import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin







class 			SCREAMING_SNAKE_CASE__ :

       def __init__(				self :			Union[str, Any]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Tuple=3_60_00       ,       _lowerCAmelCase :			Union[str, Any]=13       ,       _lowerCAmelCase :			Any=7       ,       _lowerCAmelCase :			Optional[int]=True       ,       _lowerCAmelCase :			Any=32       ,       _lowerCAmelCase :			Any=42       ,       _lowerCAmelCase :			Optional[Any]=5       ,       _lowerCAmelCase :			Optional[Any]=2       ,       _lowerCAmelCase :			List[str]=4       ,       _lowerCAmelCase :			Optional[Any]=2       ,       _lowerCAmelCase :			int=32       ,       _lowerCAmelCase :			Optional[int]=80       ,       _lowerCAmelCase :			Tuple=33       ,       _lowerCAmelCase :			Union[str, Any]=0.0       ,       _lowerCAmelCase :			str=1e-6       ,       _lowerCAmelCase :			Any=1_00       ,       _lowerCAmelCase :			Dict=0.0       ,       ):
           __snake_case    :							str             =  vocab_size
           __snake_case    :							str             =  parent
           __snake_case    :							str             =  batch_size
           __snake_case    :							List[str]             =  num_contexts
           # For common tests
           __snake_case    :							str             =  self.num_contexts
           __snake_case    :							Tuple             =  is_training
           __snake_case    :							Any             =  hidden_size
           __snake_case    :							str             =  num_ext_layers
           __snake_case    :							Union[str, Any]             =  ext_size
           __snake_case    :							str             =  num_hidden_layers
           __snake_case    :							Tuple             =  num_attention_heads
           __snake_case    :							List[Any]             =  num_experts
           __snake_case    :							Any             =  d_ff
           __snake_case    :							Dict             =  d_ext
           __snake_case    :							List[str]             =  d_spout
           __snake_case    :							List[str]             =  dropout_rate
           __snake_case    :							Dict             =  layer_norm_epsilon
           __snake_case    :							Optional[Any]             =  expert_capacity
           __snake_case    :							int             =  router_jitter_noise

       def        snake_case__  (				self :			str       ):
           return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""       )

       def        snake_case__  (				self :			Dict       ):
           __snake_case    :							Optional[Any]             =  ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size       )

           __snake_case    :							Union[str, Any]             =  self.get_config()

           return (config, input_ids)

       def        snake_case__  (				self :			Union[str, Any]       ):
           __snake_case    :							Tuple             =  ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size       )

           __snake_case    :							Union[str, Any]             =  self.get_config()

           return (config, {"input_ids": input_ids})

       def        snake_case__  (				self :			List[Any]       ):
           return GPTSanJapaneseConfig(
               vocab_size=self.vocab_size       ,       num_contexts=self.seq_length       ,       d_model=self.hidden_size       ,       d_ff=self.d_ff       ,       d_ext=self.d_ext       ,       d_spout=self.d_spout       ,       num_switch_layers=self.num_hidden_layers - self.num_ext_layers       ,       num_ext_layers=self.num_ext_layers       ,       num_heads=self.num_attention_heads       ,       num_experts=self.num_experts       ,       expert_capacity=self.expert_capacity       ,       dropout_rate=self.dropout_rate       ,       layer_norm_epsilon=self.layer_norm_epsilon       ,       router_jitter_noise=self.router_jitter_noise       ,       )





       def        snake_case__  (				self :			Optional[int]       ,       _lowerCAmelCase :			Optional[int]       ,       _lowerCAmelCase :			int       ,       ):
           __snake_case    :							Tuple             =  GPTSanJapaneseForConditionalGeneration(config=A_       )
           model.to(A_       )
           model.eval()
           __snake_case    :							Optional[int]             =  model(
               input_ids=A_       ,       )
           self.parent.assertIsNotNone(A_       )







