


'''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							:	Union[str, Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Any=1_0_0			,       __lowerCamelCase							:	int=1_3			,       __lowerCamelCase							:	Optional[int]=3_0			,       __lowerCamelCase							:	Optional[int]=2			,       __lowerCamelCase							:	List[str]=3			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	List[Any]=3_2			,       __lowerCamelCase							:	Dict=4			,       __lowerCamelCase							:	List[Any]=4			,       __lowerCamelCase							:	List[Any]=3_7			,       __lowerCamelCase							:	Optional[Any]="gelu"			,       __lowerCamelCase							:	int=0.1			,       __lowerCamelCase							:	Union[str, Any]=0.1			,       __lowerCamelCase							:	Any=1_0			,       __lowerCamelCase							:	Any=0.02			,       __lowerCamelCase							:	List[Any]=3			,       __lowerCamelCase							:	List[str]=None			,       __lowerCamelCase							:	Any=[0, 1, 2, 3]			,       ):
    UpperCAmelCase__	:str      =      parent
    UpperCAmelCase__	:Optional[int]      =      1_0_0
    UpperCAmelCase__	:str      =      batch_size
    UpperCAmelCase__	:Any      =      image_size
    UpperCAmelCase__	:List[str]      =      patch_size
    UpperCAmelCase__	:Union[str, Any]      =      num_channels
    UpperCAmelCase__	:Optional[Any]      =      is_training
    UpperCAmelCase__	:List[str]      =      use_labels
    UpperCAmelCase__	:Any      =      hidden_size
    UpperCAmelCase__	:int      =      num_hidden_layers
    UpperCAmelCase__	:Optional[int]      =      num_attention_heads
    UpperCAmelCase__	:Union[str, Any]      =      intermediate_size
    UpperCAmelCase__	:Dict      =      hidden_act
    UpperCAmelCase__	:Tuple      =      hidden_dropout_prob
    UpperCAmelCase__	:Union[str, Any]      =      attention_probs_dropout_prob
    UpperCAmelCase__	:Tuple      =      type_sequence_label_size
    UpperCAmelCase__	:Union[str, Any]      =      initializer_range
    UpperCAmelCase__	:str      =      scope
    UpperCAmelCase__	:List[str]      =      out_indices
    UpperCAmelCase__	:int      =      num_labels

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Optional[Any]      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

    UpperCAmelCase__	:List[Any]      =      None
    UpperCAmelCase__	:List[str]      =      None
    if self.use_labels:
       UpperCAmelCase__	:Tuple      =      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__	:Dict      =      self.get_config()

    return config, pixel_values, labels, pixel_labels


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[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=__lowerCamelCase			,       initializer_range=self.initializer_range			,       out_indices=self.out_indices			,       )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	List[str] ):
    UpperCAmelCase__	:Optional[Any]      =      BeitModel(config=__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:Dict      =      model(__lowerCamelCase )
    self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	List[str] ):
    UpperCAmelCase__	:Optional[Any]      =      BeitForMaskedImageModeling(config=__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:List[Any]      =      model(__lowerCamelCase )
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.seq_length - 1, self.vocab_size) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	List[Any] ):
    UpperCAmelCase__	:Dict      =      self.type_sequence_label_size
    UpperCAmelCase__	:List[str]      =      BeitForImageClassification(__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:Any      =      model(__lowerCamelCase			,       labels=__lowerCamelCase )
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.type_sequence_label_size) )

    # test greyscale images
    UpperCAmelCase__	:Dict      =      1
    UpperCAmelCase__	:List[Any]      =      BeitForImageClassification(__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()

    UpperCAmelCase__	:Union[str, Any]      =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
    UpperCAmelCase__	:Optional[int]      =      model(__lowerCamelCase			,       labels=__lowerCamelCase )
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.type_sequence_label_size) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	Optional[Any] ):
    UpperCAmelCase__	:Tuple      =      self.num_labels
    UpperCAmelCase__	:Optional[Any]      =      BeitForSemanticSegmentation(__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:Dict      =      model(__lowerCamelCase )
    self.parent.assertEqual(
        result.logits.shape			,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
    UpperCAmelCase__	:Optional[int]      =      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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Optional[Any]      =      self.prepare_config_and_inputs()
    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      config_and_inputs
    UpperCAmelCase__	:Optional[int]      =      {'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_torch
class 				UpperCAmelCase			(			_snake_case						, _snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					(
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 UpperCAmelCase				=					(
     {
         "feature-extraction": BeitModel,
         "image-classification": BeitForImageClassification,
         "image-segmentation": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase				=					False
 UpperCAmelCase				=					False
 UpperCAmelCase				=					False


