'''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 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=1_3				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=9_9				,		snake_case_=3_2				,		snake_case_=5				,		snake_case_=4				,		snake_case_=3_7				,		snake_case_="gelu"				,		snake_case_=0.1				,		snake_case_=0.1				,		snake_case_=5_1_2				,		snake_case_=1_6				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=3				,		snake_case_=4				,		snake_case_=None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				parent
       UpperCAmelCase_							:       int       =				batch_size
       UpperCAmelCase_							:       str       =				seq_length
       UpperCAmelCase_							:       Tuple       =				is_training
       UpperCAmelCase_							:       Dict       =				use_input_mask
       UpperCAmelCase_							:       Optional[Any]       =				use_token_type_ids
       UpperCAmelCase_							:       Optional[Any]       =				use_labels
       UpperCAmelCase_							:       Tuple       =				vocab_size
       UpperCAmelCase_							:       Union[str, Any]       =				hidden_size
       UpperCAmelCase_							:       Dict       =				num_hidden_layers
       UpperCAmelCase_							:       Optional[int]       =				num_attention_heads
       UpperCAmelCase_							:       Optional[Any]       =				intermediate_size
       UpperCAmelCase_							:       Any       =				hidden_act
       UpperCAmelCase_							:       Optional[Any]       =				hidden_dropout_prob
       UpperCAmelCase_							:       Dict       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Union[str, Any]       =				max_position_embeddings
       UpperCAmelCase_							:       Dict       =				type_vocab_size
       UpperCAmelCase_							:       str       =				type_sequence_label_size
       UpperCAmelCase_							:       int       =				initializer_range
       UpperCAmelCase_							:       Tuple       =				num_labels
       UpperCAmelCase_							:       int       =				num_choices
       UpperCAmelCase_							:       str       =				scope


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size						)

       UpperCAmelCase_							:       Union[str, Any]       =				None
       if self.use_input_mask:
            UpperCAmelCase_							:       str       =				random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCAmelCase_							:       str       =				None
       if self.use_token_type_ids:
            UpperCAmelCase_							:       Dict       =				ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size						)

       UpperCAmelCase_							:       Union[str, Any]       =				None
       UpperCAmelCase_							:       Dict       =				None
       UpperCAmelCase_							:       List[Any]       =				None
       if self.use_labels:
            UpperCAmelCase_							:       Optional[Any]       =				ids_tensor([self.batch_size]				,		self.type_sequence_label_size						)
            UpperCAmelCase_							:       Union[str, Any]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels						)
            UpperCAmelCase_							:       Any       =				ids_tensor([self.batch_size]				,		self.num_choices						)

       UpperCAmelCase_							:       List[Any]       =				self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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=snake_case_				,		initializer_range=self.initializer_range				,		)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				BioGptModel(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Any       =				model(snake_case_				,		attention_mask=snake_case_						)
       UpperCAmelCase_							:       Tuple       =				model(snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				BioGptForCausalLM(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[int]       =				model(snake_case_				,		attention_mask=snake_case_				,		token_type_ids=snake_case_				,		labels=snake_case_						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				BioGptModel(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()

       # create attention mask
       UpperCAmelCase_							:       int       =				torch.ones(input_ids.shape				,		dtype=torch.long				,		device=snake_case_						)
       UpperCAmelCase_							:       str       =				self.seq_length // 2
       UpperCAmelCase_							:       Union[str, Any]       =				0

       # first forward pass
       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[str]       =				model(snake_case_				,		attention_mask=snake_case_						).to_tuple()

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

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

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

       # get two different outputs
       UpperCAmelCase_							:       str       =				model(snake_case_				,		attention_mask=snake_case_						)['last_hidden_state']
       UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_				,		past_key_values=snake_case_				,		attention_mask=snake_case_						)['last_hidden_state']

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

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-3						)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				BioGptModel(config=snake_case_						).to(snake_case_						).eval()

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

       # first forward pass
       UpperCAmelCase_							:       List[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		use_cache=snake_case_						)

       UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCAmelCase_							:       Optional[Any]       =				ids_tensor((self.batch_size, 3)				,		config.vocab_size						)
       UpperCAmelCase_							:       int       =				ids_tensor((self.batch_size, 3)				,		2						)

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

       UpperCAmelCase_							:       int       =				model(snake_case_				,		attention_mask=snake_case_						)['last_hidden_state']
       UpperCAmelCase_							:       Dict       =				model(snake_case_				,		attention_mask=snake_case_				,		past_key_values=snake_case_						)[
           'last_hidden_state'
       ]

       # select random slice
       UpperCAmelCase_							:       int       =				ids_tensor((1,)				,		output_from_past.shape[-1]						).item()
       UpperCAmelCase_							:       List[str]       =				output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCAmelCase_							:       Tuple       =				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(snake_case_				,		snake_case_				,		atol=1E-3						)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		*snake_case_				,		snake_case_=False						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				BioGptForCausalLM(snake_case_						)
       model.to(snake_case_						)
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_				,		labels=snake_case_						)
       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 _UpperCamelCase					(			self				,		snake_case_				,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				BioGptModel(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				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_01						)
                 self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]						) - 0.0						)				,		0.01						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.num_labels
       UpperCAmelCase_							:       str       =				BioGptForTokenClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		token_type_ids=snake_case_						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.num_labels)						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      
       )							:       Optional[Any]       =				config_and_inputs
       UpperCAmelCase_							:       str       =				{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		(
      (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
      if is_torch_available()
      else ()
  )
  lowerCamelCase_		:Optional[Any]						   =		(BioGptForCausalLM,) if is_torch_available() else ()
  lowerCamelCase_		:List[Any]						   =		(
      {
          '''feature-extraction''': BioGptModel,
          '''text-classification''': BioGptForSequenceClassification,
          '''text-generation''': BioGptForCausalLM,
          '''token-classification''': BioGptForTokenClassification,
          '''zero-shot''': BioGptForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase_		:Optional[int]						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				BioGptModelTester(self						)
       UpperCAmelCase_							:       Any       =				ConfigTester(self				,		config_class=snake_case_				,		hidden_size=3_7						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCAmelCase_							:       List[str]       =				type
            self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*snake_case_				,		gradient_checkpointing=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*snake_case_						)


