





"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=None     ,   _SCREAMING_SNAKE_CASE=None			)					->List[Any]:





  """simple docstring"""


  if attention_mask is None:
    lowerCAmelCase__			:Tuple											=							tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE     ,   config.pad_token_id			)     ,   tf.inta			)
  return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class 					_lowerCAmelCase   :
       """simple docstring"""




       __magic_name__     :Tuple		  =					OPTConfig
       __magic_name__     :Tuple		  =					{}
       __magic_name__     :Union[str, Any]		  =					"""gelu"""






       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase=1_3    , __UpperCAmelCase=7    , __UpperCAmelCase=True    , __UpperCAmelCase=False    , __UpperCAmelCase=9_9    , __UpperCAmelCase=1_6    , __UpperCAmelCase=2    , __UpperCAmelCase=4    , __UpperCAmelCase=4    , __UpperCAmelCase="gelu"    , __UpperCAmelCase=0.1    , __UpperCAmelCase=0.1    , __UpperCAmelCase=2_0    , __UpperCAmelCase=2    , __UpperCAmelCase=1    , __UpperCAmelCase=0    , __UpperCAmelCase=1_6    , __UpperCAmelCase=1_6    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							parent
         lowerCAmelCase__			:Optional[int]											=							batch_size
         lowerCAmelCase__			:Tuple											=							seq_length
         lowerCAmelCase__			:Any											=							is_training
         lowerCAmelCase__			:List[str]											=							use_labels
         lowerCAmelCase__			:Optional[int]											=							vocab_size
         lowerCAmelCase__			:str											=							hidden_size
         lowerCAmelCase__			:int											=							num_hidden_layers
         lowerCAmelCase__			:int											=							num_attention_heads
         lowerCAmelCase__			:Optional[Any]											=							intermediate_size
         lowerCAmelCase__			:Optional[Any]											=							hidden_act
         lowerCAmelCase__			:Optional[int]											=							hidden_dropout_prob
         lowerCAmelCase__			:Union[str, Any]											=							attention_probs_dropout_prob
         lowerCAmelCase__			:List[str]											=							max_position_embeddings
         lowerCAmelCase__			:Optional[int]											=							eos_token_id
         lowerCAmelCase__			:Any											=							pad_token_id
         lowerCAmelCase__			:List[str]											=							bos_token_id
         lowerCAmelCase__			:Any											=							embed_dim
         lowerCAmelCase__			:List[Any]											=							word_embed_proj_dim
         lowerCAmelCase__			:str											=							False






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							ids_tensor([self.batch_size, self.seq_length - 1]    , self.vocab_size      )
         lowerCAmelCase__			:int											=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    , 1      )
         lowerCAmelCase__			:List[Any]											=							tf.concat([input_ids, eos_tensor]    , axis=1      )

         lowerCAmelCase__			:Dict											=							self.config_cls(
             vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , ffn_dim=self.intermediate_size    , dropout=self.hidden_dropout_prob    , attention_dropout=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , eos_token_id=self.eos_token_id    , bos_token_id=self.bos_token_id    , pad_token_id=self.pad_token_id    , embed_dim=self.embed_dim    , word_embed_proj_dim=self.word_embed_proj_dim    , is_encoder_decoder=__UpperCAmelCase    , **self.config_updates    , )
         lowerCAmelCase__			:Dict											=							prepare_opt_inputs_dict(__UpperCAmelCase    , __UpperCAmelCase      )
         return config, inputs_dict






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							TFOPTModel(config=__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							inputs_dict['input_ids']

         lowerCAmelCase__			:List[Any]											=							input_ids[:1, :]
         lowerCAmelCase__			:Tuple											=							inputs_dict['attention_mask'][:1, :]
         lowerCAmelCase__			:Optional[Any]											=							1

         # first forward pass
         lowerCAmelCase__			:List[str]											=							model(__UpperCAmelCase    , attention_mask=__UpperCAmelCase    , use_cache=__UpperCAmelCase      )

         lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowerCAmelCase__			:Optional[int]											=							ids_tensor((self.batch_size, 3)    , config.vocab_size      )
         lowerCAmelCase__			:List[Any]											=							tf.cast(ids_tensor((self.batch_size, 3)    , 2      )    , tf.inta      )

