





"""simple docstring"""


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import (
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraModel,
  )
  from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       int					,   __a   :       str					,   __a   :       List[Any]=2					,   __a   :       int=8					,   __a   :       Dict=True					,   __a   :       Tuple=True					,   __a   :       List[str]=True					,   __a   :       Dict=True					,   __a   :       List[str]=99					,   __a   :       Union[str, Any]=16					,   __a   :       Any=5					,   __a   :       Any=2					,   __a   :       Any=36					,   __a   :       Tuple="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[int]=0.0					,   __a   :       str=512					,   __a   :       Optional[int]=16					,   __a   :       Dict=2					,   __a   :       Dict=0.02					,   __a   :       Any=3					,   __a   :       List[str]=4					,   __a   :       Optional[int]=None					,   )		->				Any:
        _UpperCamelCase      :	Any						=       parent
        _UpperCamelCase      :	Union[str, Any]						=       batch_size
        _UpperCamelCase      :	Optional[int]						=       seq_length
        _UpperCamelCase      :	Union[str, Any]						=       is_training
        _UpperCamelCase      :	Optional[int]						=       use_input_mask
        _UpperCamelCase      :	Dict						=       use_token_type_ids
        _UpperCamelCase      :	int						=       use_labels
        _UpperCamelCase      :	Any						=       vocab_size
        _UpperCamelCase      :	List[str]						=       hidden_size
        _UpperCamelCase      :	Optional[int]						=       num_hidden_layers
        _UpperCamelCase      :	Any						=       num_attention_heads
        _UpperCamelCase      :	Union[str, Any]						=       intermediate_size
        _UpperCamelCase      :	Optional[Any]						=       hidden_act
        _UpperCamelCase      :	int						=       hidden_dropout_prob
        _UpperCamelCase      :	Dict						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Union[str, Any]						=       max_position_embeddings
        _UpperCamelCase      :	Tuple						=       type_vocab_size
        _UpperCamelCase      :	Dict						=       type_sequence_label_size
        _UpperCamelCase      :	List[str]						=       initializer_range
        _UpperCamelCase      :	List[str]						=       num_labels
        _UpperCamelCase      :	Dict						=       num_choices
        _UpperCamelCase      :	Any						=       scope

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict:
        _UpperCamelCase      :	List[str]						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

        _UpperCamelCase      :	List[Any]						=       None
        if self.use_input_mask:
              _UpperCamelCase      :	Optional[Any]						=       random_attention_mask([self.batch_size, self.seq_length]	)

        _UpperCamelCase      :	List[str]						=       None
        if self.use_token_type_ids:
              _UpperCamelCase      :	int						=       ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size	)

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

        _UpperCamelCase      :	List[str]						=       self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[int]:
        return MraConfig(
            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					,   is_decoder=__a					,   initializer_range=self.initializer_range					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Tuple:
        _UpperCamelCase      :	List[Any]						=       self.get_config()
        _UpperCamelCase      :	List[str]						=       300
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Union[str, Any]:
        (
            (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  
        )      :	Union[str, Any]						=       self.prepare_config_and_inputs()

