


'''simple docstring'''



import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          LayoutLMvaConfig,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaModel,
      )
      from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import LayoutLMvaImageProcessor



class 			a       :


     """simple docstring"""






     def __init__(       self  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	Any=2							,				snake_case  :	int=3							,				snake_case  :	int=4							,				snake_case  :	List[Any]=2							,				snake_case  :	List[Any]=7							,				snake_case  :	Tuple=True							,				snake_case  :	List[Any]=True							,				snake_case  :	int=True							,				snake_case  :	int=True							,				snake_case  :	str=99							,				snake_case  :	int=36							,				snake_case  :	List[str]=3							,				snake_case  :	Any=4							,				snake_case  :	Union[str, Any]=37							,				snake_case  :	Optional[int]="gelu"							,				snake_case  :	int=0.1							,				snake_case  :	Optional[int]=0.1							,				snake_case  :	Union[str, Any]=512							,				snake_case  :	Dict=16							,				snake_case  :	str=2							,				snake_case  :	Optional[int]=0.02							,				snake_case  :	Union[str, Any]=6							,				snake_case  :	str=6							,				snake_case  :	List[str]=3							,				snake_case  :	List[Any]=4							,				snake_case  :	Optional[Any]=None							,				snake_case  :	Optional[int]=1000							,				)		->							str:
          __UpperCAmelCase			:				Tuple													=    parent
          __UpperCAmelCase			:				Tuple													=    batch_size
          __UpperCAmelCase			:				Tuple													=    num_channels
          __UpperCAmelCase			:				Optional[Any]													=    image_size
          __UpperCAmelCase			:				Optional[Any]													=    patch_size
          __UpperCAmelCase			:				List[str]													=    text_seq_length
          __UpperCAmelCase			:				Dict													=    is_training
          __UpperCAmelCase			:				Optional[int]													=    use_input_mask
          __UpperCAmelCase			:				List[str]													=    use_token_type_ids
          __UpperCAmelCase			:				Dict													=    use_labels
          __UpperCAmelCase			:				List[Any]													=    vocab_size
          __UpperCAmelCase			:				List[str]													=    hidden_size
          __UpperCAmelCase			:				List[str]													=    num_hidden_layers
          __UpperCAmelCase			:				Any													=    num_attention_heads
          __UpperCAmelCase			:				Optional[int]													=    intermediate_size
          __UpperCAmelCase			:				Dict													=    hidden_act
          __UpperCAmelCase			:				Optional[int]													=    hidden_dropout_prob
          __UpperCAmelCase			:				str													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				List[Any]													=    max_position_embeddings
          __UpperCAmelCase			:				int													=    type_vocab_size
          __UpperCAmelCase			:				int													=    type_sequence_label_size
          __UpperCAmelCase			:				Tuple													=    initializer_range
          __UpperCAmelCase			:				Dict													=    coordinate_size
          __UpperCAmelCase			:				str													=    shape_size
          __UpperCAmelCase			:				Any													=    num_labels
          __UpperCAmelCase			:				Optional[Any]													=    num_choices
          __UpperCAmelCase			:				List[Any]													=    scope
          __UpperCAmelCase			:				str													=    range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          __UpperCAmelCase			:				Optional[Any]													=    text_seq_length
          __UpperCAmelCase			:				Dict													=    (image_size // patch_size) ** 2 + 1
          __UpperCAmelCase			:				Tuple													=    self.text_seq_length + self.image_seq_length

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							List[str]:
          __UpperCAmelCase			:				int													=    ids_tensor([self.batch_size, self.text_seq_length]							,				self.vocab_size    )

          __UpperCAmelCase			:				Union[str, Any]													=    ids_tensor([self.batch_size, self.text_seq_length, 4]							,				self.range_bbox    )
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]    ):
               for j in range(bbox.shape[1]    ):
                    if bbox[i, j, 3] < bbox[i, j, 1]:
                         __UpperCAmelCase			:				Dict													=    bbox[i, j, 3]
                         __UpperCAmelCase			:				List[Any]													=    bbox[i, j, 1]
                         __UpperCAmelCase			:				str													=    t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         __UpperCAmelCase			:				Any													=    bbox[i, j, 2]
                         __UpperCAmelCase			:				List[str]													=    bbox[i, j, 0]
                         __UpperCAmelCase			:				Union[str, Any]													=    t