         # append to next input_ids and
         lowerCAmelCase__			:Tuple											=							tf.concat([input_ids, next_tokens]    , axis=-1      )
         lowerCAmelCase__			:Tuple											=							tf.concat([attention_mask, next_attn_mask]    , axis=-1      )

         lowerCAmelCase__			:str											=							model(__UpperCAmelCase    , attention_mask=__UpperCAmelCase      )[0]
         lowerCAmelCase__			:List[str]											=							model(__UpperCAmelCase    , attention_mask=__UpperCAmelCase    , past_key_values=__UpperCAmelCase      )[0]

         self.parent.assertEqual(next_tokens.shape[1]    , output_from_past.shape[1]      )

         # select random slice
         lowerCAmelCase__			:int											=							int(ids_tensor((1,)    , output_from_past.shape[-1]      )      )
         lowerCAmelCase__			:List[Any]											=							output_from_no_past[:, -3:, random_slice_idx]
         lowerCAmelCase__			:str											=							output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(__UpperCAmelCase    , __UpperCAmelCase    , rtol=1E-3      )






@require_tf
class 					_lowerCAmelCase   (     a  ,   a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :List[Any]		  =					(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
       __magic_name__     :List[Any]		  =					(TFOPTForCausalLM,) if is_tf_available() else ()
       __magic_name__     :Dict		  =					(
           {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
       )
       __magic_name__     :List[str]		  =					False
       __magic_name__     :Optional[int]		  =					False
       __magic_name__     :Optional[Any]		  =					False
       __magic_name__     :Union[str, Any]		  =					10






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							TFOPTModelTester(self      )
         lowerCAmelCase__			:Tuple											=							ConfigTester(self    , config_class=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__							,				lowerCAmelCase__			:List[str]											=							self.model_tester.prepare_config_and_inputs_for_common()

         def _get_word_embedding_weight(__UpperCAmelCase    , __UpperCAmelCase      ):
           if hasattr(__UpperCAmelCase    , 'weight'      ):
             return embedding_layer.weight
           else:
             # Here we build the word embeddings weights if not exists.
             # And then we retry to get the attribute once built.
             model.build()
             if hasattr(__UpperCAmelCase    , 'weight'      ):
               return embedding_layer.weight
             else:
               return None

         for model_class in self.all_model_classes:
           for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
             # build the embeddings
             lowerCAmelCase__			:Optional[Any]											=							model_class(config=__UpperCAmelCase      )
             lowerCAmelCase__			:Optional[int]											=							_get_word_embedding_weight(__UpperCAmelCase    , model.get_input_embeddings()      )
             lowerCAmelCase__			:List[Any]											=							_get_word_embedding_weight(__UpperCAmelCase    , model.get_output_embeddings()      )

             # reshape the embeddings
             model.resize_token_embeddings(__UpperCAmelCase      )
             lowerCAmelCase__			:List[str]											=							_get_word_embedding_weight(__UpperCAmelCase    , model.get_input_embeddings()      )
             lowerCAmelCase__			:Any											=							_get_word_embedding_weight(__UpperCAmelCase    , model.get_output_embeddings()      )

             # check that the resized embeddings size matches the desired size.
             lowerCAmelCase__			:Union[str, Any]											=							size if size is not None else config.vocab_size

             self.assertEqual(new_input_embeddings.shape[0]    , __UpperCAmelCase      )

             # check that weights remain the same after resizing
             lowerCAmelCase__			:int											=							True
             for pa, pa in zip(old_input_embeddings.value()    , new_input_embeddings.value()      ):
               if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                 lowerCAmelCase__			:str											=							False
             self.assertTrue(__UpperCAmelCase      )

             if old_output_embeddings is not None and new_output_embeddings is not None:
               self.assertEqual(new_output_embeddings.shape[0]    , __UpperCAmelCase      )

               lowerCAmelCase__			:Union[str, Any]											=							True
               for pa, pa in zip(old_output_embeddings.value()    , new_output_embeddings.value()      ):
                 if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                   lowerCAmelCase__			:List[Any]											=							False
               self.assertTrue(__UpperCAmelCase      )






def  __A   (_SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  return tf.constant(_SCREAMING_SNAKE_CASE     ,   dtype=tf.intaa			)






@require_tf
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :str		  =					99






