




import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
      from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		Optional[int]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Union[str, Any]=13       ,						__lowerCamelCase	:		Any=32       ,						__lowerCamelCase	:		int=2       ,						__lowerCamelCase	:		Any=3       ,						__lowerCamelCase	:		Optional[Any]=16       ,						__lowerCamelCase	:		Optional[Any]=[1, 2, 1]       ,						__lowerCamelCase	:		List[str]=[2, 2, 4]       ,						__lowerCamelCase	:		Any=2       ,						__lowerCamelCase	:		Optional[Any]=2.0       ,						__lowerCamelCase	:		Optional[int]=True       ,						__lowerCamelCase	:		List[Any]=0.0       ,						__lowerCamelCase	:		str=0.0       ,						__lowerCamelCase	:		int=0.1       ,						__lowerCamelCase	:		Tuple="gelu"       ,						__lowerCamelCase	:		Optional[Any]=False       ,						__lowerCamelCase	:		Tuple=True       ,						__lowerCamelCase	:		List[Any]=0.02       ,						__lowerCamelCase	:		Optional[Any]=1e-5       ,						__lowerCamelCase	:		List[str]=True       ,						__lowerCamelCase	:		List[str]=None       ,						__lowerCamelCase	:		List[str]=True       ,						__lowerCamelCase	:		Any=10       ,						__lowerCamelCase	:		int=8       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      parent
       lowerCAmelCase__										=      batch_size
       lowerCAmelCase__										=      image_size
       lowerCAmelCase__										=      patch_size
       lowerCAmelCase__										=      num_channels
       lowerCAmelCase__										=      embed_dim
       lowerCAmelCase__										=      depths
       lowerCAmelCase__										=      num_heads
       lowerCAmelCase__										=      window_size
       lowerCAmelCase__										=      mlp_ratio
       lowerCAmelCase__										=      qkv_bias
       lowerCAmelCase__										=      hidden_dropout_prob
       lowerCAmelCase__										=      attention_probs_dropout_prob
       lowerCAmelCase__										=      drop_path_rate
       lowerCAmelCase__										=      hidden_act
       lowerCAmelCase__										=      use_absolute_embeddings
       lowerCAmelCase__										=      patch_norm
       lowerCAmelCase__										=      layer_norm_eps
       lowerCAmelCase__										=      initializer_range
       lowerCAmelCase__										=      is_training
       lowerCAmelCase__										=      scope
       lowerCAmelCase__										=      use_labels
       lowerCAmelCase__										=      type_sequence_label_size
       lowerCAmelCase__										=      encoder_stride
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       lowerCAmelCase__										=      None
       if self.use_labels:
         lowerCAmelCase__										=      ids_tensor([self.batch_size]       ,						self.type_sequence_label_size						)

       lowerCAmelCase__										=      self.get_config()

       return config, pixel_values, labels
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       return SwinvaConfig(
           image_size=self.image_size       ,						patch_size=self.patch_size       ,						num_channels=self.num_channels       ,						embed_dim=self.embed_dim       ,						depths=self.depths       ,						num_heads=self.num_heads       ,						window_size=self.window_size       ,						mlp_ratio=self.mlp_ratio       ,						qkv_bias=self.qkv_bias       ,						hidden_dropout_prob=self.hidden_dropout_prob       ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,						drop_path_rate=self.drop_path_rate       ,						hidden_act=self.hidden_act       ,						use_absolute_embeddings=self.use_absolute_embeddings       ,						path_norm=self.patch_norm       ,						layer_norm_eps=self.layer_norm_eps       ,						initializer_range=self.initializer_range       ,						encoder_stride=self.encoder_stride       ,						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      SwinvaModel(config=__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.eval()
       lowerCAmelCase__										=      model(__lowerCamelCase						)

       lowerCAmelCase__										=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
       lowerCAmelCase__										=      int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

       self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, expected_seq_len, expected_dim)						)
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      SwinvaForMaskedImageModeling(config=__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.eval()
       lowerCAmelCase__										=      model(__lowerCamelCase						)
       self.parent.assertEqual(
           result.logits.shape       ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

       # test greyscale images
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      SwinvaForMaskedImageModeling(__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.eval()

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



       """simple docstring"""
       lowerCAmelCase__										=      self.type_sequence_label_size
       lowerCAmelCase__										=      SwinvaForImageClassification(__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.eval()
       lowerCAmelCase__										=      model(__lowerCamelCase       ,						labels=__lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.type_sequence_label_size)						)



