


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_MAPPING,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
  )
  from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  import PIL
  from PIL import Image

  from transformers import BeitImageProcessor



class    UpperCAmelCase_	:
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=100 ,					__SCREAMING_SNAKE_CASE=13 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=37 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=10 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=[0, 1, 2, 3] ,					):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		parent
              UpperCamelCase    :	Dict   	=		100
              UpperCamelCase    :	Tuple   	=		batch_size
              UpperCamelCase    :	Union[str, Any]   	=		image_size
              UpperCamelCase    :	Tuple   	=		patch_size
              UpperCamelCase    :	Union[str, Any]   	=		num_channels
              UpperCamelCase    :	Dict   	=		is_training
              UpperCamelCase    :	List[Any]   	=		use_labels
              UpperCamelCase    :	Union[str, Any]   	=		hidden_size
              UpperCamelCase    :	List[Any]   	=		num_hidden_layers
              UpperCamelCase    :	Tuple   	=		num_attention_heads
              UpperCamelCase    :	List[Any]   	=		intermediate_size
              UpperCamelCase    :	Dict   	=		hidden_act
              UpperCamelCase    :	str   	=		hidden_dropout_prob
              UpperCamelCase    :	List[Any]   	=		attention_probs_dropout_prob
              UpperCamelCase    :	Optional[int]   	=		type_sequence_label_size
              UpperCamelCase    :	int   	=		initializer_range
              UpperCamelCase    :	str   	=		scope
              UpperCamelCase    :	List[Any]   	=		out_indices
              UpperCamelCase    :	Tuple   	=		num_labels

              # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
              UpperCamelCase    :	List[Any]   	=		(image_size // patch_size) ** 2
              UpperCamelCase    :	List[Any]   	=		num_patches + 1






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

              UpperCamelCase    :	Dict   	=		None
              UpperCamelCase    :	Optional[Any]   	=		None
              if self.use_labels:
                     UpperCamelCase    :	List[str]   	=		ids_tensor([self.batch_size] ,					self.type_sequence_label_size						)
                     UpperCamelCase    :	Dict   	=		ids_tensor([self.batch_size, self.image_size, self.image_size] ,					self.num_labels						)

              UpperCamelCase    :	List[Any]   	=		self.get_config()

              return config, pixel_values, labels, pixel_labels






       def  _lowercase   (     self						):




              """simple docstring"""




              return BeitConfig(
                  vocab_size=self.vocab_size ,					image_size=self.image_size ,					patch_size=self.patch_size ,					num_channels=self.num_channels ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					is_decoder=__SCREAMING_SNAKE_CASE ,					initializer_range=self.initializer_range ,					out_indices=self.out_indices ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		BeitModel(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	List[str]   	=		model(__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		BeitForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Optional[Any]   	=		model(__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length - 1, self.vocab_size)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.type_sequence_label_size
              UpperCamelCase    :	Optional[Any]   	=		BeitForImageClassification(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	str   	=		model(__SCREAMING_SNAKE_CASE ,					labels=__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.type_sequence_label_size)						)

              # test greyscale images
              UpperCamelCase    :	Any   	=		1
              UpperCamelCase    :	Optional[int]   	=		BeitForImageClassification(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()

              UpperCamelCase    :	List[Any]   	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
              UpperCamelCase    :	Optional[int]   	=		model(__SCREAMING_SNAKE_CASE ,					labels=__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.type_sequence_label_size)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.num_labels
              UpperCamelCase    :	Dict   	=		BeitForSemanticSegmentation(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	List[str]   	=		model(__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(
                  result.logits.shape ,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
              UpperCamelCase    :	List[str]   	=		model(__SCREAMING_SNAKE_CASE ,					labels=__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(
                  result.logits.shape ,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.prepare_config_and_inputs()
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	str   	=		config_and_inputs
              UpperCamelCase    :	List[str]   	=		{'''pixel_values''': pixel_values}
              return config, inputs_dict




@require_torch
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Union[str, Any]							=					(
           (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       __UpperCamelCase      :     Any							=					(
           {
               "feature-extraction": BeitModel,
               "image-classification": BeitForImageClassification,
               "image-segmentation": BeitForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __UpperCamelCase      :     List[str]							=					False
       __UpperCamelCase      :     Dict							=					False
       __UpperCamelCase      :     Tuple							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		BeitModelTester(self						)
              UpperCamelCase    :	Dict   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					has_text_modality=__SCREAMING_SNAKE_CASE ,					hidden_size=37						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.config_tester.run_common_tests()






