


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			:  Optional[int]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Optional[int]=100				,				lowerCamelCase__			:  str=13				,				lowerCamelCase__			:  Optional[int]=30				,				lowerCamelCase__			:  Union[str, Any]=2				,				lowerCamelCase__			:  Tuple=3				,				lowerCamelCase__			:  List[Any]=True				,				lowerCamelCase__			:  Tuple=True				,				lowerCamelCase__			:  int=32				,				lowerCamelCase__			:  Union[str, Any]=4				,				lowerCamelCase__			:  Dict=4				,				lowerCamelCase__			:  Union[str, Any]=37				,				lowerCamelCase__			:  List[Any]="gelu"				,				lowerCamelCase__			:  List[Any]=0.1				,				lowerCamelCase__			:  int=0.1				,				lowerCamelCase__			:  Union[str, Any]=10				,				lowerCamelCase__			:  str=0.02				,				lowerCamelCase__			:  Tuple=3				,				lowerCamelCase__			:  Dict=None				,				lowerCamelCase__			:  List[str]=[0, 1, 2, 3]				,				):
        a__	:  Dict     =   parent
        a__	:  Dict     =   100
        a__	:  Optional[int]     =   batch_size
        a__	:  Union[str, Any]     =   image_size
        a__	:  Any     =   patch_size
        a__	:  Optional[Any]     =   num_channels
        a__	:  int     =   is_training
        a__	:  List[str]     =   use_labels
        a__	:  Optional[Any]     =   hidden_size
        a__	:  List[Any]     =   num_hidden_layers
        a__	:  str     =   num_attention_heads
        a__	:  str     =   intermediate_size
        a__	:  int     =   hidden_act
        a__	:  List[Any]     =   hidden_dropout_prob
        a__	:  Dict     =   attention_probs_dropout_prob
        a__	:  Union[str, Any]     =   type_sequence_label_size
        a__	:  Optional[Any]     =   initializer_range
        a__	:  List[str]     =   scope
        a__	:  int     =   out_indices
        a__	:  List[str]     =   num_labels

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

  def    _UpperCamelCase( self			:  int       ):
        a__	:  Optional[int]     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

        a__	:  Optional[int]     =   self.get_config()

        return config, pixel_values, labels, pixel_labels

  def    _UpperCamelCase( self			:  Tuple       ):
        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=lowerCamelCase__				,				initializer_range=self.initializer_range				,				out_indices=self.out_indices				,				)

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Any       ):
        a__	:  str     =   BeitModel(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  List[str]     =   model(lowerCamelCase__       )
        self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)       )

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  Tuple       ):
        a__	:  int     =   BeitForMaskedImageModeling(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  List[Any]     =   model(lowerCamelCase__       )
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length - 1, self.vocab_size)       )

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  Any       ):
        a__	:  List[str]     =   self.type_sequence_label_size
        a__	:  Optional[Any]     =   BeitForImageClassification(lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  str     =   model(lowerCamelCase__				,				labels=lowerCamelCase__       )
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)       )

        # test greyscale images
        a__	:  Optional[Any]     =   1
        a__	:  List[str]     =   BeitForImageClassification(lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()

        a__	:  List[str]     =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        a__	:  Union[str, Any]     =   model(lowerCamelCase__				,				labels=lowerCamelCase__       )
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)       )

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Dict       ):
        a__	:  int     =   self.num_labels
        a__	:  List[str]     =   BeitForSemanticSegmentation(lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  Tuple     =   model(lowerCamelCase__       )
        self.parent.assertEqual(
            result.logits.shape				,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)       )
        a__	:  str     =   model(lowerCamelCase__				,				labels=lowerCamelCase__       )
        self.parent.assertEqual(
            result.logits.shape				,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Any     =   self.prepare_config_and_inputs()
        a__,	a__,	a__,	a__	:  Union[str, Any]     =   config_and_inputs
        a__	:  Dict     =   {"pixel_values": pixel_values}
        return config, inputs_dict




@require_torch
class A__     ( A__				,		A__				,		unittest.TestCase  ):



  """simple docstring"""



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

  _lowercase									=		False
  _lowercase									=		False
  _lowercase									=		False