     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.prepare_config_and_inputs()
       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      config_and_inputs
       lowerCAmelCase__										=      {'''pixel_values''': pixel_values}
       return config, inputs_dict





@require_torch
class     SCREAMING_SNAKE_CASE__		(_a    ,					_a    ,					unittest.TestCase			):
     lowercase_ :      Union[str, Any]				= (
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     lowercase_ :      List[str]				= (
         {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     lowercase_ :      Tuple				= False
     lowercase_ :      Optional[Any]				= False
     lowercase_ :      Any				= False
     lowercase_ :      int				= False
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      SwinvaModelTester(self						)
       lowerCAmelCase__										=      ConfigTester(self       ,						config_class=__lowerCamelCase       ,						embed_dim=37						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase						)
     @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''						)
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       pass
     @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''						)
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       pass
     def        A__      (						self	:		str						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

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



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

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



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase__										=      True

       for model_class in self.all_model_classes:
         lowerCAmelCase__										=      True
         lowerCAmelCase__										=      False
         lowerCAmelCase__										=      True
         lowerCAmelCase__										=      model_class(__lowerCamelCase						)
         model.to(__lowerCamelCase						)
         model.eval()
         with torch.no_grad():
           lowerCAmelCase__										=      model(**self._prepare_for_class(__lowerCamelCase       ,						__lowerCamelCase						)						)
         lowerCAmelCase__										=      outputs.attentions
         lowerCAmelCase__										=      len(self.model_tester.depths						)
         self.assertEqual(len(__lowerCamelCase						)       ,						__lowerCamelCase						)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         lowerCAmelCase__										=      True
         lowerCAmelCase__										=      config.window_size**2
         lowerCAmelCase__										=      model_class(__lowerCamelCase						)
         model.to(__lowerCamelCase						)
         model.eval()
         with torch.no_grad():
           lowerCAmelCase__										=      model(**self._prepare_for_class(__lowerCamelCase       ,						__lowerCamelCase						)						)
         lowerCAmelCase__										=      outputs.attentions
         self.assertEqual(len(__lowerCamelCase						)       ,						__lowerCamelCase						)

         self.assertListEqual(
             list(attentions[0].shape[-3:]						)       ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,						)
         lowerCAmelCase__										=      len(__lowerCamelCase						)

         # Check attention is always last and order is fine
         lowerCAmelCase__										=      True
         lowerCAmelCase__										=      True
         lowerCAmelCase__										=      model_class(__lowerCamelCase						)
         model.to(__lowerCamelCase						)
         model.eval()
         with torch.no_grad():
           lowerCAmelCase__										=      model(**self._prepare_for_class(__lowerCamelCase       ,						__lowerCamelCase						)						)

         if hasattr(self.model_tester       ,						'''num_hidden_states_types'''						):
           lowerCAmelCase__										=      self.model_tester.num_hidden_states_types
         else:
           # also another +1 for reshaped_hidden_states
           lowerCAmelCase__										=      2
         self.assertEqual(out_len + added_hidden_states       ,						len(__lowerCamelCase						)						)

         lowerCAmelCase__										=      outputs.attentions

         self.assertEqual(len(__lowerCamelCase						)       ,						__lowerCamelCase						)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]						)       ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,						)
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      model_class(__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.eval()

       with torch.no_grad():
         lowerCAmelCase__										=      model(**self._prepare_for_class(__lowerCamelCase       ,						__lowerCamelCase						)						)

       lowerCAmelCase__										=      outputs.hidden_states

       lowerCAmelCase__										=      getattr(
           self.model_tester       ,						'''expected_num_hidden_layers'''       ,						len(self.model_tester.depths						) + 1						)
       self.assertEqual(len(__lowerCamelCase						)       ,						__lowerCamelCase						)

       # Swinv2 has a different seq_length
       lowerCAmelCase__										=      (
           config.patch_size
           if isinstance(config.patch_size       ,						collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )

       lowerCAmelCase__										=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]						)       ,						[num_patches, self.model_tester.embed_dim]       ,						)

       lowerCAmelCase__										=      outputs.reshaped_hidden_states
       self.assertEqual(len(__lowerCamelCase						)       ,						__lowerCamelCase						)