       @unittest.skip(reason='''BEiT does not use inputs_embeds'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @require_torch_multi_gpu
       @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	int   	=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Union[str, Any]   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)						)
                     UpperCamelCase    :	List[str]   	=		model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE ,					nn.Linear						)						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	List[str]   	=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Union[str, Any]   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		inspect.signature(model.forward						)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase    :	List[Any]   	=		[*signature.parameters.keys()]

                     UpperCamelCase    :	str   	=		['''pixel_values''']
                     self.assertListEqual(arg_names[:1] ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              if not self.model_tester.is_training:
                     return

              UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase    :	Tuple   	=		True

              for model_class in self.all_model_classes:
                     # we don't test BeitForMaskedImageModeling
                     if model_class in [*get_values(__SCREAMING_SNAKE_CASE						), BeitForMaskedImageModeling]:
                            continue

                     UpperCamelCase    :	str   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     model.to(__SCREAMING_SNAKE_CASE						)
                     model.train()
                     UpperCamelCase    :	Dict   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_labels=__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	int   	=		model(**__SCREAMING_SNAKE_CASE						).loss
                     loss.backward()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()
              if not self.model_tester.is_training:
                     return

              UpperCamelCase    :	int   	=		False
              UpperCamelCase    :	Optional[Any]   	=		True

              for model_class in self.all_model_classes:
                     # we don't test BeitForMaskedImageModeling
                     if (
                         model_class in [*get_values(__SCREAMING_SNAKE_CASE						), BeitForMaskedImageModeling]
                         or not model_class.supports_gradient_checkpointing
                     ):
                            continue

                     UpperCamelCase    :	str   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     model.gradient_checkpointing_enable()
                     model.to(__SCREAMING_SNAKE_CASE						)
                     model.train()
                     UpperCamelCase    :	Optional[int]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_labels=__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	str   	=		model(**__SCREAMING_SNAKE_CASE						).loss
                     loss.backward()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	str   	=		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase    :	Any   	=		_config_zero_init(__SCREAMING_SNAKE_CASE						)
              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Optional[Any]   	=		model_class(config=__SCREAMING_SNAKE_CASE						)
                     for name, param in model.named_parameters():
                            # we skip lambda parameters as these require special initial values
                            # determined by config.layer_scale_init_value
                            if "lambda" in name:
                                   continue
                            if param.requires_grad:
                                   self.assertIn(
                                       ((param.data.mean() * 1e9).round() / 1e9).item() ,					[0.0, 1.0] ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,					)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	Union[str, Any]   	=		BeitModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)




def      a	(							):






       """simple docstring"""





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



@require_torch
@require_vision
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @cached_property
       def  _lowercase   (     self						):




              """simple docstring"""




              return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''						) if is_vision_available() else None






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		self.default_image_processor
              UpperCamelCase    :	Any   	=		prepare_img()
              UpperCamelCase    :	int   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).pixel_values.to(__SCREAMING_SNAKE_CASE						)

              # prepare bool_masked_pos
              UpperCamelCase    :	Tuple   	=		torch.ones((1, 196) ,					dtype=torch.bool						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	List[Any]   	=		model(pixel_values=__SCREAMING_SNAKE_CASE ,					bool_masked_pos=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		outputs.logits

              # verify the logits
              UpperCamelCase    :	int   	=		torch.Size((1, 196, 8_192)						)
              self.assertEqual(logits.shape ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		torch.tensor(
                  [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]						).to(__SCREAMING_SNAKE_CASE						)

              self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,					__SCREAMING_SNAKE_CASE ,					atol=1e-2						)						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		self.default_image_processor
              UpperCamelCase    :	List[str]   	=		prepare_img()
              UpperCamelCase    :	List[Any]   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	Dict   	=		model(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		outputs.logits

              # verify the logits
              UpperCamelCase    :	Dict   	=		torch.Size((1, 1_000)						)
              self.assertEqual(logits.shape ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		torch.tensor([-1.2_385, -1.0_987, -1.0_108]						).to(__SCREAMING_SNAKE_CASE						)

              self.assertTrue(torch.allclose(logits[0, :3] ,					__SCREAMING_SNAKE_CASE ,					atol=1e-4						)						)

              UpperCamelCase    :	Union[str, Any]   	=		281
              self.assertEqual(logits.argmax(-1						).item() ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''						).to(
                  __SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		self.default_image_processor
              UpperCamelCase    :	Dict   	=		prepare_img()
              UpperCamelCase    :	Tuple   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	Any   	=		model(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		outputs.logits