        _UpperCamelCase      :	Optional[int]						=       True
        _UpperCamelCase      :	Union[str, Any]						=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
        _UpperCamelCase      :	Optional[int]						=       ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2	)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       List[str]					,   __a   :       Tuple					,   __a   :       Tuple					,   __a   :       List[Any]					,   __a   :       Optional[int]					,   __a   :       List[str]					,   __a   :       Union[str, Any]	)		->				Tuple:
        _UpperCamelCase      :	Optional[Any]						=       MraModel(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	Tuple						=       model(__a					,   attention_mask=__a					,   token_type_ids=__a	)
        _UpperCamelCase      :	str						=       model(__a					,   token_type_ids=__a	)
        _UpperCamelCase      :	Tuple						=       model(__a	)
        self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       Any					,   __a   :       Optional[Any]					,   __a   :       int					,   __a   :       List[str]					,   __a   :       str					,   __a   :       Tuple					,   __a   :       Optional[int]					,   __a   :       Optional[int]					,   __a   :       int					,   )		->				int:
        _UpperCamelCase      :	List[Any]						=       True
        _UpperCamelCase      :	str						=       MraModel(__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	str						=       model(
            __a					,   attention_mask=__a					,   token_type_ids=__a					,   encoder_hidden_states=__a					,   encoder_attention_mask=__a					,   )
        _UpperCamelCase      :	List[str]						=       model(
            __a					,   attention_mask=__a					,   token_type_ids=__a					,   encoder_hidden_states=__a					,   )
        _UpperCamelCase      :	Dict						=       model(__a					,   attention_mask=__a					,   token_type_ids=__a	)
        self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       Union[str, Any]					,   __a   :       str					,   __a   :       Dict					,   __a   :       int					,   __a   :       List[str]					,   __a   :       int					,   __a   :       int	)		->				int:
        _UpperCamelCase      :	Tuple						=       MraForMaskedLM(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	Any						=       model(__a					,   attention_mask=__a					,   token_type_ids=__a					,   labels=__a	)
        self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int					,   __a   :       int					,   __a   :       int					,   __a   :       Any					,   __a   :       List[str]					,   __a   :       List[str]					,   __a   :       Dict	)		->				str:
        _UpperCamelCase      :	Union[str, Any]						=       MraForQuestionAnswering(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	List[Any]						=       model(
            __a					,   attention_mask=__a					,   token_type_ids=__a					,   start_positions=__a					,   end_positions=__a					,   )
        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    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       Dict					,   __a   :       Any					,   __a   :       Any					,   __a   :       Optional[Any]					,   __a   :       List[Any]					,   __a   :       Optional[int]					,   __a   :       Any	)		->				Optional[Any]:
        _UpperCamelCase      :	List[Any]						=       self.num_labels
        _UpperCamelCase      :	Optional[int]						=       MraForSequenceClassification(__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	int						=       model(__a					,   attention_mask=__a					,   token_type_ids=__a					,   labels=__a	)
        self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       str					,   __a   :       Tuple					,   __a   :       int					,   __a   :       Any					,   __a   :       Optional[int]					,   __a   :       str					,   __a   :       str	)		->				int:
        _UpperCamelCase      :	Optional[int]						=       self.num_labels
        _UpperCamelCase      :	Tuple						=       MraForTokenClassification(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	Any						=       model(__a					,   attention_mask=__a					,   token_type_ids=__a					,   labels=__a	)
        self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       Union[str, Any]					,   __a   :       Optional[Any]					,   __a   :       Any					,   __a   :       Optional[int]					,   __a   :       str					,   __a   :       Any					,   __a   :       Any	)		->				str:
        _UpperCamelCase      :	Tuple						=       self.num_choices
        _UpperCamelCase      :	Optional[int]						=       MraForMultipleChoice(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	str						=       input_ids.unsqueeze(1	).expand(-1					,   self.num_choices					,   -1	).contiguous()
        _UpperCamelCase      :	Optional[Any]						=       token_type_ids.unsqueeze(1	).expand(-1					,   self.num_choices					,   -1	).contiguous()
        _UpperCamelCase      :	Tuple						=       input_mask.unsqueeze(1	).expand(-1					,   self.num_choices					,   -1	).contiguous()
        _UpperCamelCase      :	Optional[int]						=       model(
            __a					,   attention_mask=__a					,   token_type_ids=__a					,   labels=__a					,   )
        self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_choices)	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[Any]						=       self.prepare_config_and_inputs()
        (
            (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  
        )      :	int						=       config_and_inputs
        _UpperCamelCase      :	str						=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					(
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__							:str	     =					False
  SCREAMING_SNAKE_CASE__							:int	     =					False
  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					False
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					False

  SCREAMING_SNAKE_CASE__							:List[Any]	     =					()

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Any:
        _UpperCamelCase      :	Optional[int]						=       MraModelTester(self	)
        _UpperCamelCase      :	Any						=       ConfigTester(self					,   config_class=__a					,   hidden_size=37	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Dict:
        self.config_tester.run_common_tests()

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[str]:
        _UpperCamelCase      :	int						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[str]:
        _UpperCamelCase      :	Optional[int]						=       self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              _UpperCamelCase      :	Tuple						=       type
              self.model_tester.create_and_check_model(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[str]:
        _UpperCamelCase      :	Union[str, Any]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				str:
        _UpperCamelCase      :	Optional[Any]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[int]:
        _UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Any:
        _UpperCamelCase      :	Union[str, Any]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Optional[Any]:
        _UpperCamelCase      :	Tuple						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__a	)