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  int     =   BeitModelTester(self       )
        a__	:  Optional[Any]     =   ConfigTester(self				,				config_class=lowerCamelCase__				,				has_text_modality=lowerCamelCase__				,				hidden_size=37       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        self.config_tester.run_common_tests()

  @unittest.skip(reason="BEiT does not use inputs_embeds"       )
  def    _UpperCamelCase( self			:  str       ):
        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			:  Dict       ):
        pass

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__,	a__	:  Optional[Any]     =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              a__	:  List[str]     =   model_class(lowerCamelCase__       )
              self.assertIsInstance(model.get_input_embeddings()				,				(nn.Module)       )
              a__	:  Optional[int]     =   model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCamelCase__				,				nn.Linear       )       )

  def    _UpperCamelCase( self			:  str       ):
        a__,	a__	:  Union[str, Any]     =   self.model_tester.prepare_config_and_inputs_for_common()

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

              a__	:  Any     =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Dict     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int       ):
        a__	:  int     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[int]     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Union[str, Any]     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        if not self.model_tester.is_training:
              return

        a__,	a__	:  Tuple     =   self.model_tester.prepare_config_and_inputs_for_common()
        a__	:  str     =   True

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

              a__	:  List[str]     =   model_class(lowerCamelCase__       )
              model.to(lowerCamelCase__       )
              model.train()
              a__	:  Any     =   self._prepare_for_class(lowerCamelCase__				,				lowerCamelCase__				,				return_labels=lowerCamelCase__       )
              a__	:  Tuple     =   model(**lowerCamelCase__       ).loss
              loss.backward()

  def    _UpperCamelCase( self			:  Tuple       ):
        a__,	a__	:  Optional[Any]     =   self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
              return

        a__	:  List[Any]     =   False
        a__	:  List[str]     =   True

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

              a__	:  Optional[Any]     =   model_class(lowerCamelCase__       )
              model.gradient_checkpointing_enable()
              model.to(lowerCamelCase__       )
              model.train()
              a__	:  Union[str, Any]     =   self._prepare_for_class(lowerCamelCase__				,				lowerCamelCase__				,				return_labels=lowerCamelCase__       )
              a__	:  int     =   model(**lowerCamelCase__       ).loss
              loss.backward()

  def    _UpperCamelCase( self			:  List[str]       ):
        a__,	a__	:  Tuple     =   self.model_tester.prepare_config_and_inputs_for_common()

        a__	:  Dict     =   _config_zero_init(lowerCamelCase__       )
        for model_class in self.all_model_classes:
              a__	:  str     =   model_class(config=lowerCamelCase__       )
              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[int]       ):
        for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a__	:  Tuple     =   BeitModel.from_pretrained(lowerCamelCase__       )
              self.assertIsNotNone(lowerCamelCase__       )




def UpperCamelCase_				(				)	->   Any:
      a__	:  List[Any]     =   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[int]       ):
        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"       ) if is_vision_available() else None

  @slow
  def    _UpperCamelCase( self			:  str       ):
        a__	:  int     =   BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"       ).to(lowerCamelCase__       )

        a__	:  Optional[Any]     =   self.default_image_processor
        a__	:  Dict     =   prepare_img()
        a__	:  Optional[int]     =   image_processor(images=lowerCamelCase__				,				return_tensors="pt"       ).pixel_values.to(lowerCamelCase__       )

        # prepare bool_masked_pos
        a__	:  Optional[Any]     =   torch.ones((1, 196)				,				dtype=torch.bool       ).to(lowerCamelCase__       )

        # forward pass
        with torch.no_grad():
              a__	:  Any     =   model(pixel_values=lowerCamelCase__				,				bool_masked_pos=lowerCamelCase__       )
        a__	:  Tuple     =   outputs.logits

        # verify the logits
        a__	:  List[str]     =   torch.Size((1, 196, 8_192)       )
        self.assertEqual(logits.shape				,				lowerCamelCase__       )

        a__	:  Optional[int]     =   torch.tensor(
            [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]       ).to(lowerCamelCase__       )

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

  @slow
  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  str     =   BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"       ).to(lowerCamelCase__       )

        a__	:  int     =   self.default_image_processor
        a__	:  List[Any]     =   prepare_img()
        a__	:  Tuple     =   image_processor(images=lowerCamelCase__				,				return_tensors="pt"       ).to(lowerCamelCase__       )