              # verify the logits
              UpperCamelCase    :	Union[str, Any]   	=		torch.Size((1, 21_841)						)
              self.assertEqual(logits.shape ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		torch.tensor([1.6_881, -0.2_787, 0.5_901]						).to(__SCREAMING_SNAKE_CASE						)

              self.assertTrue(torch.allclose(logits[0, :3] ,					__SCREAMING_SNAKE_CASE ,					atol=1e-4						)						)

              UpperCamelCase    :	List[Any]   	=		2_396
              self.assertEqual(logits.argmax(-1						).item() ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''						)
              UpperCamelCase    :	List[str]   	=		model.to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		BeitImageProcessor(do_resize=__SCREAMING_SNAKE_CASE ,					size=640 ,					do_center_crop=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		load_dataset('''hf-internal-testing/fixtures_ade20k''' ,					split='''test'''						)
              UpperCamelCase    :	Any   	=		Image.open(ds[0]['''file''']						)
              UpperCamelCase    :	List[str]   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	List[Any]   	=		model(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		outputs.logits

              # verify the logits
              UpperCamelCase    :	List[str]   	=		torch.Size((1, 150, 160, 160)						)
              self.assertEqual(logits.shape ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		version.parse(PIL.__version__						) < version.parse('''9.0.0'''						)

              if is_pillow_less_than_a:
                     UpperCamelCase    :	Optional[Any]   	=		torch.tensor(
                         [
                             [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                             [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                             [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
                         ] ,					device=__SCREAMING_SNAKE_CASE ,					)
              else:
                     UpperCamelCase    :	Any   	=		torch.tensor(
                         [
                             [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
                             [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
                             [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
                         ] ,					device=__SCREAMING_SNAKE_CASE ,					)

              self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,					__SCREAMING_SNAKE_CASE ,					atol=1e-4						)						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''						)
              UpperCamelCase    :	str   	=		model.to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Any   	=		BeitImageProcessor(do_resize=__SCREAMING_SNAKE_CASE ,					size=640 ,					do_center_crop=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		load_dataset('''hf-internal-testing/fixtures_ade20k''' ,					split='''test'''						)
              UpperCamelCase    :	Dict   	=		Image.open(ds[0]['''file''']						)
              UpperCamelCase    :	Optional[int]   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	Optional[Any]   	=		model(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[int]   	=		outputs.logits.detach().cpu()

              UpperCamelCase    :	Dict   	=		image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE ,					target_sizes=[(500, 300)]						)
              UpperCamelCase    :	Tuple   	=		torch.Size((500, 300)						)
              self.assertEqual(segmentation[0].shape ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Dict   	=		torch.Size((160, 160)						)
              self.assertEqual(segmentation[0].shape ,					__SCREAMING_SNAKE_CASE						)





from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


__UpperCAmelCase : List[Any] 			=				[
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__UpperCAmelCase : List[str] 			=				"UperNetConfig"



class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = 1 ,					):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	str   	=		nn.Convad(
                  in_channels=__SCREAMING_SNAKE_CASE ,					out_channels=__SCREAMING_SNAKE_CASE ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					bias=__SCREAMING_SNAKE_CASE ,					dilation=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	int   	=		nn.BatchNormad(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		nn.ReLU()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.conv(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		self.batch_norm(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		self.activation(__SCREAMING_SNAKE_CASE						)

              return output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		[
                  nn.AdaptiveAvgPoolad(__SCREAMING_SNAKE_CASE						),
                  UperNetConvModule(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					kernel_size=1						),
              ]
              for i, layer in enumerate(self.layers						):
                     self.add_module(str(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		input
              for layer in self.layers:
                     UpperCamelCase    :	int   	=		layer(__SCREAMING_SNAKE_CASE						)
              return hidden_state




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		pool_scales
              UpperCamelCase    :	Dict   	=		align_corners
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	Union[str, Any]   	=		channels
              UpperCamelCase    :	List[str]   	=		[]
              for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Union[str, Any]   	=		UperNetPyramidPoolingBlock(pool_scale=__SCREAMING_SNAKE_CASE ,					in_channels=__SCREAMING_SNAKE_CASE ,					channels=__SCREAMING_SNAKE_CASE						)
                     self.blocks.append(__SCREAMING_SNAKE_CASE						)
                     self.add_module(str(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[]
              for ppm in self.blocks:
                     UpperCamelCase    :	List[str]   	=		ppm(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		nn.functional.interpolate(
                         __SCREAMING_SNAKE_CASE ,					size=x.size()[2:] ,					mode='''bilinear''' ,					align_corners=self.align_corners						)
                     ppm_outs.append(__SCREAMING_SNAKE_CASE						)
              return ppm_outs