@require_torch
class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase					,				__UpperCamelCase					,				unittest.TestCase  ):
       A				:     str					    =     (GPTSanJapaneseModel,) if is_torch_available() else ()
       A				:     Any					    =     (
           {
               "conversational": GPTSanJapaneseForConditionalGeneration,
               "feature-extraction": GPTSanJapaneseForConditionalGeneration,
               "summarization": GPTSanJapaneseForConditionalGeneration,
               "text2text-generation": GPTSanJapaneseForConditionalGeneration,
               "translation": GPTSanJapaneseForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       A				:     Any					    =     False
       A				:     Tuple					    =     False
       A				:     List[Any]					    =     False
       A				:     Union[str, Any]					    =     False
       A				:     str					    =     False
       A				:     Union[str, Any]					    =     False
       A				:     Dict					    =     False
       A				:     Optional[int]					    =     False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       A				:     str					    =     [0.8, 0.9]

       def        snake_case__  (				self :			Any       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			Any       ,       _lowerCAmelCase :			int       ):


           if pipeline_test_casse_name == "SummarizationPipelineTests":
               # TODO: fix `_reorder_cache` is not implemented for this model
               return True
           elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
               # TODO: check this.
               return True

           return False

       def        snake_case__  (				self :			List[str]       ):
           __snake_case    :							Any             =  GPTSanJapaneseTester(self       )
           __snake_case    :							str             =  ConfigTester(self       ,       config_class=A_       ,       d_model=37       )

       def        snake_case__  (				self :			Any       ):
           GPTSanJapaneseConfig()

       def        snake_case__  (				self :			Union[str, Any]       ):
           __snake_case    :							Any             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_       )

       @unittest.skip(
           reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""       )
       def        snake_case__  (				self :			Optional[int]       ):
           super().test_model_parallelism()





       @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
       def        snake_case__  (				self :			str       ):
           pass







@require_torch
class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase					,				__UpperCamelCase					,				unittest.TestCase  ):
       A				:     Optional[Any]					    =     (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
       A				:     Any					    =     False
       A				:     Tuple					    =     False
       A				:     Any					    =     False
       A				:     Optional[int]					    =     False
       A				:     Optional[int]					    =     False
       A				:     int					    =     False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       A				:     Optional[int]					    =     [0.8, 0.9]

       def        snake_case__  (				self :			Dict       ):
           __snake_case    :							List[Any]             =  GPTSanJapaneseTester(self       )
           __snake_case    :							Dict             =  ConfigTester(self       ,       config_class=A_       ,       d_model=37       )

       def        snake_case__  (				self :			str       ):
           GPTSanJapaneseConfig()

       def        snake_case__  (				self :			str       ):
           __snake_case    :							str             =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_       )

       @unittest.skip(
           reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""       )
       def        snake_case__  (				self :			str       ):
           super().test_model_parallelism()

       @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
       def        snake_case__  (				self :			Tuple       ):
           pass

       @slow
       def        snake_case__  (				self :			List[str]       ):
           __snake_case    :							Any             =  GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Optional[int]             =  GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Union[str, Any]             =  tokenizer.encode("""武田信玄は"""       ,       return_tensors="""pt"""       )
           __snake_case    :							Optional[Any]             =  model(A_       )
           __snake_case    :							str             =  outputs.logits.detach().cpu().numpy()
           # Output of original model created with mesh-tensoflow
           __snake_case    :							int             =  [
               # fmt: off
               [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
               -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
               -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
               -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
               [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
               -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
               -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
               -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
               [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
               -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
               -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
               -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
               [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
               -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
               -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
               -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
               [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
               -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
               -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
               -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
               # fmt: on
           ]
           __snake_case    :							List[str]             =  np.array(A_       ).flatten()
           __snake_case    :							str             =  output_logits[0, :, :20].flatten()

           def check(_lowerCAmelCase :			Any       ,       _lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			Optional[Any]=5e-4       ):
               return abs(a - b       ) < epsilon * max(abs(A_       )       ,       abs(A_       )       )

           self.assertTrue(np.all([check(target[i]       ,       predict[i]       ) for i in range(len(A_       )       )]       )       )

       @slow
       def        snake_case__  (				self :			Tuple       ):
           __snake_case    :							Union[str, Any]             =  GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Dict             =  GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           model.to(A_       )

           # set deterministically
           __snake_case    :							Optional[Any]             =  GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Optional[int]             =  1

           # use different length sentences to test batching
           __snake_case    :							Dict             =  [
               "甲斐なら武田と言うほど",
               "織田信長は、",
           ]