          __UpperCAmelCase			:				str													=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

          __UpperCAmelCase			:				str													=    None
          if self.use_input_mask:
               __UpperCAmelCase			:				Tuple													=    random_attention_mask([self.batch_size, self.text_seq_length]    )

          __UpperCAmelCase			:				Optional[Any]													=    None
          if self.use_token_type_ids:
               __UpperCAmelCase			:				List[Any]													=    ids_tensor([self.batch_size, self.text_seq_length]							,				self.type_vocab_size    )

          __UpperCAmelCase			:				Union[str, Any]													=    None
          __UpperCAmelCase			:				List[Any]													=    None
          if self.use_labels:
               __UpperCAmelCase			:				List[Any]													=    ids_tensor([self.batch_size]							,				self.type_sequence_label_size    )
               __UpperCAmelCase			:				Optional[Any]													=    ids_tensor([self.batch_size, self.text_seq_length]							,				self.num_labels    )

          __UpperCAmelCase			:				List[str]													=    LayoutLMvaConfig(
              vocab_size=self.vocab_size							,				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							,				max_position_embeddings=self.max_position_embeddings							,				type_vocab_size=self.type_vocab_size							,				initializer_range=self.initializer_range							,				coordinate_size=self.coordinate_size							,				shape_size=self.shape_size							,				input_size=self.image_size							,				patch_size=self.patch_size							,				)

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]							,				snake_case  :	Tuple							,				snake_case  :	Dict							,				snake_case  :	str    )		->							Dict:
          __UpperCAmelCase			:				List[str]													=    LayoutLMvaModel(config=snake_case    )
          model.to(snake_case    )
          model.eval()

          # text + image
          __UpperCAmelCase			:				Dict													=    model(snake_case							,				pixel_values=snake_case    )
          __UpperCAmelCase			:				Dict													=    model(
              snake_case							,				bbox=snake_case							,				pixel_values=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				int													=    model(snake_case							,				bbox=snake_case							,				pixel_values=snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				List[Any]													=    model(snake_case							,				bbox=snake_case							,				pixel_values=snake_case    )

          self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)    )

          # text only
          __UpperCAmelCase			:				Any													=    model(snake_case    )
          self.parent.assertEqual(
              result.last_hidden_state.shape							,				(self.batch_size, self.text_seq_length, self.hidden_size)    )

          # image only
          __UpperCAmelCase			:				Optional[Any]													=    model(pixel_values=snake_case    )
          self.parent.assertEqual(
              result.last_hidden_state.shape							,				(self.batch_size, self.image_seq_length, self.hidden_size)    )

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Dict							,				snake_case  :	Tuple							,				snake_case  :	Any							,				snake_case  :	int							,				snake_case  :	List[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[int]							,				snake_case  :	List[str]    )		->							Optional[int]:
          __UpperCAmelCase			:				Tuple													=    self.num_labels
          __UpperCAmelCase			:				Dict													=    LayoutLMvaForSequenceClassification(snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				str													=    model(
              snake_case							,				bbox=snake_case							,				pixel_values=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case							,				)
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Optional[Any]							,				snake_case  :	Tuple							,				snake_case  :	Optional[int]							,				snake_case  :	List[Any]							,				snake_case  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	Dict							,				snake_case  :	List[Any]    )		->							Any:
          __UpperCAmelCase			:				Optional[Any]													=    self.num_labels
          __UpperCAmelCase			:				List[Any]													=    LayoutLMvaForTokenClassification(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				Any													=    model(
              snake_case							,				bbox=snake_case							,				pixel_values=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case							,				)
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.text_seq_length, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	Tuple							,				snake_case  :	List[str]							,				snake_case  :	List[Any]							,				snake_case  :	Dict							,				snake_case  :	str							,				snake_case  :	str    )		->							Optional[Any]:
          __UpperCAmelCase			:				Optional[Any]													=    LayoutLMvaForQuestionAnswering(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[Any]													=    model(
              snake_case							,				bbox=snake_case							,				pixel_values=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				start_positions=snake_case							,				end_positions=snake_case							,				)
          self.parent.assertEqual(result.start_logits.shape							,				(self.batch_size, self.seq_length)    )
          self.parent.assertEqual(result.end_logits.shape							,				(self.batch_size, self.seq_length)    )






