




"""simple docstring"""




import math
import unittest

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

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


if is_torch_available():
			import torch

			from transformers import (
			    BioGptForCausalLM,
			    BioGptForSequenceClassification,
			    BioGptForTokenClassification,
			    BioGptModel,
			    BioGptTokenizer,
			)
			from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class   UpperCAmelCase_    :
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_=13				,						UpperCamelCase_=7				,						UpperCamelCase_=True				,						UpperCamelCase_=True				,						UpperCamelCase_=False				,						UpperCamelCase_=True				,						UpperCamelCase_=99				,						UpperCamelCase_=32				,						UpperCamelCase_=5				,						UpperCamelCase_=4				,						UpperCamelCase_=37				,						UpperCamelCase_="gelu"				,						UpperCamelCase_=0.1				,						UpperCamelCase_=0.1				,						UpperCamelCase_=5_12				,						UpperCamelCase_=16				,						UpperCamelCase_=2				,						UpperCamelCase_=0.0_2				,						UpperCamelCase_=3				,						UpperCamelCase_=4				,						UpperCamelCase_=None				,						)						->       List[Any]:
					__lowercase			:  str								=       parent
					__lowercase			:  List[Any]								=       batch_size
					__lowercase			:  Optional[int]								=       seq_length
					__lowercase			:  Dict								=       is_training
					__lowercase			:  Optional[int]								=       use_input_mask
					__lowercase			:  List[Any]								=       use_token_type_ids
					__lowercase			:  List[Any]								=       use_labels
					__lowercase			:  List[Any]								=       vocab_size
					__lowercase			:  Optional[int]								=       hidden_size
					__lowercase			:  Dict								=       num_hidden_layers
					__lowercase			:  int								=       num_attention_heads
					__lowercase			:  Optional[Any]								=       intermediate_size
					__lowercase			:  Any								=       hidden_act
					__lowercase			:  Dict								=       hidden_dropout_prob
					__lowercase			:  str								=       attention_probs_dropout_prob
					__lowercase			:  Optional[int]								=       max_position_embeddings
					__lowercase			:  Optional[Any]								=       type_vocab_size
					__lowercase			:  Any								=       type_sequence_label_size
					__lowercase			:  Any								=       initializer_range
					__lowercase			:  Dict								=       num_labels
					__lowercase			:  List[str]								=       num_choices
					__lowercase			:  str								=       scope
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  List[Any]								=       ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size       )

					__lowercase			:  str								=       None
					if self.use_input_mask:
								__lowercase			:  List[str]								=       random_attention_mask([self.batch_size, self.seq_length]       )

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

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

					__lowercase			:  Optional[int]								=       self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					return BioGptConfig(
					    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=UpperCamelCase_				,						initializer_range=self.initializer_range				,						)
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Union[str, Any]:
					__lowercase			:  Dict								=       BioGptModel(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  Optional[Any]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_       )
					__lowercase			:  Optional[int]								=       model(UpperCamelCase_       )
					self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						)						->       Union[str, Any]:
					__lowercase			:  Union[str, Any]								=       BioGptForCausalLM(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  Tuple								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_				,						labels=UpperCamelCase_       )
					self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						*UpperCamelCase_       )						->       Tuple:
					__lowercase			:  Any								=       BioGptModel(config=UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()

					# create attention mask
					__lowercase			:  str								=       torch.ones(input_ids.shape				,						dtype=torch.long				,						device=UpperCamelCase_       )
					__lowercase			:  Dict								=       self.seq_length // 2
					__lowercase			:  str								=       0

					# first forward pass
					__lowercase			,__lowercase			:  str								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_       ).to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__lowercase			:  Any								=       ids_tensor((self.batch_size, 1)				,						config.vocab_size       )

					# change a random masked slice from input_ids
					__lowercase			:  int								=       ids_tensor((1,)				,						UpperCamelCase_       ).item() + 1
					__lowercase			:  Optional[Any]								=       ids_tensor((self.batch_size, 1)				,						config.vocab_size       ).squeeze(-1       )
					__lowercase			:  str								=       random_other_next_tokens

