






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 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=100		,		_UpperCAmelCase=13		,		_UpperCAmelCase=30		,		_UpperCAmelCase=2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=4		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=10		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=None		,		_UpperCAmelCase=[0, 1, 2, 3]		,		)			-> Tuple:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     100
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     type_sequence_label_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     scope
     UpperCamelCase_									=     out_indices
     UpperCamelCase_									=     num_labels

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




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     UpperCamelCase_									=     None
     UpperCamelCase_									=     None
     if self.use_labels:
        UpperCamelCase_									=     ids_tensor([self.batch_size]		,		self.type_sequence_label_size )
        UpperCamelCase_									=     ids_tensor([self.batch_size, self.image_size, self.image_size]		,		self.num_labels )

     UpperCamelCase_									=     self.get_config()

     return config, pixel_values, labels, pixel_labels




  def    _UpperCAmelCase     (     self )			-> str:
     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=_UpperCAmelCase		,		initializer_range=self.initializer_range		,		out_indices=self.out_indices		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> int:
     UpperCamelCase_									=     BeitModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     BeitForMaskedImageModeling(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length - 1, self.vocab_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     self.type_sequence_label_size
     UpperCamelCase_									=     BeitForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     UpperCamelCase_									=     1
     UpperCamelCase_									=     BeitForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()

     UpperCamelCase_									=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     BeitForSemanticSegmentation(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(
         result.logits.shape		,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     self.parent.assertEqual(
         result.logits.shape		,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  A_						=   (
      {
          """feature-extraction""": BeitModel,
          """image-classification""": BeitForImageClassification,
          """image-segmentation""": BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     BeitModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     self.config_tester.run_common_tests()




  @unittest.skip(reason='BEiT does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> Any:
     pass




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




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        self.assertIsInstance(model.get_input_embeddings()		,		(nn.Module) )
        UpperCamelCase_									=     model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_UpperCAmelCase		,		nn.Linear ) )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     if not self.model_tester.is_training:
        return

     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     UpperCamelCase_									=     True

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

        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
        return

     UpperCamelCase_									=     False
     UpperCamelCase_									=     True

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

        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.gradient_checkpointing_enable()
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     UpperCamelCase_									=     _config_zero_init(_UpperCAmelCase )
     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(config=_UpperCAmelCase )
        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    _UpperCAmelCase     (     self )			-> Optional[Any]:
     for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     BeitModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


@require_torch
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  @cached_property
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None




  @slow
  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).pixel_values.to(_UpperCAmelCase )

     # prepare bool_masked_pos
     UpperCamelCase_									=     torch.ones((1, 196)		,		dtype=torch.bool ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(pixel_values=_UpperCAmelCase		,		bool_masked_pos=_UpperCAmelCase )
     UpperCamelCase_									=     outputs.logits

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 196, 8192) )
     self.assertEqual(logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor(
         [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]] ).to(_UpperCAmelCase )

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




  @slow
  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )
     UpperCamelCase_									=     outputs.logits

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 1000) )
     self.assertEqual(logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] ).to(_UpperCAmelCase )

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

     UpperCamelCase_									=     281
     self.assertEqual(logits.argmax(-1 ).item()		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
         _UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )
     UpperCamelCase_									=     outputs.logits

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 21841) )
     self.assertEqual(logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] ).to(_UpperCAmelCase )

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

     UpperCamelCase_									=     2396
     self.assertEqual(logits.argmax(-1 ).item()		,		_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
     UpperCamelCase_									=     model.to(_UpperCAmelCase )

     UpperCamelCase_									=     BeitImageProcessor(do_resize=_UpperCAmelCase		,		size=640		,		do_center_crop=_UpperCAmelCase )

     UpperCamelCase_									=     load_dataset('hf-internal-testing/fixtures_ade20k'		,		split='test' )
     UpperCamelCase_									=     Image.open(ds[0]['file'] )
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )
     UpperCamelCase_									=     outputs.logits

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 150, 160, 160) )
     self.assertEqual(logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     version.parse(PIL.__version__ ) < version.parse('9.0.0' )

     if is_pillow_less_than_a:
        UpperCamelCase_									=     torch.tensor(
            [
                [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
            ]		,		device=_UpperCAmelCase		,		)
     else:
        UpperCamelCase_									=     torch.tensor(
            [
                [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
            ]		,		device=_UpperCAmelCase		,		)

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




  @slow
  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
     UpperCamelCase_									=     model.to(_UpperCAmelCase )

     UpperCamelCase_									=     BeitImageProcessor(do_resize=_UpperCAmelCase		,		size=640		,		do_center_crop=_UpperCAmelCase )

     UpperCamelCase_									=     load_dataset('hf-internal-testing/fixtures_ade20k'		,		split='test' )
     UpperCamelCase_									=     Image.open(ds[0]['file'] )
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )

     UpperCamelCase_									=     outputs.logits.detach().cpu()

     UpperCamelCase_									=     image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase		,		target_sizes=[(500, 300)] )
     UpperCamelCase_									=     torch.Size((500, 300) )
     self.assertEqual(segmentation[0].shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase )
     UpperCamelCase_									=     torch.Size((160, 160) )
     self.assertEqual(segmentation[0].shape		,		_UpperCAmelCase )













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     ImageTextPipelineOutput,
     UniDiffuserPipeline,
 )
else:
 from .modeling_text_decoder import UniDiffuserTextDecoder
 from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
 from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline





