


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

SCREAMING_SNAKE_CASE_      =			TypeVar('KEY')
SCREAMING_SNAKE_CASE_      =			TypeVar('VAL')


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





  """simple docstring"""



  a_       :KEY
  a_       :VAL


class  lowerCAmelCase_						(       _Item      ):





  """simple docstring"""



  def __init__(					self     :							Dict						):





       '''simple docstring'''

       super().__init__(lowerCamelCase__				,							lowerCamelCase__						)


  def __bool__(					self     :							List[str]						):





       '''simple docstring'''

       return False


SCREAMING_SNAKE_CASE_      =			_DeletedItem()


class  lowerCAmelCase_						(       MutableMapping[KEY, VAL]      ):





  """simple docstring"""



  def __init__(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							int = 8				,							SCREAMING_SNAKE_CASE__     :							float = 0.7_5						):





       '''simple docstring'''

       __a       =   initial_block_size
       __a       =   [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       __a       =   capacity_factor
       __a       =   0

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							KEY						):





       '''simple docstring'''

       return hash(lowerCamelCase__						) % len(self._buckets						)

  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       return (ind + 1) % len(self._buckets						)

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							KEY				,							SCREAMING_SNAKE_CASE__     :							VAL						):





       '''simple docstring'''

       __a       =   self._buckets[ind]
       if not stored:
            __a       =   _Item(lowerCamelCase__				,							lowerCamelCase__						)
            self._len += 1
            return True
       elif stored.key == key:
            __a       =   _Item(lowerCamelCase__				,							lowerCamelCase__						)
            return True
       else:
            return False

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(lowerCamelCase__						)

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       if len(self._buckets						) <= self._initial_block_size:
            return False
       __a       =   len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       __a       =   self._buckets
       __a       =   [None] * new_size
       __a       =   0
       for item in old_buckets:
            if item:
                 self._add_item(item.key				,							item.val						)

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





       '''simple docstring'''

       self._resize(len(self._buckets						) * 2						)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       self._resize(len(self._buckets						) // 2						)

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							KEY						):





       '''simple docstring'''

       __a       =   self._get_bucket_index(lowerCamelCase__						)
       for _ in range(len(self._buckets						)						):
            yield ind
            __a       =   self._get_next_ind(lowerCamelCase__						)

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							KEY				,							SCREAMING_SNAKE_CASE__     :							VAL						):





       '''simple docstring'''

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

  def __setitem__(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							KEY				,							SCREAMING_SNAKE_CASE__     :							VAL						):





       '''simple docstring'''

       if self._is_full():
            self._size_up()

       self._add_item(lowerCamelCase__				,							lowerCamelCase__						)

  def __delitem__(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							KEY						):





       '''simple docstring'''

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

  def __getitem__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							KEY						):





       '''simple docstring'''

       for ind in self._iterate_buckets(lowerCamelCase__						):
            __a       =   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     :							Optional[Any]						):





       '''simple docstring'''

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


  def __repr__(					self     :							str						):





       '''simple docstring'''

       __a       =   " ,".join(
           f'''{item.key}: {item.val}''' for item in self._buckets if item						)
       return f'''HashMap({val_string})'''










'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