  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				BioGptForCausalLM.from_pretrained('microsoft/biogpt'						)
       model.to(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				BioGptTokenizer.from_pretrained('microsoft/biogpt'						)

       UpperCAmelCase_							:       Tuple       =				'left'

       # Define PAD Token = EOS Token = 50256
       UpperCAmelCase_							:       Any       =				tokenizer.eos_token
       UpperCAmelCase_							:       Union[str, Any]       =				model.config.eos_token_id

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

       UpperCAmelCase_							:       Optional[int]       =				tokenizer(snake_case_				,		return_tensors='pt'				,		padding=snake_case_						)
       UpperCAmelCase_							:       int       =				inputs['input_ids'].to(snake_case_						)

       UpperCAmelCase_							:       Optional[int]       =				model.generate(
           input_ids=snake_case_				,		attention_mask=inputs['attention_mask'].to(snake_case_						)				,		)

       UpperCAmelCase_							:       List[Any]       =				tokenizer(sentences[0]				,		return_tensors='pt'						).input_ids.to(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				model.generate(input_ids=snake_case_						)

       UpperCAmelCase_							:       List[str]       =				inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
       UpperCAmelCase_							:       Dict       =				tokenizer(sentences[1]				,		return_tensors='pt'						).input_ids.to(snake_case_						)
       UpperCAmelCase_							:       List[str]       =				model.generate(input_ids=snake_case_				,		max_length=model.config.max_length - num_paddings						)

       UpperCAmelCase_							:       List[Any]       =				tokenizer.batch_decode(snake_case_				,		skip_special_tokens=snake_case_						)
       UpperCAmelCase_							:       Tuple       =				tokenizer.decode(output_non_padded[0]				,		skip_special_tokens=snake_case_						)
       UpperCAmelCase_							:       str       =				tokenizer.decode(output_padded[0]				,		skip_special_tokens=snake_case_						)

       UpperCAmelCase_							:       Union[str, Any]       =				[
           '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(snake_case_				,		snake_case_						)
       self.assertListEqual(snake_case_				,		[non_padded_sentence, padded_sentence]						)


  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_							:       List[Any]       =				BioGptModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Tuple       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       List[str]       =				3
       UpperCAmelCase_							:       Optional[Any]       =				input_dict['input_ids']
       UpperCAmelCase_							:       Optional[Any]       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size						)
       UpperCAmelCase_							:       Any       =				BioGptForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[int]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Union[str, Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       Dict       =				3
       UpperCAmelCase_							:       List[str]       =				'multi_label_classification'
       UpperCAmelCase_							:       Optional[int]       =				input_dict['input_ids']
       UpperCAmelCase_							:       int       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				ids_tensor(
           [self.model_tester.batch_size, config.num_labels]				,		self.model_tester.type_sequence_label_size						).to(torch.float						)
       UpperCAmelCase_							:       List[str]       =				BioGptForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				BioGptForCausalLM.from_pretrained('microsoft/biogpt'						)
       UpperCAmelCase_							:       Optional[Any]       =				torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]						)
       UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_						)[0]

       UpperCAmelCase_							:       List[str]       =				4_2_3_8_4

       UpperCAmelCase_							:       Union[str, Any]       =				torch.Size((1, 5, vocab_size)						)
       self.assertEqual(output.shape				,		snake_case_						)

       UpperCAmelCase_							:       List[Any]       =				torch.tensor(
           [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]						)

       self.assertTrue(torch.allclose(output[:, :3, :3]				,		snake_case_				,		atol=1E-4						)						)






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				BioGptTokenizer.from_pretrained('microsoft/biogpt'						)
       UpperCAmelCase_							:       Tuple       =				BioGptForCausalLM.from_pretrained('microsoft/biogpt'						)
       model.to(snake_case_						)

       torch.manual_seed(0						)
       UpperCAmelCase_							:       int       =				tokenizer('COVID-19 is'				,		return_tensors='pt'						).to(snake_case_						)
       UpperCAmelCase_							:       str       =				model.generate(
           **snake_case_				,		min_length=1_0_0				,		max_length=1_0_2_4				,		num_beams=5				,		early_stopping=snake_case_				,		)
       UpperCAmelCase_							:       Tuple       =				tokenizer.decode(output_ids[0]				,		skip_special_tokens=snake_case_						)

       UpperCAmelCase_							:       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(snake_case_				,		snake_case_						)







'''simple docstring'''


snake_case__					:      str				      =  '''Tobias Carryer'''

from time import time






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=int(time()						)						):  # noqa: B008




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				multiplier
       UpperCAmelCase_							:       Dict       =				increment
       UpperCAmelCase_							:       Tuple       =				modulo
       UpperCAmelCase_							:       Dict       =				seed






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				(self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__					:      Any				      =  LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
 while True:
  print(lcg.next_number())






