
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	__SCREAMING_SNAKE_CASE  =			"platform"

	import jax
	import jax.numpy as jnp

	from transformers import MarianTokenizer
	from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def 				__magic_name__(					UpperCAmelCase__   :					Tuple       ,							UpperCAmelCase__   :					Dict       ,							UpperCAmelCase__   :					List[str]=None       ,							UpperCAmelCase__   :					List[str]=None       ,							UpperCAmelCase__   :					Optional[int]=None       ,							UpperCAmelCase__   :					Optional[int]=None       ,							UpperCAmelCase__   :					int=None       ,							UpperCAmelCase__   :					str=None       ,							):
						if attention_mask is None:
												UpperCamelCase_    :  Optional[Any]				    =    np.where(input_ids != config.pad_token_id       ,							1       ,							0  )
						if decoder_attention_mask is None:
												UpperCamelCase_    :  Optional[int]				    =    np.where(decoder_input_ids != config.pad_token_id       ,							1       ,							0  )
						if head_mask is None:
												UpperCamelCase_    :  str				    =    np.ones((config.encoder_layers, config.encoder_attention_heads)  )
						if decoder_head_mask is None:
												UpperCamelCase_    :  Tuple				    =    np.ones((config.decoder_layers, config.decoder_attention_heads)  )
						if cross_attn_head_mask is None:
												UpperCamelCase_    :  str				    =    np.ones((config.decoder_layers, config.decoder_attention_heads)  )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}




class 						__snake_case :




		def __init__(     self				:str ,						_SCREAMING_SNAKE_CASE				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:List[str]=1_3 ,						_SCREAMING_SNAKE_CASE				:Dict=7 ,						_SCREAMING_SNAKE_CASE				:Tuple=True ,						_SCREAMING_SNAKE_CASE				:List[str]=False ,						_SCREAMING_SNAKE_CASE				:Any=9_9 ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=1_6 ,						_SCREAMING_SNAKE_CASE				:str=2 ,						_SCREAMING_SNAKE_CASE				:str=4 ,						_SCREAMING_SNAKE_CASE				:List[Any]=4 ,						_SCREAMING_SNAKE_CASE				:str="gelu" ,						_SCREAMING_SNAKE_CASE				:Any=0.1 ,						_SCREAMING_SNAKE_CASE				:Optional[int]=0.1 ,						_SCREAMING_SNAKE_CASE				:Tuple=3_2 ,						_SCREAMING_SNAKE_CASE				:Optional[int]=2 ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=1 ,						_SCREAMING_SNAKE_CASE				:Optional[int]=0 ,						_SCREAMING_SNAKE_CASE				:Optional[int]=0.02 ,						)				-> List[str]:






								'''simple docstring'''






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




		def   _snake_case  (     self				:Any)				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  int				    =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,						self.vocab_size) ,						3 ,						self.vocab_size)
								UpperCamelCase_    :  Optional[int]				    =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,						dtype=np.intaa)) ,						-1)

								UpperCamelCase_    :  int				    =    shift_tokens_right(_SCREAMING_SNAKE_CASE ,						1 ,						2)

								UpperCamelCase_    :  Optional[Any]				    =    MarianConfig(
								    vocab_size=self.vocab_size ,						d_model=self.hidden_size ,						encoder_layers=self.num_hidden_layers ,						decoder_layers=self.num_hidden_layers ,						encoder_attention_heads=self.num_attention_heads ,						decoder_attention_heads=self.num_attention_heads ,						encoder_ffn_dim=self.intermediate_size ,						decoder_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 ,						initializer_range=self.initializer_range ,						use_cache=_SCREAMING_SNAKE_CASE ,						)
								UpperCamelCase_    :  str				    =    prepare_marian_inputs_dict(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								return config, inputs_dict




		def   _snake_case  (     self				:Dict)				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  str				    =    self.prepare_config_and_inputs()
								return config, inputs_dict




		def   _snake_case  (     self				:Dict ,						_SCREAMING_SNAKE_CASE				:Dict ,						_SCREAMING_SNAKE_CASE				:List[str] ,						_SCREAMING_SNAKE_CASE				:Any)				-> Dict:






								'''simple docstring'''






								UpperCamelCase_    :  Any				    =    2_0
								UpperCamelCase_    :  Dict				    =    model_class_name(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  int				    =    model.encode(inputs_dict["""input_ids"""])

								UpperCamelCase_    :  Tuple				    =    (
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								UpperCamelCase_    :  Any				    =    model.init_cache(decoder_input_ids.shape[0] ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  List[Any]				    =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,						dtype="""i4""")