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							tf.ones((4, 1)    , dtype=tf.intaa      ) * 2
         lowerCAmelCase__			:Optional[Any]											=							tf.concat([ids_tensor((4, 6)    , self.vocab_size - 3      ) + 3, eos_column_vector]    , axis=1      )
         lowerCAmelCase__			:int											=							input_ids.shape[0]
         lowerCAmelCase__			:Optional[Any]											=							OPTConfig(
             vocab_size=self.vocab_size    , hidden_size=2_4    , num_hidden_layers=2    , num_attention_heads=2    , ffn_dim=3_2    , max_position_embeddings=4_8    , eos_token_id=2    , pad_token_id=1    , bos_token_id=0    , )
         return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							TFOPTModel.from_pretrained('facebook/opt-350m'      )
         lowerCAmelCase__			:Dict											=							_long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
         lowerCAmelCase__			:int											=							tf.not_equal(__UpperCAmelCase    , model.config.pad_token_id      )
         with tf.GradientTape():
           lowerCAmelCase__			:int											=							model(input_ids=__UpperCAmelCase    , attention_mask=__UpperCAmelCase      ).last_hidden_state
         lowerCAmelCase__			:Dict											=							(1, 1_1, 5_1_2)
         self.assertEqual(output.shape    , __UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							tf.constant(
             [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]      )
         self.assertTrue(np.allclose(output[:, :3, :3]    , __UpperCAmelCase    , atol=4E-3      )      )

         lowerCAmelCase__			:List[Any]											=							tf.function(__UpperCAmelCase    , jit_compile=__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							xla_generate(__UpperCAmelCase    , __UpperCAmelCase      )[0]
         self.assertTrue(np.allclose(output[:, :3, :3]    , __UpperCAmelCase    , atol=4E-2      )      )






@require_tf
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         super().setUp()
         lowerCAmelCase__			:List[str]											=							'facebook/opt-350m'






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							TFOPTForCausalLM.from_pretrained(self.path_model      )
         lowerCAmelCase__			:List[Any]											=							GPTaTokenizer.from_pretrained(self.path_model      )

         lowerCAmelCase__			:int											=							[
             'Today is a beautiful day and I want to',
             'In the city of',
             'Paris is the capital of France and',
             'Computers and mobile phones have taken',
         ]
         # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
         lowerCAmelCase__			:List[str]											=							tokenizer(__UpperCAmelCase    , return_tensors='tf'    , padding=__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							tf.math.reduce_mean(model(inputs.input_ids    , attention_mask=inputs.attention_mask      )[0]    , axis=-1      )
         lowerCAmelCase__			:Tuple											=							tf.constant(
             [
                 [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                 [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                 [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                 [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
             ]      )
         self.assertTrue(np.allclose(__UpperCAmelCase    , __UpperCAmelCase    , atol=1E-4      )      )

         lowerCAmelCase__			:List[Any]											=							tf.function(__UpperCAmelCase    , jit_compile=__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							tf.math.reduce_mean(xla_generate(inputs.input_ids    , attention_mask=inputs.attention_mask      )[0]    , axis=-1      )
         self.assertTrue(np.allclose(__UpperCAmelCase    , __UpperCAmelCase    , atol=1E-4      )      )






@require_tf
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return [
             "Today is a beautiful day and I want",
             "In the city of",
             "Paris is the capital of France and",
             "Computers and mobile phones have taken",
         ]






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							'facebook/opt-125m'

         lowerCAmelCase__			:List[str]											=							[
             'Today is a beautiful day and I want to',
             'In the city of New York, the city',
             'Paris is the capital of France and the capital',
             'Computers and mobile phones have taken over the',
         ]

         lowerCAmelCase__			:Any											=							[]
         lowerCAmelCase__			:int											=							GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

         for prompt in self.prompts:
           lowerCAmelCase__			:List[Any]											=							tokenizer(__UpperCAmelCase    , return_tensors='tf'      ).input_ids

           lowerCAmelCase__			:List[Any]											=							model.generate(__UpperCAmelCase    , max_length=1_0      )

           lowerCAmelCase__			:Dict											=							tokenizer.batch_decode(__UpperCAmelCase    , skip_special_tokens=__UpperCAmelCase      )
           predicted_outputs += generated_string

         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							'facebook/opt-350m'

         lowerCAmelCase__			:List[Any]											=							GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							'left'

         # use different length sentences to test batching
         lowerCAmelCase__			:Any											=							[
             'Hello, my dog is a little',
             'Today, I',
         ]

         lowerCAmelCase__			:Dict											=							tokenizer(__UpperCAmelCase    , return_tensors='tf'    , padding=__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							inputs['input_ids']

         lowerCAmelCase__			:Optional[int]											=							model.generate(input_ids=__UpperCAmelCase    , attention_mask=inputs['attention_mask']      )

         lowerCAmelCase__			:str											=							tokenizer(sentences[0]    , return_tensors='tf'      ).input_ids
         lowerCAmelCase__			:List[str]											=							model.generate(input_ids=__UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							inputs_non_padded.shape[-1] - tf.math.reduce_sum(
             tf.cast(inputs['attention_mask'][-1]    , tf.intaa      )      )
         lowerCAmelCase__			:Dict											=							tokenizer(sentences[1]    , return_tensors='tf'      ).input_ids
         lowerCAmelCase__			:List[str]											=							model.generate(input_ids=__UpperCAmelCase    , max_length=model.config.max_length - num_paddings      )

         lowerCAmelCase__			:List[Any]											=							tokenizer.batch_decode(__UpperCAmelCase    , skip_special_tokens=__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							tokenizer.decode(output_non_padded[0]    , skip_special_tokens=__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							tokenizer.decode(output_padded[0]    , skip_special_tokens=__UpperCAmelCase      )

         lowerCAmelCase__			:int											=							[
             'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
             'Today, I was in the middle of a conversation with a friend about the',
         ]
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , [non_padded_sentence, padded_sentence]      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							'facebook/opt-350m'

         lowerCAmelCase__			:Optional[int]											=							[
             'Today is a beautiful day and I want to',
             'In the city of San Francisco, the city',
             'Paris is the capital of France and the capital',
             'Computers and mobile phones have taken over the',
         ]

         lowerCAmelCase__			:Optional[int]											=							[]
         lowerCAmelCase__			:List[Any]											=							GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

         for prompt in self.prompts:
           lowerCAmelCase__			:int											=							tokenizer(__UpperCAmelCase    , return_tensors='tf'      ).input_ids

           lowerCAmelCase__			:str											=							model.generate(__UpperCAmelCase    , max_length=1_0      )

           lowerCAmelCase__			:int											=							tokenizer.batch_decode(__UpperCAmelCase    , skip_special_tokens=__UpperCAmelCase      )
           predicted_outputs += generated_string

         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )







"""simple docstring"""





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

__A											=		TypeVar("""KEY""")
__A											=		TypeVar("""VAL""")






@dataclass(frozen=a  ,   slots=a       )
class 					_lowerCAmelCase   (     Generic[KEY, VAL]       ):
       """simple docstring"""




       __magic_name__     :KEY
       __magic_name__     :VAL






class 					_lowerCAmelCase   (     _Item       ):
       """simple docstring"""




       def __init__(    self      ):



         '''simple docstring'''

         super().__init__(__UpperCAmelCase    , __UpperCAmelCase      )






       def __bool__(    self      ):



         '''simple docstring'''

         return False






__A											=		_DeletedItem()






class 					_lowerCAmelCase   (     MutableMapping[KEY, VAL]       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase = 8    , __UpperCAmelCase = 0.75      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							initial_block_size
         lowerCAmelCase__			:list[_Item | None]											=							[None] * initial_block_size
         assert 0.0 < capacity_factor < 1.0
         lowerCAmelCase__			:Tuple											=							capacity_factor
         lowerCAmelCase__			:str											=							0






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							self._buckets[ind]
         if not stored:
           lowerCAmelCase__			:Dict											=							_Item(__UpperCAmelCase    , __UpperCAmelCase      )
           self._len += 1
           return True
         elif stored.key == key:
           lowerCAmelCase__			:Optional[Any]											=							_Item(__UpperCAmelCase    , __UpperCAmelCase      )
           return True
         else:
           return False






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							len(self._buckets      ) * self._capacity_factor
         return len(self      ) >= int(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							self._buckets
         lowerCAmelCase__			:Tuple											=							[None] * new_size
         lowerCAmelCase__			:List[Any]											=							0
         for item in old_buckets:
           if item:
             self._add_item(item.key    , item.val      )






       def      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							self._get_bucket_index(__UpperCAmelCase      )
         for _ in range(len(self._buckets      )      ):
           yield ind
           lowerCAmelCase__			:Tuple											=							self._get_next_ind(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def __setitem__(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

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

         self._add_item(__UpperCAmelCase    , __UpperCAmelCase      )






       def __delitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def __getitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           lowerCAmelCase__			:str											=							self._buckets[ind]
           if item is None:
             break
           if item is _deleted:
             continue
           if item.key == key:
             return item.val
         raise KeyError(__UpperCAmelCase      )






       def __len__(    self      ):



         '''simple docstring'''

         return self._len






       def __iter__(    self      ):



         '''simple docstring'''

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






       def __repr__(    self      ):



         '''simple docstring'''

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