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Union[str, Any]:
        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _UpperCamelCase      :	Tuple						=       MraModel.from_pretrained(__a	)
              self.assertIsNotNone(__a	)


  @unittest.skip(reason="MRA does not output attentions"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Tuple:
        return





@require_torch
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Any:
        _UpperCamelCase      :	Tuple						=       MraModel.from_pretrained("uw-madison/mra-base-512-4"	)
        _UpperCamelCase      :	Optional[Any]						=       torch.arange(256	).unsqueeze(0	)

        with torch.no_grad():
              _UpperCamelCase      :	Dict						=       model(__a	)[0]

        _UpperCamelCase      :	Optional[Any]						=       torch.Size((1, 256, 768)	)
        self.assertEqual(output.shape					,   __a	)

        _UpperCamelCase      :	Any						=       torch.tensor(
            [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3]					,   __a					,   atol=1e-4	)	)

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[Any]:
        _UpperCamelCase      :	List[Any]						=       MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"	)
        _UpperCamelCase      :	str						=       torch.arange(256	).unsqueeze(0	)

        with torch.no_grad():
              _UpperCamelCase      :	Optional[int]						=       model(__a	)[0]

        _UpperCamelCase      :	int						=       5_0265

        _UpperCamelCase      :	str						=       torch.Size((1, 256, vocab_size)	)
        self.assertEqual(output.shape					,   __a	)

        _UpperCamelCase      :	List[str]						=       torch.tensor(
            [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3]					,   __a					,   atol=1e-4	)	)


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[Any]:
        _UpperCamelCase      :	Dict						=       MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"	)
        _UpperCamelCase      :	Any						=       torch.arange(4096	).unsqueeze(0	)

        with torch.no_grad():
              _UpperCamelCase      :	Any						=       model(__a	)[0]

        _UpperCamelCase      :	Any						=       5_0265

        _UpperCamelCase      :	str						=       torch.Size((1, 4096, vocab_size)	)
        self.assertEqual(output.shape					,   __a	)

        _UpperCamelCase      :	List[str]						=       torch.tensor(
            [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3]					,   __a					,   atol=1e-4	)	)







"""simple docstring"""


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

lowerCamelCase__				  =   TypeVar("KEY")
lowerCamelCase__				  =   TypeVar("VAL")



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







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:KEY
  SCREAMING_SNAKE_CASE__							:VAL





class      __SCREAMING_SNAKE_CASE    (						_Item   ):







  '''simple docstring'''


  def __init__(		self   :       List[str]	)		->				None:
        super().__init__(__a					,   __a	)


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





lowerCamelCase__				  =   _DeletedItem()



class      __SCREAMING_SNAKE_CASE    (						MutableMapping[KEY, VAL]   ):







  '''simple docstring'''


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

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       KEY	)		->				int:
        return hash(__a	) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       int	)		->				int:
        return (ind + 1) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				bool:
        _UpperCamelCase      :	List[Any]						=       self._buckets[ind]
        if not stored:
              _UpperCamelCase      :	Tuple						=       _Item(__a					,   __a	)
              self._len += 1
              return True
        elif stored.key == key:
              _UpperCamelCase      :	Union[str, Any]						=       _Item(__a					,   __a	)
              return True
        else:
              return False

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				bool:
        _UpperCamelCase      :	Any						=       len(self._buckets	) * self._capacity_factor
        return len(self	) >= int(__a	)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				None:
        self._resize(len(self._buckets	) * 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				None:
        self._resize(len(self._buckets	) // 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       KEY	)		->				Iterator[int]:
        _UpperCamelCase      :	str						=       self._get_bucket_index(__a	)
        for _ in range(len(self._buckets	)	):
              yield ind
              _UpperCamelCase      :	Tuple						=       self._get_next_ind(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        for ind in self._iterate_buckets(__a	):
              if self._try_set(__a					,   __a					,   __a	):
                    break

  def __setitem__(		self   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        if self._is_full():
              self._size_up()

        self._add_item(__a					,   __a	)

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

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

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

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


  def __repr__(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       " ,".join(
            F'''{item.key}: {item.val}''' for item in self._buckets if item	)
        return F'''HashMap({val_string})'''