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

        # verify the logits
        a__	:  Union[str, Any]     =   torch.Size((1, 1_000)       )
        self.assertEqual(logits.shape				,				lowerCamelCase__       )

        a__	:  int     =   torch.tensor([-1.2385, -1.0987, -1.0108]       ).to(lowerCamelCase__       )

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

        a__	:  Tuple     =   281
        self.assertEqual(logits.argmax(-1       ).item()				,				lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Dict     =   BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"       ).to(
            lowerCamelCase__       )

        a__	:  str     =   self.default_image_processor
        a__	:  List[str]     =   prepare_img()
        a__	:  Tuple     =   image_processor(images=lowerCamelCase__				,				return_tensors="pt"       ).to(lowerCamelCase__       )

        # forward pass
        with torch.no_grad():
              a__	:  Dict     =   model(**lowerCamelCase__       )
        a__	:  List[str]     =   outputs.logits

        # verify the logits
        a__	:  Optional[int]     =   torch.Size((1, 21_841)       )
        self.assertEqual(logits.shape				,				lowerCamelCase__       )

        a__	:  Optional[Any]     =   torch.tensor([1.6881, -0.2787, 0.5901]       ).to(lowerCamelCase__       )

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

        a__	:  Optional[Any]     =   2_396
        self.assertEqual(logits.argmax(-1       ).item()				,				lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  int       ):
        a__	:  Optional[Any]     =   BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"       )
        a__	:  Tuple     =   model.to(lowerCamelCase__       )

        a__	:  List[Any]     =   BeitImageProcessor(do_resize=lowerCamelCase__				,				size=640				,				do_center_crop=lowerCamelCase__       )

        a__	:  Tuple     =   load_dataset("hf-internal-testing/fixtures_ade20k"				,				split="test"       )
        a__	:  Union[str, Any]     =   Image.open(ds[0]["file"]       )
        a__	:  List[Any]     =   image_processor(images=lowerCamelCase__				,				return_tensors="pt"       ).to(lowerCamelCase__       )

        # forward pass
        with torch.no_grad():
              a__	:  Optional[Any]     =   model(**lowerCamelCase__       )
        a__	:  List[str]     =   outputs.logits

        # verify the logits
        a__	:  Tuple     =   torch.Size((1, 150, 160, 160)       )
        self.assertEqual(logits.shape				,				lowerCamelCase__       )

        a__	:  int     =   version.parse(PIL.__version__       ) < version.parse("9.0.0"       )

        if is_pillow_less_than_a:
              a__	:  Dict     =   torch.tensor(
                  [
                      [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                      [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                      [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                  ]				,				device=lowerCamelCase__				,				)
        else:
              a__	:  Dict     =   torch.tensor(
                  [
                      [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                      [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                      [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
                  ]				,				device=lowerCamelCase__				,				)

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

  @slow
  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  str     =   BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"       )
        a__	:  List[Any]     =   model.to(lowerCamelCase__       )

        a__	:  int     =   BeitImageProcessor(do_resize=lowerCamelCase__				,				size=640				,				do_center_crop=lowerCamelCase__       )

        a__	:  Optional[int]     =   load_dataset("hf-internal-testing/fixtures_ade20k"				,				split="test"       )
        a__	:  str     =   Image.open(ds[0]["file"]       )
        a__	:  str     =   image_processor(images=lowerCamelCase__				,				return_tensors="pt"       ).to(lowerCamelCase__       )

        # forward pass
        with torch.no_grad():
              a__	:  List[Any]     =   model(**lowerCamelCase__       )

        a__	:  Any     =   outputs.logits.detach().cpu()

        a__	:  List[Any]     =   image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__				,				target_sizes=[(500, 300)]       )
        a__	:  Optional[int]     =   torch.Size((500, 300)       )
        self.assertEqual(segmentation[0].shape				,				lowerCamelCase__       )

        a__	:  List[str]     =   image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__       )
        a__	:  Any     =   torch.Size((160, 160)       )
        self.assertEqual(segmentation[0].shape				,				lowerCamelCase__       )









class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