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:List[str]      =      BeitModelTester(self )
    UpperCAmelCase__	:Tuple      =      ConfigTester(self			,       config_class=__lowerCamelCase			,       has_text_modality=__lowerCamelCase			,       hidden_size=3_7 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    self.config_tester.run_common_tests()


 @unittest.skip(reason='''BEiT does not use inputs_embeds''' )
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[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        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    pass


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self.model_tester.prepare_config_and_inputs_for_common()

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase__	:List[Any]      =      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Tuple      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:int      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Optional[int]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase )


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

    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      self.model_tester.prepare_config_and_inputs_for_common()
    UpperCAmelCase__	:int      =      True

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      self.model_tester.prepare_config_and_inputs_for_common()
    if not self.model_tester.is_training:
       return

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

       UpperCAmelCase__	:List[Any]      =      model_class(__lowerCamelCase )
       model.gradient_checkpointing_enable()
       model.to(__lowerCamelCase )
       model.train()
       UpperCAmelCase__	:Dict      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase			,       return_labels=__lowerCamelCase )
       UpperCAmelCase__	:int      =      model(**__lowerCamelCase ).loss
       loss.backward()


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__	:List[Any]      =      _config_zero_init(__lowerCamelCase )
    for model_class in self.all_model_classes:
       UpperCAmelCase__	:List[Any]      =      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        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase__	:Union[str, Any]      =      BeitModel.from_pretrained(__lowerCamelCase )
       self.assertIsNotNone(__lowerCamelCase )


def        a__     (					):
   UpperCAmelCase__	:Tuple      =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
   return image




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


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


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    UpperCAmelCase__	:Any      =      BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' ).to(__lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      self.default_image_processor
    UpperCAmelCase__	:Dict      =      prepare_img()
    UpperCAmelCase__	:List[Any]      =      image_processor(images=__lowerCamelCase			,       return_tensors='''pt''' ).pixel_values.to(__lowerCamelCase )

    # prepare bool_masked_pos
    UpperCAmelCase__	:Tuple      =      torch.ones((1, 1_9_6)			,       dtype=torch.bool ).to(__lowerCamelCase )

    # forward pass
    with torch.no_grad():
       UpperCAmelCase__	:Union[str, Any]      =      model(pixel_values=__lowerCamelCase			,       bool_masked_pos=__lowerCamelCase )
    UpperCAmelCase__	:str      =      outputs.logits

    # verify the logits
    UpperCAmelCase__	:List[str]      =      torch.Size((1, 1_9_6, 8_1_9_2) )
    self.assertEqual(logits.shape			,       __lowerCamelCase )

    UpperCAmelCase__	:Any      =      torch.tensor(
        [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] ).to(__lowerCamelCase )

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


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' ).to(__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      self.default_image_processor
    UpperCAmelCase__	:int      =      prepare_img()
    UpperCAmelCase__	:Union[str, Any]      =      image_processor(images=__lowerCamelCase			,       return_tensors='''pt''' ).to(__lowerCamelCase )

    # forward pass
    with torch.no_grad():
       UpperCAmelCase__	:Dict      =      model(**__lowerCamelCase )
    UpperCAmelCase__	:List[Any]      =      outputs.logits

    # verify the logits
    UpperCAmelCase__	:List[Any]      =      torch.Size((1, 1_0_0_0) )
    self.assertEqual(logits.shape			,       __lowerCamelCase )

    UpperCAmelCase__	:str      =      torch.tensor([-1.23_85, -1.09_87, -1.01_08] ).to(__lowerCamelCase )

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

    UpperCAmelCase__	:Union[str, Any]      =      2_8_1
    self.assertEqual(logits.argmax(-1 ).item()			,       __lowerCamelCase )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Union[str, Any]      =      BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' ).to(
        __lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      self.default_image_processor
    UpperCAmelCase__	:List[Any]      =      prepare_img()
    UpperCAmelCase__	:Union[str, Any]      =      image_processor(images=__lowerCamelCase			,       return_tensors='''pt''' ).to(__lowerCamelCase )

    # forward pass
    with torch.no_grad():
       UpperCAmelCase__	:Any      =      model(**__lowerCamelCase )
    UpperCAmelCase__	:List[Any]      =      outputs.logits