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	int   	=		config
              UpperCamelCase    :	List[str]   	=		config.pool_scales  # e.g. (1, 2, 3, 6)
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	str   	=		config.hidden_size
              UpperCamelCase    :	str   	=		False
              UpperCamelCase    :	List[str]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)

              # PSP Module
              UpperCamelCase    :	Optional[int]   	=		UperNetPyramidPoolingModule(
                  self.pool_scales ,					self.in_channels[-1] ,					self.channels ,					align_corners=self.align_corners ,					)
              UpperCamelCase    :	str   	=		UperNetConvModule(
                  self.in_channels[-1] + len(self.pool_scales						) * self.channels ,					self.channels ,					kernel_size=3 ,					padding=1 ,					)
              # FPN Module
              UpperCamelCase    :	Union[str, Any]   	=		nn.ModuleList()
              UpperCamelCase    :	Union[str, Any]   	=		nn.ModuleList()
              for in_channels in self.in_channels[:-1]:  # skip the top layer
                     UpperCamelCase    :	List[Any]   	=		UperNetConvModule(__SCREAMING_SNAKE_CASE ,					self.channels ,					kernel_size=1						)
                     UpperCamelCase    :	int   	=		UperNetConvModule(self.channels ,					self.channels ,					kernel_size=3 ,					padding=1						)
                     self.lateral_convs.append(__SCREAMING_SNAKE_CASE						)
                     self.fpn_convs.append(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		UperNetConvModule(
                  len(self.in_channels						) * self.channels ,					self.channels ,					kernel_size=3 ,					padding=1 ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					nn.Convad						):
                     module.weight.data.normal_(mean=0.0 ,					std=self.config.initializer_range						)
                     if module.bias is not None:
                            module.bias.data.zero_()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		inputs[-1]
              UpperCamelCase    :	int   	=		[x]
              psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	Any   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=1						)
              UpperCamelCase    :	Union[str, Any]   	=		self.bottleneck(__SCREAMING_SNAKE_CASE						)

              return output






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[lateral_conv(encoder_hidden_states[i]						) for i, lateral_conv in enumerate(self.lateral_convs						)]

              laterals.append(self.psp_forward(__SCREAMING_SNAKE_CASE						)						)

              # build top-down path
              UpperCamelCase    :	int   	=		len(__SCREAMING_SNAKE_CASE						)
              for i in range(used_backbone_levels - 1 ,					0 ,					-1						):
                     UpperCamelCase    :	Optional[int]   	=		laterals[i - 1].shape[2:]
                     UpperCamelCase    :	Optional[Any]   	=		laterals[i - 1] + nn.functional.interpolate(
                         laterals[i] ,					size=__SCREAMING_SNAKE_CASE ,					mode='''bilinear''' ,					align_corners=self.align_corners						)

              # build outputs
              UpperCamelCase    :	str   	=		[self.fpn_convs[i](laterals[i]						) for i in range(used_backbone_levels - 1						)]
              # append psp feature
              fpn_outs.append(laterals[-1]						)

              for i in range(used_backbone_levels - 1 ,					0 ,					-1						):
                     UpperCamelCase    :	int   	=		nn.functional.interpolate(
                         fpn_outs[i] ,					size=fpn_outs[0].shape[2:] ,					mode='''bilinear''' ,					align_corners=self.align_corners						)
              UpperCamelCase    :	str   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=1						)
              UpperCamelCase    :	Tuple   	=		self.fpn_bottleneck(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		self.classifier(__SCREAMING_SNAKE_CASE						)

              return output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 2 ,					__SCREAMING_SNAKE_CASE = 3 ,					__SCREAMING_SNAKE_CASE = 1						):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	Dict   	=		config
              UpperCamelCase    :	Optional[Any]   	=		config.auxiliary_in_channels
              UpperCamelCase    :	Union[str, Any]   	=		config.auxiliary_channels
              UpperCamelCase    :	Union[str, Any]   	=		config.auxiliary_num_convs
              UpperCamelCase    :	Optional[Any]   	=		config.auxiliary_concat_input
              UpperCamelCase    :	List[str]   	=		in_index