     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				str													=    self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     
          )			:				Union[str, Any]													=    config_and_inputs
          __UpperCAmelCase			:				List[Any]													=    {
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''pixel_values''': pixel_values,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict



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


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     False
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     False
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     False

     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     (
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE :				List[str]							     =     (
         {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]							,				snake_case  :	List[Any]							,				snake_case  :	Dict							,				snake_case  :	int    )		->							str:
          # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
          # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
          # the sequence dimension of the text embedding only.
          # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
          return True

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
          __UpperCAmelCase			:				int													=    LayoutLMvaModelTester(self    )
          __UpperCAmelCase			:				Optional[Any]													=    ConfigTester(self							,				config_class=snake_case							,				hidden_size=37    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Optional[Any]							,				snake_case  :	Tuple							,				snake_case  :	Optional[int]=False    )		->							str:
          __UpperCAmelCase			:				Optional[int]													=    copy.deepcopy(snake_case    )
          if model_class in get_values(snake_case    ):
               __UpperCAmelCase			:				List[str]													=    {
                   k: v.unsqueeze(1    ).expand(-1							,				self.model_tester.num_choices							,				-1    ).contiguous()
                   if isinstance(snake_case							,				torch.Tensor    ) and v.ndim > 1
                   else v
                   for k, v in inputs_dict.items()
               }
          if return_labels:
               if model_class in get_values(snake_case    ):
                    __UpperCAmelCase			:				List[str]													=    torch.ones(self.model_tester.batch_size							,				dtype=torch.long							,				device=snake_case    )
               elif model_class in get_values(snake_case    ):
                    __UpperCAmelCase			:				Optional[int]													=    torch.zeros(
                        self.model_tester.batch_size							,				dtype=torch.long							,				device=snake_case    )
                    __UpperCAmelCase			:				Dict													=    torch.zeros(
                        self.model_tester.batch_size							,				dtype=torch.long							,				device=snake_case    )
               elif model_class in [
                   *get_values(snake_case    ),
               ]:
                    __UpperCAmelCase			:				Dict													=    torch.zeros(
                        self.model_tester.batch_size							,				dtype=torch.long							,				device=snake_case    )
               elif model_class in [
                   *get_values(snake_case    ),
               ]:
                    __UpperCAmelCase			:				Optional[int]													=    torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.text_seq_length)							,				dtype=torch.long							,				device=snake_case							,				)

          return inputs_dict

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

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							int:
          __UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          __UpperCAmelCase			:				str													=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               __UpperCAmelCase			:				Dict													=    type
               self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							int:
          __UpperCAmelCase			:				Tuple													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case    )

     def 				lowerCamelCase__				(       self  :	int    )		->							List[str]:
          __UpperCAmelCase			:				Optional[int]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							List[str]:
          __UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case    )






     @slow
     def 				lowerCamelCase__				(       self  :	Any    )		->							Optional[int]:
          for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				Optional[int]													=    LayoutLMvaModel.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     return image



@require_torch
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     @cached_property
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[int]:
          return LayoutLMvaImageProcessor(apply_ocr=snake_case    ) if is_vision_available() else None






     @slow
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Tuple:
          __UpperCAmelCase			:				Any													=    LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''    ).to(snake_case    )

          __UpperCAmelCase			:				List[Any]													=    self.default_image_processor
          __UpperCAmelCase			:				List[str]													=    prepare_img()
          __UpperCAmelCase			:				List[str]													=    image_processor(images=snake_case							,				return_tensors='''pt'''    ).pixel_values.to(snake_case    )

          __UpperCAmelCase			:				Dict													=    torch.tensor([[1, 2]]    )
          __UpperCAmelCase			:				Optional[Any]													=    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]    ).unsqueeze(0    )

          # forward pass
          __UpperCAmelCase			:				int													=    model(
              input_ids=input_ids.to(snake_case    )							,				bbox=bbox.to(snake_case    )							,				pixel_values=pixel_values.to(snake_case    )							,				)

          # verify the logits
          __UpperCAmelCase			:				str													=    torch.Size((1, 199, 768)    )
          self.assertEqual(outputs.last_hidden_state.shape							,				snake_case    )

          __UpperCAmelCase			:				Any													=    torch.tensor(
              [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]    ).to(snake_case    )