								UpperCamelCase_    :  Optional[Any]				    =    jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,						)
								UpperCamelCase_    :  List[str]				    =    model.decode(
								    decoder_input_ids[:, :-1] ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						past_key_values=_SCREAMING_SNAKE_CASE ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  Any				    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,						dtype="""i4""")
								UpperCamelCase_    :  Union[str, Any]				    =    model.decode(
								    decoder_input_ids[:, -1:] ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						past_key_values=outputs_cache.past_key_values ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  Optional[int]				    =    model.decode(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Union[str, Any]				    =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1E-3 ,						msg=F"""Max diff is {diff}""")





		def   _snake_case  (     self				:int ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Dict ,						_SCREAMING_SNAKE_CASE				:List[str])				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    2_0
								UpperCamelCase_    :  List[str]				    =    model_class_name(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Dict				    =    model.encode(inputs_dict["""input_ids"""])

								UpperCamelCase_    :  List[Any]				    =    (
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								UpperCamelCase_    :  Tuple				    =    jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
								    ] ,						axis=-1 ,						)

								UpperCamelCase_    :  Optional[int]				    =    model.init_cache(decoder_input_ids.shape[0] ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  List[Any]				    =    jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,						)

								UpperCamelCase_    :  int				    =    model.decode(
								    decoder_input_ids[:, :-1] ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						past_key_values=_SCREAMING_SNAKE_CASE ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)
								UpperCamelCase_    :  str				    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,						dtype="""i4""")
								UpperCamelCase_    :  Union[str, Any]				    =    model.decode(
								    decoder_input_ids[:, -1:] ,						_SCREAMING_SNAKE_CASE ,						past_key_values=outputs_cache.past_key_values ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  List[str]				    =    model.decode(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Optional[Any]				    =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1E-3 ,						msg=F"""Max diff is {diff}""")







@require_flax
class 						__snake_case (						_a				, unittest.TestCase				, _a  ):
		a_        =				True
		a_        =				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
		a_        =				(FlaxMarianMTModel,) if is_flax_available() else ()




		def   _snake_case  (     self				:str)				-> Tuple:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    FlaxMarianModelTester(self)




		def   _snake_case  (     self				:List[Any])				-> int:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:int)				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:Dict)				-> Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_    :  int				    =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__):
																				UpperCamelCase_    :  Tuple				    =    self._prepare_for_class(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_    :  Optional[int]				    =    model_class(_SCREAMING_SNAKE_CASE)

																				@jax.jit
																				def encode_jitted(_SCREAMING_SNAKE_CASE				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:Optional[int]=None ,						**_SCREAMING_SNAKE_CASE				:List[str]):
																										return model.encode(input_ids=_SCREAMING_SNAKE_CASE ,						attention_mask=_SCREAMING_SNAKE_CASE)

																				with self.subTest("""JIT Enabled"""):
																										UpperCamelCase_    :  List[Any]				    =    encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				with self.subTest("""JIT Disabled"""):
																										with jax.disable_jit():
																																UpperCamelCase_    :  Optional[Any]				    =    encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,						len(_SCREAMING_SNAKE_CASE))
																				for jitted_output, output in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																										self.assertEqual(jitted_output.shape ,						output.shape)




		def   _snake_case  (     self				:Tuple)				-> Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Union[str, Any]				    =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__):
																				UpperCamelCase_    :  Tuple				    =    model_class(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_    :  List[str]				    =    model.encode(inputs_dict["""input_ids"""] ,						inputs_dict["""attention_mask"""])

																				UpperCamelCase_    :  Tuple				    =    {
																				    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																				    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																				    """encoder_outputs""": encoder_outputs,
																				}

																				@jax.jit
																				def decode_jitted(_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:List[Any]):
																										return model.decode(
																										    decoder_input_ids=_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						encoder_outputs=_SCREAMING_SNAKE_CASE ,						)

																				with self.subTest("""JIT Enabled"""):
																										UpperCamelCase_    :  Optional[int]				    =    decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				with self.subTest("""JIT Disabled"""):
																										with jax.disable_jit():
																																UpperCamelCase_    :  Dict				    =    decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,						len(_SCREAMING_SNAKE_CASE))
																				for jitted_output, output in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																										self.assertEqual(jitted_output.shape ,						output.shape)





		@slow
		def   _snake_case  (     self				:Optional[int])				-> Optional[int]:






								'''simple docstring'''






								for model_class_name in self.all_model_classes:
														UpperCamelCase_    :  Union[str, Any]				    =    model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de""")
														# FlaxMarianForSequenceClassification expects eos token in input_ids
														UpperCamelCase_    :  Dict				    =    np.ones((1, 1)) * model.config.eos_token_id
														UpperCamelCase_    :  List[Any]				    =    model(_SCREAMING_SNAKE_CASE)
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE)







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						unittest.TestCase  ):
		a_        =				None
		a_        =				None




		@classmethod
		def   _snake_case  (     cls				:Tuple)				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  Any				    =    F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls




		@cached_property
		def   _snake_case  (     self				:str)				-> List[Any]:






								'''simple docstring'''






								return MarianTokenizer.from_pretrained(self.model_name)




		@property
		def   _snake_case  (     self				:Union[str, Any])				-> int:






								'''simple docstring'''






								return self.tokenizer.eos_token_id




		@cached_property
		def   _snake_case  (     self				:Optional[int])				-> Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_    :  FlaxMarianMTModel				    =    FlaxMarianMTModel.from_pretrained(self.model_name)
								self.assertEqual(model.config.decoder_start_token_id ,						model.config.pad_token_id)
								return model




		def   _snake_case  (     self				:Optional[Any] ,						**_SCREAMING_SNAKE_CASE				:str)				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[int]				    =    self.translate_src_text(**_SCREAMING_SNAKE_CASE)
								self.assertListEqual(self.expected_text ,						_SCREAMING_SNAKE_CASE)





		def   _snake_case  (     self				:Any ,						**_SCREAMING_SNAKE_CASE				:List[Any])				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    self.tokenizer(self.src_text ,						padding=_SCREAMING_SNAKE_CASE ,						return_tensors="""np""" ,						**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Union[str, Any]				    =    self.model.generate(
								    model_inputs.input_ids ,						attention_mask=model_inputs.attention_mask ,						num_beams=2 ,						max_length=1_2_8 ,						).sequences
								UpperCamelCase_    :  Tuple				    =    self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,						skip_special_tokens=_SCREAMING_SNAKE_CASE)
								return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"en"
		a_        =				"fr"
		a_        =				[
		    "I am a small frog.",
		    "Now I can forget the 100 words of german that I know.",
		]
		a_        =				[
		    "Je suis une petite grenouille.",
		    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
		]




		@slow
		def   _snake_case  (     self				:str)				-> Optional[Any]:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"fr"
		a_        =				"en"
		a_        =				[
		    "Donnez moi le micro.",
		    "Tom et Mary étaient assis à une table.",  # Accents
		]
		a_        =				[
		    "Give me the microphone.",
		    "Tom and Mary were sitting at a table.",
		]




		@slow
		def   _snake_case  (     self				:List[Any])				-> Tuple:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"mt"
		a_        =				"en"
		a_        =				["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
		a_        =				["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]




		@slow
		def   _snake_case  (     self				:Union[str, Any])				-> Union[str, Any]:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"en"
		a_        =				"de"
		a_        =				[
		    "I am a small frog.",
		    "Now I can forget the 100 words of german that I know.",
		    "Tom asked his teacher for advice.",
		    "That's how I would do it.",
		    "Tom really admired Mary's courage.",
		    "Turn around and close your eyes.",
		]
		a_        =				[
		    "Ich bin ein kleiner Frosch.",
		    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
		    "Tom bat seinen Lehrer um Rat.",
		    "So würde ich das machen.",
		    "Tom bewunderte Marias Mut wirklich.",
		    "Drehen Sie sich um und schließen Sie die Augen.",
		]




		@slow
		def   _snake_case  (     self				:str)				-> Union[str, Any]:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"en"
		a_        =				"zh"
		a_        =				["My name is Wolfgang and I live in Berlin"]
		a_        =				["我叫沃尔夫冈 我住在柏林"]




		@slow
		def   _snake_case  (     self				:Union[str, Any])				-> Union[str, Any]:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"ru"
		a_        =				"fr"
		a_        =				["Он показал мне рукопись своей новой пьесы."]
		a_        =				["Il m'a montré le manuscrit de sa nouvelle pièce."]




		@slow
		def   _snake_case  (     self				:Any)				-> Dict:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						_a  ):
		a_        =				"en"
		a_        =				"ROMANCE"
		a_        =				[
		    ">>fr<< Don't spend so much time watching TV.",
		    ">>pt<< Your message has been sent.",
		    ">>es<< He's two years older than me.",
		]
		a_        =				[
		    "Ne passez pas autant de temps à regarder la télé.",
		    "A sua mensagem foi enviada.",
		    "Es dos años más viejo que yo.",
		]




		@slow
		def   _snake_case  (     self				:Any)				-> str:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()






'''simple docstring'''


from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 						__snake_case (						_a  ):




		def __init__(     self				:Optional[Any] ,						*_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						**_SCREAMING_SNAKE_CASE				:List[Any])				-> List[str]:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> Optional[int]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    (
								    next_prime(value % self.size_table)
								    if not is_prime(value % self.size_table)
								    else value % self.size_table
								)  # gt = bigger than
								return next_prime_gt - (data % next_prime_gt)




		def   _snake_case  (     self				:List[str] ,						_SCREAMING_SNAKE_CASE				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:int)				-> Dict:






								'''simple docstring'''






								return (increment * self.__hash_function_a(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)) % self.size_table





		def   _snake_case  (     self				:Tuple ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:int=None)				-> List[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    1
								UpperCamelCase_    :  str				    =    self.hash_function(_SCREAMING_SNAKE_CASE)

								while self.values[new_key] is not None and self.values[new_key] != key:
														UpperCamelCase_    :  int				    =    (
														    self.__hash_double_function(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
														    if self.balanced_factor() >= self.lim_charge
														    else None
														)
														if new_key is None:
																				break
														else:
																				i += 1

								return new_key