              UpperCamelCase    :	Any   	=		(kernel_size // 2) * dilation
              UpperCamelCase    :	Optional[Any]   	=		[]
              convs.append(
                  UperNetConvModule(
                      self.in_channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					dilation=__SCREAMING_SNAKE_CASE						)						)
              for i in range(self.num_convs - 1						):
                     convs.append(
                         UperNetConvModule(
                             self.channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					dilation=__SCREAMING_SNAKE_CASE						)						)
              if self.num_convs == 0:
                     UpperCamelCase    :	str   	=		nn.Identity()
              else:
                     UpperCamelCase    :	Dict   	=		nn.Sequential(*__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	Union[str, Any]   	=		UperNetConvModule(
                         self.in_channels + self.channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=kernel_size // 2						)

              UpperCamelCase    :	Optional[Any]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					nn.Convad						):
                     module.weight.data.normal_(mean=0.0 ,					std=self.config.initializer_range						)
                     if module.bias is not None:
                            module.bias.data.zero_()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		encoder_hidden_states[self.in_index]
              UpperCamelCase    :	str   	=		self.convs(__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	int   	=		self.conv_cat(torch.cat([hidden_states, output] ,					dim=1						)						)
              UpperCamelCase    :	Union[str, Any]   	=		self.classifier(__SCREAMING_SNAKE_CASE						)
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					UperNetConfig
       __UpperCamelCase      :     Optional[int]							=					"pixel_values"
       __UpperCamelCase      :     Dict							=					True






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     module.backbone.init_weights()
                     module.decode_head.init_weights()
                     module.auxiliary_head.init_weights()






       def  _lowercase   (     self						):




              """simple docstring"""




              self.backbone.init_weights()
              self.decode_head.init_weights()
              self.auxiliary_head.init_weights()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	str   	=		value




__UpperCAmelCase : List[Any] 			=				r"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

__UpperCAmelCase : Union[str, Any] 			=				r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.",     _a,     )
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		AutoBackbone.from_config(config.backbone_config						)

              # Semantic segmentation head(s)
              UpperCamelCase    :	int   	=		UperNetHead(__SCREAMING_SNAKE_CASE ,					in_channels=self.backbone.channels						)
              UpperCamelCase    :	int   	=		UperNetFCNHead(__SCREAMING_SNAKE_CASE						) if config.use_auxiliary_head else None

              # Initialize weights and apply final processing
              self.post_init()






       @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length'''						)						)
       @replace_return_docstrings(output_type=__SCREAMING_SNAKE_CASE ,					config_class=_CONFIG_FOR_DOC						)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		return_dict if return_dict is not None else self.config.use_return_dict
              UpperCamelCase    :	str   	=		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase    :	Tuple   	=		output_attentions if output_attentions is not None else self.config.output_attentions

              UpperCamelCase    :	Tuple   	=		self.backbone.forward_with_filtered_kwargs(
                  __SCREAMING_SNAKE_CASE ,					output_hidden_states=__SCREAMING_SNAKE_CASE ,					output_attentions=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		outputs.feature_maps

              UpperCamelCase    :	Union[str, Any]   	=		self.decode_head(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		nn.functional.interpolate(__SCREAMING_SNAKE_CASE ,					size=pixel_values.shape[2:] ,					mode='''bilinear''' ,					align_corners=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		None
              if self.auxiliary_head is not None:
                     UpperCamelCase    :	int   	=		self.auxiliary_head(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		nn.functional.interpolate(
                         __SCREAMING_SNAKE_CASE ,					size=pixel_values.shape[2:] ,					mode='''bilinear''' ,					align_corners=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		None
              if labels is not None:
                     if self.config.num_labels == 1:
                            raise ValueError('''The number of labels should be greater than one'''						)
                     else:
                            # compute weighted loss
                            UpperCamelCase    :	Optional[int]   	=		CrossEntropyLoss(ignore_index=self.config.loss_ignore_index						)
                            UpperCamelCase    :	Tuple   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Optional[Any]   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Union[str, Any]   	=		main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

              if not return_dict:
                     if output_hidden_states:
                            UpperCamelCase    :	Optional[Any]   	=		(logits,) + outputs[1:]
                     else:
                            UpperCamelCase    :	int   	=		(logits,) + outputs[2:]
                     return ((loss,) + output) if loss is not None else output

              return SemanticSegmenterOutput(
                  loss=__SCREAMING_SNAKE_CASE ,					logits=__SCREAMING_SNAKE_CASE ,					hidden_states=outputs.hidden_states ,					attentions=outputs.attentions ,					)