					# append to next input_ids and attn_mask
					__lowercase			:  Dict								=       torch.cat([input_ids, next_tokens]				,						dim=-1       )
					__lowercase			:  List[Any]								=       torch.cat(
					    [attn_mask, torch.ones((attn_mask.shape[0], 1)				,						dtype=torch.long				,						device=UpperCamelCase_       )]				,						dim=1				,						)

					# get two different outputs
					__lowercase			:  Tuple								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_       )['''last_hidden_state''']
					__lowercase			:  str								=       model(UpperCamelCase_				,						past_key_values=UpperCamelCase_				,						attention_mask=UpperCamelCase_       )['''last_hidden_state''']

					# select random slice
					__lowercase			:  int								=       ids_tensor((1,)				,						output_from_past.shape[-1]       ).item()
					__lowercase			:  List[str]								=       output_from_no_past[:, -1, random_slice_idx].detach()
					__lowercase			:  str								=       output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(UpperCamelCase_				,						UpperCamelCase_				,						atol=1E-3       )       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						*UpperCamelCase_       )						->       Dict:
					__lowercase			:  str								=       BioGptModel(config=UpperCamelCase_       ).to(UpperCamelCase_       ).eval()

					__lowercase			:  List[Any]								=       torch.ones(input_ids.shape				,						dtype=torch.long				,						device=UpperCamelCase_       )

					# first forward pass
					__lowercase			:  int								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						use_cache=UpperCamelCase_       )

					__lowercase			,__lowercase			:  List[str]								=       outputs.to_tuple()

					# create hypothetical multiple next token and extent to next_input_ids
					__lowercase			:  int								=       ids_tensor((self.batch_size, 3)				,						config.vocab_size       )
					__lowercase			:  str								=       ids_tensor((self.batch_size, 3)				,						2       )

					# append to next input_ids and
					__lowercase			:  Union[str, Any]								=       torch.cat([input_ids, next_tokens]				,						dim=-1       )
					__lowercase			:  List[Any]								=       torch.cat([attention_mask, next_attn_mask]				,						dim=-1       )

					__lowercase			:  Optional[Any]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_       )['''last_hidden_state''']
					__lowercase			:  List[Any]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						past_key_values=UpperCamelCase_       )[
					    '''last_hidden_state'''
					]

					# select random slice
					__lowercase			:  Optional[int]								=       ids_tensor((1,)				,						output_from_past.shape[-1]       ).item()
					__lowercase			:  Union[str, Any]								=       output_from_no_past[:, -3:, random_slice_idx].detach()
					__lowercase			:  Union[str, Any]								=       output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(UpperCamelCase_				,						UpperCamelCase_				,						atol=1E-3       )       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						*UpperCamelCase_				,						UpperCamelCase_=False       )						->       Union[str, Any]:
					__lowercase			:  Any								=       BioGptForCausalLM(UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					if gradient_checkpointing:
								model.gradient_checkpointing_enable()

					__lowercase			:  Dict								=       model(UpperCamelCase_				,						labels=UpperCamelCase_       )
					self.parent.assertEqual(result.loss.shape				,						()       )
					self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)       )
					result.loss.backward()
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						*UpperCamelCase_       )						->       List[str]:
					__lowercase			:  List[str]								=       BioGptModel(UpperCamelCase_       )
					__lowercase			:  List[str]								=       model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers       )
					for key in model.state_dict().keys():
								if "c_proj" in key and "weight" in key:
											self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]       ) - model_std       )				,						0.0_0_1       )
											self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]       ) - 0.0       )				,						0.0_1       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						*UpperCamelCase_       )						->       Any:
					__lowercase			:  Optional[Any]								=       self.num_labels
					__lowercase			:  List[str]								=       BioGptForTokenClassification(UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  List[str]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						token_type_ids=UpperCamelCase_       )
					self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.num_labels)       )
		def 	_lowerCamelCase							(						self       )						->       Dict:
					__lowercase			:  Tuple								=       self.prepare_config_and_inputs()
					(
					    (
					    __lowercase
					)			,(
					    __lowercase
					)			,(
					    __lowercase
					)			,(
					    __lowercase
					)			,(
					    __lowercase
					)			,(
					    __lowercase
					)			,(
					    __lowercase
					)			,
					)			:  Optional[Any]								=       config_and_inputs
					__lowercase			:  Optional[Any]								=       {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict


@require_torch
class   UpperCAmelCase_    (			snake_case   ,					snake_case   ,					snake_case   ,					unittest.TestCase		):
		UpperCamelCase							=(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		UpperCamelCase							=(BioGptForCausalLM,) if is_torch_available() else ()
		UpperCamelCase							=(
		    {
		        "feature-extraction": BioGptModel,
		        "text-classification": BioGptForSequenceClassification,
		        "text-generation": BioGptForCausalLM,
		        "token-classification": BioGptForTokenClassification,
		        "zero-shot": BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		UpperCamelCase							=False
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					__lowercase			:  int								=       BioGptModelTester(self       )
					__lowercase			:  Any								=       ConfigTester(self				,						config_class=UpperCamelCase_				,						hidden_size=37       )
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					self.config_tester.run_common_tests()
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					__lowercase			:  Tuple								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Dict:
					__lowercase			:  List[Any]								=       self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								__lowercase			:  List[Any]								=       type
								self.model_tester.create_and_check_model(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					__lowercase			:  Any								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       str:
					__lowercase			:  Dict								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_forward_and_backwards(*UpperCamelCase_				,						gradient_checkpointing=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  List[Any]								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Dict:
					__lowercase			:  Optional[int]								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  int								=       self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCamelCase_       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       str:
					__lowercase			:  Tuple								=       BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
					model.to(UpperCamelCase_       )
					__lowercase			:  List[Any]								=       BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )

					__lowercase			:  int								=       '''left'''

					# Define PAD Token = EOS Token = 50256
					__lowercase			:  Tuple								=       tokenizer.eos_token
					__lowercase			:  Optional[Any]								=       model.config.eos_token_id

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

					__lowercase			:  Tuple								=       tokenizer(UpperCamelCase_				,						return_tensors='''pt'''				,						padding=UpperCamelCase_       )
					__lowercase			:  Dict								=       inputs['''input_ids'''].to(UpperCamelCase_       )

					__lowercase			:  str								=       model.generate(
					    input_ids=UpperCamelCase_				,						attention_mask=inputs['''attention_mask'''].to(UpperCamelCase_       )				,						)

					__lowercase			:  Union[str, Any]								=       tokenizer(sentences[0]				,						return_tensors='''pt'''       ).input_ids.to(UpperCamelCase_       )
					__lowercase			:  Dict								=       model.generate(input_ids=UpperCamelCase_       )

					__lowercase			:  int								=       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
					__lowercase			:  List[Any]								=       tokenizer(sentences[1]				,						return_tensors='''pt'''       ).input_ids.to(UpperCamelCase_       )
					__lowercase			:  Any								=       model.generate(input_ids=UpperCamelCase_				,						max_length=model.config.max_length - num_paddings       )

					__lowercase			:  Union[str, Any]								=       tokenizer.batch_decode(UpperCamelCase_				,						skip_special_tokens=UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       tokenizer.decode(output_non_padded[0]				,						skip_special_tokens=UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       tokenizer.decode(output_padded[0]				,						skip_special_tokens=UpperCamelCase_       )