           __snake_case    :							Optional[Any]             =  "left"
           __snake_case    :							str             =  tokenizer(A_       ,       return_tensors="""pt"""       ,       padding=A_       )
           __snake_case    :							int             =  inputs["input_ids"].to(A_       )

           self.assertNotEqual(inputs["""attention_mask"""][0].numpy().tolist()       ,       inputs["""attention_mask"""][1].numpy().tolist()       )

           __snake_case    :							int             =  model.generate(
               input_ids=A_       ,       attention_mask=inputs["""attention_mask"""].to(A_       )       ,       max_new_tokens=3       ,       generation_config=A_       ,       )

           __snake_case    :							List[Any]             =  tokenizer(sentences[0]       ,       return_tensors="""pt"""       ).input_ids.to(A_       )
           __snake_case    :							Union[str, Any]             =  model.generate(
               input_ids=A_       ,       max_new_tokens=3       ,       generation_config=A_       )

           __snake_case    :							int             =  tokenizer(sentences[1]       ,       return_tensors="""pt"""       ).input_ids.to(A_       )
           __snake_case    :							Optional[int]             =  model.generate(input_ids=A_       ,       max_new_tokens=3       ,       generation_config=A_       )

           self.assertNotEqual(inputs_non_padded.shape       ,       inputs_padded.shape       )

           __snake_case    :							Tuple             =  tokenizer.batch_decode(A_       ,       skip_special_tokens=A_       )
           __snake_case    :							Union[str, Any]             =  tokenizer.decode(output_non_padded[0]       ,       skip_special_tokens=A_       )
           __snake_case    :							str             =  tokenizer.decode(output_padded[0]       ,       skip_special_tokens=A_       )

           __snake_case    :							List[str]             =  [
               "甲斐なら武田と言うほど甲斐の武田",
               "織田信長は、このような",
           ]
           self.assertListEqual(A_       ,       A_       )
           self.assertListEqual(A_       ,       [non_padded_sentence, padded_sentence]       )

       @tooslow
       def        snake_case__  (				self :			Union[str, Any]       ):
           __snake_case    :							List[Any]             =  GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							int             =  GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           # Output of original model created with mesh-tensoflow
           __snake_case    :							Any             =  [
               ("武田信玄は", 3_56_75),
               ("武田信玄は、", 45),
               ("武田信玄は、この", 29),
               ("武田信玄は、このよう", 3_06_42),
               ("武田信玄は、このような", 3_56_80),
               ("武田信玄は、このような「", 86_40),
               ("武田信玄は、このような「武田", 3_16_17),
               ("武田信玄は、このような「武田家", 3_06_46),
               ("武田信玄は、このような「武田家の", 3_16_17),
               ("武田信玄は、このような「武田家の家", 3_13_81),
           ]
           for input, output in target:
               __snake_case    :							Union[str, Any]             =  tokenizer.encode(A_       ,       return_tensors="""pt"""       )
               __snake_case    :							int             =  model(A_       )
               __snake_case    :							Optional[int]             =  outputs.logits.detach().cpu().numpy()[0]
               __snake_case    :							Union[str, Any]             =  np.argmax(output_logits[-1]       )
               self.assertEqual(A_       ,       A_       )

       @slow
       def        snake_case__  (				self :			Optional[Any]       ):
           __snake_case    :							Tuple             =  GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Optional[Any]             =  GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           model.to(A_       )

           # set deterministically
           __snake_case    :							Dict             =  GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Dict             =  1

           __snake_case    :							List[Any]             =  "武田信玄は、"
           __snake_case    :							Optional[Any]             =  tokenizer(A_       ,       return_tensors="""pt"""       ).input_ids.to(A_       )
           __snake_case    :							Any             =  tokenizer([input_text, input_text]       ,       return_tensors="""pt"""       ).input_ids.to(A_       )

           # spout from uniform and one-hot
           __snake_case    :							Union[str, Any]             =  [
               # fmt: off
               [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
               0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
               0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
               0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
               0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
               0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
               0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
               0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
               0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
               0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
               0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
               0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
               0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
               0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
               0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
               0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
               0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
               0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
               0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
               0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
               0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
               0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
               0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
               0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
               0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
               0.9552151 , 0.93901511, 0.22489288], # uniform
               [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                0., 0., 0., 0., 0., 0., 0., 0.],
               # fmt: on
           ]