    # verify the logits
    UpperCAmelCase__	:List[str]      =      torch.Size((1, 2_1_8_4_1) )
    self.assertEqual(logits.shape			,       __lowerCamelCase )

    UpperCAmelCase__	:str      =      torch.tensor([1.68_81, -0.27_87, 0.59_01] ).to(__lowerCamelCase )

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

    UpperCAmelCase__	:Tuple      =      2_3_9_6
    self.assertEqual(logits.argmax(-1 ).item()			,       __lowerCamelCase )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:Optional[Any]      =      BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
    UpperCAmelCase__	:Tuple      =      model.to(__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      BeitImageProcessor(do_resize=__lowerCamelCase			,       size=6_4_0			,       do_center_crop=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      load_dataset('''hf-internal-testing/fixtures_ade20k'''			,       split='''test''' )
    UpperCAmelCase__	:Any      =      Image.open(ds[0]['''file'''] )
    UpperCAmelCase__	:Any      =      image_processor(images=__lowerCamelCase			,       return_tensors='''pt''' ).to(__lowerCamelCase )

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

    # verify the logits
    UpperCAmelCase__	:Union[str, Any]      =      torch.Size((1, 1_5_0, 1_6_0, 1_6_0) )
    self.assertEqual(logits.shape			,       __lowerCamelCase )

    UpperCAmelCase__	:str      =      version.parse(PIL.__version__ ) < version.parse('''9.0.0''' )

    if is_pillow_less_than_a:
       UpperCAmelCase__	:int      =      torch.tensor(
           [
               [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
               [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
               [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
           ]			,       device=__lowerCamelCase			,       )
    else:
       UpperCAmelCase__	:str      =      torch.tensor(
           [
               [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
               [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
               [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
           ]			,       device=__lowerCamelCase			,       )

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





 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Optional[int]      =      BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
    UpperCAmelCase__	:int      =      model.to(__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      BeitImageProcessor(do_resize=__lowerCamelCase			,       size=6_4_0			,       do_center_crop=__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      load_dataset('''hf-internal-testing/fixtures_ade20k'''			,       split='''test''' )
    UpperCAmelCase__	:Any      =      Image.open(ds[0]['''file'''] )
    UpperCAmelCase__	:Union[str, Any]      =      image_processor(images=__lowerCamelCase			,       return_tensors='''pt''' ).to(__lowerCamelCase )

    # forward pass
    with torch.no_grad():
       UpperCAmelCase__	:Dict      =      model(**__lowerCamelCase )

    UpperCAmelCase__	:str      =      outputs.logits.detach().cpu()

    UpperCAmelCase__	:List[Any]      =      image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase			,       target_sizes=[(5_0_0, 3_0_0)] )
    UpperCAmelCase__	:Optional[int]      =      torch.Size((5_0_0, 3_0_0) )
    self.assertEqual(segmentation[0].shape			,       __lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase )
    UpperCAmelCase__	:Union[str, Any]      =      torch.Size((1_6_0, 1_6_0) )
    self.assertEqual(segmentation[0].shape			,       __lowerCamelCase )







'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

    def _delete(__lowerCamelCase							:	TrieNode			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int ) -> bool:
       if index == len(__lowerCamelCase ):
          # If word does not exist
          if not curr.is_leaf:
             return False
          UpperCAmelCase__	:int      =      False
          return len(curr.nodes ) == 0
       UpperCAmelCase__	:str      =      word[index]
       UpperCAmelCase__	:Optional[Any]      =      curr.nodes.get(__lowerCamelCase )
       # If char not in current trie node
       if not char_node:
          return False
       # Flag to check if node can be deleted
       UpperCAmelCase__	:Any      =      _delete(__lowerCamelCase			,       __lowerCamelCase			,       index + 1 )
       if delete_curr:
          del curr.nodes[char]
          return len(curr.nodes ) == 0
       return delete_curr

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

   for key, value in node.nodes.items():
      print_words(UpperCamelCase_,   word + key						)


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) for word in words						)
   assert root.find('''banana'''						)
   assert not root.find('''bandanas'''						)
   assert not root.find('''apps'''						)
   assert root.find('''apple'''						)
   assert root.find('''all'''						)
   root.delete('''all'''						)
   assert not root.find('''all'''						)
   root.delete('''banana'''						)
   assert not root.find('''banana'''						)
   assert root.find('''bananas'''						)
   return True


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