					__lowercase			:  Dict								=       [
					    '''Hello, my dog is a little bit bigger than a little bit.''',
					    '''Today, I have a good idea of how to use the information''',
					]
					self.assertListEqual(UpperCamelCase_				,						UpperCamelCase_       )
					self.assertListEqual(UpperCamelCase_				,						[non_padded_sentence, padded_sentence]       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       int:
					for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase			:  List[Any]								=       BioGptModel.from_pretrained(UpperCamelCase_       )
								self.assertIsNotNone(UpperCamelCase_       )
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			,__lowercase			:  str								=       self.model_tester.prepare_config_and_inputs_for_common()
					__lowercase			:  Optional[int]								=       3
					__lowercase			:  Optional[Any]								=       input_dict['''input_ids''']
					__lowercase			:  str								=       input_ids.ne(1       ).to(UpperCamelCase_       )
					__lowercase			:  Dict								=       ids_tensor([self.model_tester.batch_size]				,						self.model_tester.type_sequence_label_size       )
					__lowercase			:  Optional[Any]								=       BioGptForSequenceClassification(UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  Union[str, Any]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						labels=UpperCamelCase_       )
					self.assertEqual(result.logits.shape				,						(self.model_tester.batch_size, self.model_tester.num_labels)       )
		def 	_lowerCamelCase							(						self       )						->       str:
					__lowercase			,__lowercase			:  Any								=       self.model_tester.prepare_config_and_inputs_for_common()
					__lowercase			:  str								=       3
					__lowercase			:  Optional[int]								=       '''multi_label_classification'''
					__lowercase			:  List[str]								=       input_dict['''input_ids''']
					__lowercase			:  Dict								=       input_ids.ne(1       ).to(UpperCamelCase_       )
					__lowercase			:  Optional[int]								=       ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]				,						self.model_tester.type_sequence_label_size       ).to(torch.float       )
					__lowercase			:  Optional[int]								=       BioGptForSequenceClassification(UpperCamelCase_       )
					model.to(UpperCamelCase_       )
					model.eval()
					__lowercase			:  List[str]								=       model(UpperCamelCase_				,						attention_mask=UpperCamelCase_				,						labels=UpperCamelCase_       )
					self.assertEqual(result.logits.shape				,						(self.model_tester.batch_size, self.model_tester.num_labels)       )


@require_torch
class   UpperCAmelCase_    (			unittest.TestCase		):
		@slow
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					__lowercase			:  int								=       BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
					__lowercase			:  List[Any]								=       torch.tensor([[2, 48_05, 9, 6_56, 21]]       )
					__lowercase			:  int								=       model(UpperCamelCase_       )[0]

					__lowercase			:  Dict								=       4_23_84

					__lowercase			:  Tuple								=       torch.Size((1, 5, vocab_size)       )
					self.assertEqual(output.shape				,						UpperCamelCase_       )

					__lowercase			:  Union[str, Any]								=       torch.tensor(
					    [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]       )

					self.assertTrue(torch.allclose(output[:, :3, :3]				,						UpperCamelCase_				,						atol=1E-4       )       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  int								=       BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )
					__lowercase			:  List[str]								=       BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
					model.to(UpperCamelCase_       )

					torch.manual_seed(0       )
					__lowercase			:  int								=       tokenizer('''COVID-19 is'''				,						return_tensors='''pt'''       ).to(UpperCamelCase_       )
					__lowercase			:  Dict								=       model.generate(
					    **UpperCamelCase_				,						min_length=1_00				,						max_length=10_24				,						num_beams=5				,						early_stopping=UpperCamelCase_				,						)
					__lowercase			:  str								=       tokenizer.decode(output_ids[0]				,						skip_special_tokens=UpperCamelCase_       )

					__lowercase			:  Optional[int]								=       (
					    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
					    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
					    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
					    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
					    ''' more than 800,000 deaths.'''
					)
					self.assertEqual(UpperCamelCase_				,						UpperCamelCase_       )








'''simple docstring'''





def 		__snake_case(							)						->					Optional[Any]:
      for n in range(1	,  1_000_000						):
            yield n * (n + 1) // 2





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      snake_case__    :       Optional[int]       		= 1
      snake_case__    :       List[Any]       		= 2
      while i * i <= n:
            snake_case__    :       Dict       		= 0
            while n % i == 0:
                  n //= i
                  multiplicity += 1
            divisors_count *= multiplicity + 1
            i += 1
      if n > 1:
            divisors_count *= 2
      return divisors_count





def 		__snake_case(							)						->					List[str]:
      return next(i for i in triangle_number_generator() if count_divisors(_lowerCAmelCase						) > 500						)


if __name__ == "__main__":
       print(solution())