           __snake_case    :							int             =  model.generate(
               input_ids=A_       ,       spout=spouts[0]       ,       max_new_tokens=20       ,       generation_config=A_       ,       )

           __snake_case    :							Tuple             =  model.generate(
               input_ids=A_       ,       spout=spouts[1]       ,       max_new_tokens=20       ,       generation_config=A_       ,       )

           __snake_case    :							Union[str, Any]             =  model.generate(
               input_ids=A_       ,       spout=A_       ,       max_new_tokens=20       ,       generation_config=A_       ,       )

           __snake_case    :							str             =  tokenizer.decode(outputa[0]       )
           __snake_case    :							int             =  tokenizer.decode(outputa[0]       )
           __snake_case    :							Any             =  tokenizer.batch_decode(A_       )

           __snake_case    :							Union[str, Any]             =  [
               "武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である",
               "武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討",
           ]
           self.assertListEqual(A_       ,       A_       )
           self.assertListEqual(A_       ,       [outa_sentence, outa_sentence]       )





       @slow
       def        snake_case__  (				self :			Dict       ):
           __snake_case    :							str             =  GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							Dict             =  GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           model.to(A_       )

           # set deterministically
           __snake_case    :							Dict             =  GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""       )
           __snake_case    :							List[str]             =  1

           __snake_case    :							Tuple             =  "武田信玄"
           __snake_case    :							Dict             =  "織田信長"
           __snake_case    :							Optional[Any]             =  "は、"
           __snake_case    :							int             =  "が、"
           __snake_case    :							Optional[int]             =  tokenizer(A_       ,       prefix_text=A_       ,       return_tensors="""pt"""       )
           __snake_case    :							Tuple             =  tokenizer(A_       ,       prefix_text=A_       ,       return_tensors="""pt"""       )
           __snake_case    :							Optional[int]             =  tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]       ,       return_tensors="""pt"""       )

           __snake_case    :							Dict             =  model.generate(
               input_ids=input_tok_a.input_ids.to(A_       )       ,       token_type_ids=input_tok_a.token_type_ids.to(A_       )       ,       max_new_tokens=20       ,       generation_config=A_       ,       )

           __snake_case    :							str             =  model.generate(
               input_ids=input_tok_a.input_ids.to(A_       )       ,       token_type_ids=input_tok_a.token_type_ids.to(A_       )       ,       max_new_tokens=20       ,       generation_config=A_       ,       )

           __snake_case    :							str             =  model.generate(
               input_ids=input_tok_a.input_ids.to(A_       )       ,       token_type_ids=input_tok_a.token_type_ids.to(A_       )       ,       attention_mask=input_tok_a.attention_mask.to(A_       )       ,       max_new_tokens=20       ,       generation_config=A_       ,       )

           __snake_case    :							List[str]             =  tokenizer.decode(outputa[0]       )
           __snake_case    :							Dict             =  tokenizer.decode(outputa[0]       )
           __snake_case    :							Any             =  tokenizer.batch_decode(A_       )

           __snake_case    :							List[Any]             =  [
               "武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
               "織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
           ]
           self.assertListEqual(A_       ,       A_       )
           self.assertListEqual(A_       ,       [outa_sentence, outa_sentence]       )






from hashlib import mda, shaaaa

SCREAMING_SNAKE_CASE_						=					(shaaaa, mda)

class  a     :


						def __init__(				self  ,    A_ = 8		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			0B0
										_UpperCAmelCase				:						Optional[Any]       						=			size


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.hash_(A_		)
										self.bitarray |= h


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			self.hash_(A_		)
										return (h & self.bitarray) == h


						def __contains__(				self  ,    A_		):







										'''simple docstring'''


										return self.exists(A_		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			bin(A_		)[2:]
										return res.zfill(self.size		)


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.format_bin(self.bitarray		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			0B0
										for func in HASH_FUNCTIONS:
														_UpperCAmelCase				:						str       						=			(
														    int.from_bytes(func(value.encode()		).digest()  ,    "little"		) % self.size
														)
														res |= 2**position
										return res


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										return self.format_bin(self.hash_(A_		)		)





						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			bin(self.bitarray		).count("1"		)
										return (n_ones / self.size) ** len(A_		)


