



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 SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






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

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






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        A :       int          =   self.get_config()

        return config, pixel_values, labels, pixel_labels






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






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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Tuple          =   self.type_sequence_label_size
        A :       List[str]          =   BeitForImageClassification(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A :       List[str]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		)
        self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A :       Dict          =   1
        A :       Tuple          =   BeitForImageClassification(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Tuple          =   self.num_labels
        A :       Union[str, Any]          =   BeitForSemanticSegmentation(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A :       Union[str, Any]          =   model(lowerCamelCase__		)
        self.parent.assertEqual(
            result.logits.shape,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
        A :       Union[str, Any]          =   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 			_lowerCAmelCase					(       self		):
        A :       int          =   self.prepare_config_and_inputs()
        A     ,							A     ,							A     ,							A :       Dict          =   config_and_inputs
        A :       Union[str, Any]          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict






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




    '''simple docstring'''






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

    __lowerCamelCase			: Dict					=      False
    __lowerCamelCase			: Optional[Any]					=      False
    __lowerCamelCase			: int					=      False






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   BeitModelTester(self		)
        A :       List[Any]          =   ConfigTester(self,		config_class=lowerCamelCase__,		has_text_modality=lowerCamelCase__,		hidden_size=37		)






    def 			_lowerCAmelCase					(       self		):
        self.config_tester.run_common_tests()






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






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






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       List[str]          =   self.model_tester.prepare_config_and_inputs_for_common()

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






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Optional[int]          =   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 :       List[str]          =   [*signature.parameters.keys()]

            A :       List[str]          =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1],		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if not self.model_tester.is_training:
            return

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






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Optional[int]          =   self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
            return

        A :       str          =   False
        A :       Optional[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

            A :       Tuple          =   model_class(lowerCamelCase__		)
            model.gradient_checkpointing_enable()
            model.to(lowerCamelCase__		)
            model.train()
            A :       Tuple          =   self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__,		return_labels=lowerCamelCase__		)
            A :       int          =   model(**lowerCamelCase__		).loss
            loss.backward()






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Tuple          =   self.model_tester.prepare_config_and_inputs_for_common()

        A :       Union[str, Any]          =   _config_zero_init(lowerCamelCase__		)
        for model_class in self.all_model_classes:
            A :       Tuple          =   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 			_lowerCAmelCase					(       self		):
        for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A :       Union[str, Any]          =   BeitModel.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)




def 	__UpperCamelCase	(  )       ->  Optional[int]:



    """simple docstring"""






    A :       List[Any]          =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
    return image






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




    '''simple docstring'''






    @cached_property
    def 			_lowerCAmelCase					(       self		):
        return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""		) if is_vision_available() else None






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

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

        # prepare bool_masked_pos
        A :       Any          =   torch.ones((1, 196),		dtype=torch.bool		).to(lowerCamelCase__		)

        # forward pass
        with torch.no_grad():
            A :       str          =   model(pixel_values=lowerCamelCase__,		bool_masked_pos=lowerCamelCase__		)
        A :       str          =   outputs.logits

        # verify the logits
        A :       Any          =   torch.Size((1, 196, 8192)		)
        self.assertEqual(logits.shape,		lowerCamelCase__		)

        A :       Dict          =   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 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""		).to(lowerCamelCase__		)

        A :       Optional[int]          =   self.default_image_processor
        A :       Union[str, 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 :       Dict          =   outputs.logits

        # verify the logits
        A :       int          =   torch.Size((1, 1000)		)
        self.assertEqual(logits.shape,		lowerCamelCase__		)

        A :       Dict          =   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 			_lowerCAmelCase					(       self		):
        A :       str          =   BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""		).to(
            lowerCamelCase__		)

        A :       List[str]          =   self.default_image_processor
        A :       Dict          =   prepare_img()
        A :       Optional[Any]          =   image_processor(images=lowerCamelCase__,		return_tensors="""pt"""		).to(lowerCamelCase__		)

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

        # verify the logits
        A :       Optional[Any]          =   torch.Size((1, 2_1841)		)
        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 :       List[str]          =   2396
        self.assertEqual(logits.argmax(-1		).item(),		lowerCamelCase__		)






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

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

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

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

        # verify the logits
        A :       List[Any]          =   torch.Size((1, 150, 160, 160)		)
        self.assertEqual(logits.shape,		lowerCamelCase__		)

        A :       Optional[int]          =   version.parse(PIL.__version__		) < version.parse("""9.0.0"""		)

        if is_pillow_less_than_a:
            A :       Union[str, Any]          =   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 :       List[str]          =   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 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""		)
        A :       int          =   model.to(lowerCamelCase__		)

        A :       Dict          =   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 :       List[Any]          =   image_processor(images=lowerCamelCase__,		return_tensors="""pt"""		).to(lowerCamelCase__		)

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

        A :       List[Any]          =   outputs.logits.detach().cpu()

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

        A :       Any          =   image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__		)
        A :       Optional[int]          =   torch.Size((160, 160)		)
        self.assertEqual(segmentation[0].shape,		lowerCamelCase__		)







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_:List[Any]             =						TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict             =						TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__ ,						slots=SCREAMING_SNAKE_CASE__    )
class SCREAMING_SNAKE_CASE__     (		Generic[KEY, VAL]    ):




    '''simple docstring'''






    __lowerCamelCase			: KEY
    __lowerCamelCase			: VAL






class SCREAMING_SNAKE_CASE__     (		_Item    ):




    '''simple docstring'''






    def __init__(       self		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)






    def __bool__(       self		):
        return False


SCREAMING_SNAKE_CASE_:Optional[Any]             =						_DeletedItem()






class SCREAMING_SNAKE_CASE__     (		MutableMapping[KEY, VAL]    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 8,		lowerCamelCase__ = 0.75		):
        A :       List[str]          =   initial_block_size
        A :       list[_Item | None]          =   [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        A :       Dict          =   capacity_factor
        A :       Optional[Any]          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return hash(lowerCamelCase__		) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return (ind + 1) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       List[Any]          =   self._buckets[ind]
        if not stored:
            A :       Optional[int]          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            self._len += 1
            return True
        elif stored.key == key:
            A :       int          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            return True
        else:
            return False






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if len(self._buckets		) <= self._initial_block_size:
            return False
        A :       Optional[Any]          =   len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Dict          =   self._buckets
        A :       Optional[Any]          =   [None] * new_size
        A :       List[str]          =   0
        for item in old_buckets:
            if item:
                self._add_item(item.key,		item.val		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) * 2		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) // 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Union[str, Any]          =   self._get_bucket_index(lowerCamelCase__		)
        for _ in range(len(self._buckets		)		):
            yield ind
            A :       Dict          =   self._get_next_ind(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            if self._try_set(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                break






    def __setitem__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self._is_full():
            self._size_up()

        self._add_item(lowerCamelCase__,		lowerCamelCase__		)






    def __delitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       Tuple          =   self._buckets[ind]
            if item is None:
                raise KeyError(lowerCamelCase__		)
            if item is _deleted:
                continue
            if item.key == key:
                A :       int          =   _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()






    def __getitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       int          =   self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(lowerCamelCase__		)






    def __len__(       self		):
        return self._len






    def __iter__(       self		):
        yield from (item.key for item in self._buckets if item)






    def __repr__(       self		):
        A :       Union[str, Any]          =   """ ,""".join(
            f'''{item.key}: {item.val}''' for item in self._buckets if item		)
        return f'''HashMap({val_string})'''


