase__		).loss
        loss.backward()




  def  _lowercase      (			self    : Dict		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.all_model_classes[1]
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[str]       =   self.model_tester.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE :      List[str]       =   True
        SCREAMING_SNAKE_CASE :      List[str]       =   True

        SCREAMING_SNAKE_CASE :      List[str]       =   model_class(UpperCAmelCase__		).to(UpperCAmelCase__		)
        model.train()

        SCREAMING_SNAKE_CASE :      List[str]       =   model(UpperCAmelCase__				,				mask_labels=UpperCAmelCase__				,				class_labels=UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE :      str       =   outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        SCREAMING_SNAKE_CASE :      List[Any]       =   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=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		)


UpperCAmelCase__ :     Optional[Any]				    =    1E-4






def 					__lowercase      (     )     ->						List[Any]:
      SCREAMING_SNAKE_CASE :      int       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image







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







  """simple docstring"""




  @cached_property
  def  _lowercase      (			self    : List[str]		)					->str:
        """simple docstring"""







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


  @cached_property
  def  _lowercase      (			self    : str		)					->str:
        """simple docstring"""







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


  def  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.default_image_processor
        SCREAMING_SNAKE_CASE :      Tuple       =   prepare_img()
        SCREAMING_SNAKE_CASE :      Any       =   image_processor(UpperCAmelCase__				,				return_tensors="""pt"""		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   inputs["""pixel_values"""].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0		)
        # check size
        self.assertEqual(UpperCAmelCase__				,				(1, 3, 3_8_4, 3_8_4)		)

        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      str       =   model(**UpperCAmelCase__		)

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

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

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


  def  _lowercase      (			self    : Any		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(UpperCAmelCase__		).eval()
        SCREAMING_SNAKE_CASE :      Dict       =   self.default_image_processor
        SCREAMING_SNAKE_CASE :      Any       =   prepare_img()
        SCREAMING_SNAKE_CASE :      List[str]       =   image_processor(UpperCAmelCase__				,				return_tensors="""pt"""		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   inputs["""pixel_values"""].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0		)
        # check size
        self.assertEqual(UpperCAmelCase__				,				(1, 3, 3_8_4, 3_8_4)		)

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




  def  _lowercase      (			self    : Tuple		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(UpperCAmelCase__		).eval()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.default_image_processor

        SCREAMING_SNAKE_CASE :      int       =   image_processor(
            [np.zeros((3, 8_0_0, 1_3_3_3)		), np.zeros((3, 8_0_0, 1_3_3_3)		)]				,				segmentation_maps=[np.zeros((3_8_4, 3_8_4)		).astype(np.floataa		), np.zeros((3_8_4, 3_8_4)		).astype(np.floataa		)]				,				return_tensors="""pt"""				,				)

        SCREAMING_SNAKE_CASE :      int       =   inputs["""pixel_values"""].to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   [el.to(UpperCAmelCase__		) for el in inputs["""mask_labels"""]]
        SCREAMING_SNAKE_CASE :      Tuple       =   [el.to(UpperCAmelCase__		) for el in inputs["""class_labels"""]]

        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      Dict       =   model(**UpperCAmelCase__		)

        self.assertTrue(outputs.loss is not None		)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :     Dict				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       ="""gpt_neox_japanese"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : Any=3_2_0_0_0				,				UpperCAmelCase__    : Dict=2_5_6_0				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3_2				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : List[str]="gelu"				,				UpperCAmelCase__    : Optional[int]=1.00				,				UpperCAmelCase__    : List[Any]=1_0_0_0_0				,				UpperCAmelCase__    : Tuple=2_0_4_8				,				UpperCAmelCase__    : Optional[int]=0.02				,				UpperCAmelCase__    : Dict=1e-5				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Tuple=3_1_9_9_6				,				UpperCAmelCase__    : Tuple=3_1_9_9_9				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : Union[str, Any]=0.0				,				**UpperCAmelCase__    : Optional[Any]				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(bos_token_id=UpperCAmelCase__				,				eos_token_id=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[str]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Any       =   intermediate_multiple_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_act
        SCREAMING_SNAKE_CASE :      Any       =   rotary_pct
        SCREAMING_SNAKE_CASE :      Tuple       =   rotary_emb_base
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      str       =   use_cache
        SCREAMING_SNAKE_CASE :      Any       =   attention_dropout
        SCREAMING_SNAKE_CASE :      str       =   hidden_dropout