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,				snake_case							,				atol=1E-4    )    )



'''simple docstring'''



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

__UpperCAmelCase    :Optional[int]				       =     TypeVar("KEY")
__UpperCAmelCase    :Tuple				       =     TypeVar("VAL")



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


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				KEY
     SCREAMING_SNAKE_CASE :				VAL



class 			a       (	_Item ):


     """simple docstring"""






     def __init__(       self  :	Optional[int]    )		->							None:
          super().__init__(snake_case							,				snake_case    )






     def __bool__(       self  :	Dict    )		->							bool:
          return False



__UpperCAmelCase    :Optional[Any]				       =     _DeletedItem()



class 			a       (	MutableMapping[KEY, VAL] ):


     """simple docstring"""






     def __init__(       self  :	str							,				snake_case  :	int = 8							,				snake_case  :	float = 0.75    )		->							None:
          __UpperCAmelCase			:				Dict													=    initial_block_size
          __UpperCAmelCase			:				list[_Item | None]													=    [None] * initial_block_size
          assert 0.0 < capacity_factor < 1.0
          __UpperCAmelCase			:				str													=    capacity_factor
          __UpperCAmelCase			:				Optional[int]													=    0

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	KEY    )		->							int:
          return hash(snake_case    ) % len(self._buckets    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							int:
          return (ind + 1) % len(self._buckets    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	int							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							bool:
          __UpperCAmelCase			:				int													=    self._buckets[ind]
          if not stored:
               __UpperCAmelCase			:				List[str]													=    _Item(snake_case							,				snake_case    )
               self._len += 1
               return True
          elif stored.key == key:
               __UpperCAmelCase			:				int													=    _Item(snake_case							,				snake_case    )
               return True
          else:
               return False

     def 				lowerCamelCase__				(       self  :	str    )		->							bool:
          __UpperCAmelCase			:				Union[str, Any]													=    len(self._buckets    ) * self._capacity_factor
          return len(self    ) >= int(snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							bool:
          if len(self._buckets    ) <= self._initial_block_size:
               return False
          __UpperCAmelCase			:				List[str]													=    len(self._buckets    ) * self._capacity_factor / 2
          return len(self    ) < limit

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int    )		->							None:
          __UpperCAmelCase			:				Union[str, Any]													=    self._buckets
          __UpperCAmelCase			:				Union[str, Any]													=    [None] * new_size
          __UpperCAmelCase			:				List[str]													=    0
          for item in old_buckets:
               if item:
                    self._add_item(item.key							,				item.val    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							None:
          self._resize(len(self._buckets    ) * 2    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							None:
          self._resize(len(self._buckets    ) // 2    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	KEY    )		->							Iterator[int]:
          __UpperCAmelCase			:				Tuple													=    self._get_bucket_index(snake_case    )
          for _ in range(len(self._buckets    )    ):
               yield ind
               __UpperCAmelCase			:				Dict													=    self._get_next_ind(snake_case    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							None:
          for ind in self._iterate_buckets(snake_case    ):
               if self._try_set(snake_case							,				snake_case							,				snake_case    ):
                    break

     def __setitem__(       self  :	List[str]							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							None:
          if self._is_full():
               self._size_up()

          self._add_item(snake_case							,				snake_case    )

     def __delitem__(       self  :	Any							,				snake_case  :	KEY    )		->							None:
          for ind in self._iterate_buckets(snake_case    ):
               __UpperCAmelCase			:				List[Any]													=    self._buckets[ind]
               if item is None:
                    raise KeyError(snake_case    )
               if item is _deleted:
                    continue
               if item.key == key:
                    __UpperCAmelCase			:				Optional[Any]													=    _deleted
                    self._len -= 1
                    break
          if self._is_sparse():
               self._size_down()

     def __getitem__(       self  :	List[str]							,				snake_case  :	KEY    )		->							VAL:
          for ind in self._iterate_buckets(snake_case    ):
               __UpperCAmelCase			:				Optional[int]													=    self._buckets[ind]
               if item is None:
                    break
               if item is _deleted:
                    continue
               if item.key == key:
                    return item.val
          raise KeyError(snake_case    )

     def __len__(       self  :	List[Any]    )		->							int:
          return self._len

     def __iter__(       self  :	str    )		->							Iterator[KEY]:
          yield from (item.key for item in self._buckets if item)






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