       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      reshaped_hidden_states[0].shape
       lowerCAmelCase__										=      (
           reshaped_hidden_states[0].view(__lowerCamelCase       ,						__lowerCamelCase       ,						height * width						).permute(0       ,						2       ,						1						)
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]						)       ,						[num_patches, self.model_tester.embed_dim]       ,						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase__										=      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,						collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
         lowerCAmelCase__										=      True
         self.check_hidden_states_output(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCAmelCase__										=      True

         self.check_hidden_states_output(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase__										=      3

       lowerCAmelCase__										=      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,						collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       lowerCAmelCase__										=      (
           config.patch_size
           if isinstance(config.patch_size       ,						collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )

       lowerCAmelCase__										=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       lowerCAmelCase__										=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         lowerCAmelCase__										=      True
         self.check_hidden_states_output(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						(padded_height, padded_width)						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCAmelCase__										=      True
         self.check_hidden_states_output(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						(padded_height, padded_width)						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase						)
     @slow
     def        A__      (						self	:		int						):



       """simple docstring"""
       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__										=      SwinvaModel.from_pretrained(__lowerCamelCase						)
         self.assertIsNotNone(__lowerCamelCase						)



     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase__										=      _config_zero_init(__lowerCamelCase						)
       for model_class in self.all_model_classes:
         lowerCAmelCase__										=      model_class(config=__lowerCamelCase						)
         for name, param in model.named_parameters():
           if "embeddings" not in name and "logit_scale" not in name and 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"""       ,						)





@require_vision
@require_torch
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     @cached_property
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       return (
           AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''						)
           if is_vision_available()
           else None
       )



     @slow
     def        A__      (						self	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''						).to(
           __lowerCamelCase						)
       lowerCAmelCase__										=      self.default_image_processor

       lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       lowerCAmelCase__										=      image_processor(images=__lowerCamelCase       ,						return_tensors='''pt'''						).to(__lowerCamelCase						)

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

       # verify the logits
       lowerCAmelCase__										=      torch.Size((1, 10_00)						)
       self.assertEqual(outputs.logits.shape       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      torch.tensor([-0.3947, -0.4306, 0.0026]						).to(__lowerCamelCase						)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,						__lowerCamelCase       ,						atol=1e-4						)						)







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

__magic_name__  :  Any					=       TypeVar("""KEY""")
__magic_name__  :  Dict					=       TypeVar("""VAL""")





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





class     SCREAMING_SNAKE_CASE__		(_Item			):
     def __init__(						self	:		Tuple						):



       """simple docstring"""
       super().__init__(__lowerCamelCase       ,						__lowerCamelCase						)



     def __bool__(						self	:		Tuple						):



       """simple docstring"""
       return False



__magic_name__  :  str					=       _DeletedItem()





class     SCREAMING_SNAKE_CASE__		(MutableMapping[KEY, VAL]			):
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		int = 8       ,						__lowerCamelCase	:		float = 0.75						):



       """simple docstring"""
       lowerCAmelCase__										=      initial_block_size
       lowerCAmelCase__										=      [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowerCAmelCase__										=      capacity_factor
       lowerCAmelCase__										=      0
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       return hash(__lowerCamelCase						) % len(self._buckets						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       return (ind + 1) % len(self._buckets						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets[ind]
       if not stored:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         self._len += 1
         return True
       elif stored.key == key:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         return True
       else:
         return False
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       if len(self._buckets						) <= self._initial_block_size:
         return False
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets
       lowerCAmelCase__										=      [None] * new_size
       lowerCAmelCase__										=      0
       for item in old_buckets:
         if item:
           self._add_item(item.key       ,						item.val						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self._resize(len(self._buckets						) * 2						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       self._resize(len(self._buckets						) // 2						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       lowerCAmelCase__										=      self._get_bucket_index(__lowerCamelCase						)
       for _ in range(len(self._buckets						)						):
         yield ind
         lowerCAmelCase__										=      self._get_next_ind(__lowerCamelCase						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         if self._try_set(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						):
           break
     def __setitem__(						self	:		str       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       if self._is_full():
         self._size_up()

       self._add_item(__lowerCamelCase       ,						__lowerCamelCase						)
     def __delitem__(						self	:		Tuple       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           raise KeyError(__lowerCamelCase						)
         if item is _deleted:
           continue
         if item.key == key:
           lowerCAmelCase__										=      _deleted
           self._len -= 1
           break
       if self._is_sparse():
         self._size_down()
     def __getitem__(						self	:		Optional[int]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      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	:		int						):



       """simple docstring"""
       return self._len
     def __iter__(						self	:		Union[str, Any]						):



       """simple docstring"""
       yield from (item.key for item in self._buckets if item)



     def __repr__(						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      ''' ,'''.join(
           F"""{item.key}: {item.val}""" for item in self._buckets if item						)
       return F"""HashMap({val_string})"""

