




"""simple docstring"""



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_  :




    def __init__(   self						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=100     ,			snake_case_						:	Any=13     ,			snake_case_						:	Optional[int]=30     ,			snake_case_						:	str=2     ,			snake_case_						:	int=3     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	int=True     ,			snake_case_						:	Optional[Any]=32     ,			snake_case_						:	str=4     ,			snake_case_						:	List[Any]=4     ,			snake_case_						:	Any=37     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Optional[int]=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Tuple=10     ,			snake_case_						:	Any=0.02     ,			snake_case_						:	Any=3     ,			snake_case_						:	Tuple=None     ,			snake_case_						:	str=[0, 1, 2, 3]     ,			)				->							int:




     '''simple docstring'''

     A__	=    parent
     A__	=    100
     A__	=    batch_size
     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    is_training
     A__	=    use_labels
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    type_sequence_label_size
     A__	=    initializer_range
     A__	=    scope
     A__	=    out_indices
     A__	=    num_labels

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




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     A__	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

     A__	=    self.get_config()

     return config, pixel_values, labels, pixel_labels




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''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=UpperCamelCase_     ,			initializer_range=self.initializer_range     ,			out_indices=self.out_indices     ,			)




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	int				)				->							List[str]:




     '''simple docstring'''

     A__	=    BeitModel(config=UpperCamelCase_				)
     model.to(UpperCamelCase_				)
     model.eval()
     A__	=    model(UpperCamelCase_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Dict				)				->							Any:




     '''simple docstring'''

     A__	=    BeitForMaskedImageModeling(config=UpperCamelCase_				)
     model.to(UpperCamelCase_				)
     model.eval()
     A__	=    model(UpperCamelCase_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length - 1, self.vocab_size)				)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[Any]     ,			snake_case_						:	str     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	List[Any]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.type_sequence_label_size
     A__	=    BeitForImageClassification(UpperCamelCase_				)
     model.to(UpperCamelCase_				)
     model.eval()
     A__	=    model(UpperCamelCase_     ,			labels=UpperCamelCase_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)				)

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

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




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]				)				->							Optional[int]:




     '''simple docstring'''

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




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     A__,				A__,				A__,				A__	=    config_and_inputs
     A__	=    {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						UpperCamelCase__,       UpperCamelCase__,       unittest.TestCase							):
    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 							__magic_name__							(   self						:	Optional[int]				)				->							Any:




     '''simple docstring'''

     A__	=    BeitModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=UpperCamelCase_     ,			has_text_modality=UpperCamelCase_     ,			hidden_size=37				)




    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    @unittest.skip(reason="BEiT does not use inputs_embeds"				)
    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[int]:




     '''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 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Optional[int]				)				->							List[str]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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

      A__	=    ["pixel_values"]
      self.assertListEqual(arg_names[:1]     ,			UpperCamelCase_				)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCamelCase_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_				)




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     if not self.model_tester.is_training:
      return

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

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

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




    def 							__magic_name__							(   self						:	List[str]				)				->							List[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
      return

     A__	=    False
     A__	=    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

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							List[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     A__	=    _config_zero_init(UpperCamelCase_				)
     for model_class in self.all_model_classes:
      A__	=    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 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




     '''simple docstring'''

     for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    BeitModel.from_pretrained(UpperCamelCase_				)
      self.assertIsNotNone(UpperCamelCase_				)







def   _SCREAMING_SNAKE_CASE       (							)						->		List[str]:
 A__	=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
 return image






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




    @cached_property
    def 							__magic_name__							(   self						:	List[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

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




    @slow
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"				).to(UpperCamelCase_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=UpperCamelCase_     ,			return_tensors="pt"				).pixel_values.to(UpperCamelCase_				)

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

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

     # verify the logits
     A__	=    torch.Size((1, 196, 8_192)				)
     self.assertEqual(logits.shape     ,			UpperCamelCase_				)

     A__	=    torch.tensor(
         [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]				).to(UpperCamelCase_				)

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




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"				).to(UpperCamelCase_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=UpperCamelCase_     ,			return_tensors="pt"				).to(UpperCamelCase_				)

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

     # verify the logits
     A__	=    torch.Size((1, 1_000)				)
     self.assertEqual(logits.shape     ,			UpperCamelCase_				)

     A__	=    torch.tensor([-1.2385, -1.0987, -1.0108]				).to(UpperCamelCase_				)

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

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




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"				).to(
         UpperCamelCase_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=UpperCamelCase_     ,			return_tensors="pt"				).to(UpperCamelCase_				)

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

     # verify the logits
     A__	=    torch.Size((1, 21_841)				)
     self.assertEqual(logits.shape     ,			UpperCamelCase_				)

     A__	=    torch.tensor([1.6881, -0.2787, 0.5901]				).to(UpperCamelCase_				)

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

     A__	=    2_396
     self.assertEqual(logits.argmax(-1				).item()     ,			UpperCamelCase_				)




    @slow
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"				)
     A__	=    model.to(UpperCamelCase_				)

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

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

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

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

     A__	=    version.parse(PIL.__version__				) < version.parse("9.0.0"				)

     if is_pillow_less_than_a:
      A__	=    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=UpperCamelCase_     ,			)
     else:
      A__	=    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=UpperCamelCase_     ,			)

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




    @slow
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"				)
     A__	=    model.to(UpperCamelCase_				)

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

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

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

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

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

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











"""simple docstring"""



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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





