



"""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 							lowerCamelCase_:





				'''simple docstring'''



				def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_2				,       lowerCamelCase__=5				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=3				,       lowerCamelCase__=4				,       lowerCamelCase__=None				,       ):
								_lowerCamelCase        =				parent
								_lowerCamelCase        =				batch_size
								_lowerCamelCase        =				seq_length
								_lowerCamelCase        =				is_training
								_lowerCamelCase        =				use_input_mask
								_lowerCamelCase        =				use_token_type_ids
								_lowerCamelCase        =				use_labels
								_lowerCamelCase        =				vocab_size
								_lowerCamelCase        =				hidden_size
								_lowerCamelCase        =				num_hidden_layers
								_lowerCamelCase        =				num_attention_heads
								_lowerCamelCase        =				intermediate_size
								_lowerCamelCase        =				hidden_act
								_lowerCamelCase        =				hidden_dropout_prob
								_lowerCamelCase        =				attention_probs_dropout_prob
								_lowerCamelCase        =				max_position_embeddings
								_lowerCamelCase        =				type_vocab_size
								_lowerCamelCase        =				type_sequence_label_size
								_lowerCamelCase        =				initializer_range
								_lowerCamelCase        =				num_labels
								_lowerCamelCase        =				num_choices
								_lowerCamelCase        =				scope

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

								_lowerCamelCase        =				None
								if self.use_input_mask:
												_lowerCamelCase        =				random_attention_mask([self.batch_size, self.seq_length]   )

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

								_lowerCamelCase        =				None
								_lowerCamelCase        =				None
								_lowerCamelCase        =				None
								if self.use_labels:
												_lowerCamelCase        =				ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )
												_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels   )
												_lowerCamelCase        =				ids_tensor([self.batch_size]				,       self.num_choices   )

								_lowerCamelCase        =				self.get_config()

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

				def 			snake_case__      (    self   ):
								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=SCREAMING_SNAKE_CASE_				,       initializer_range=self.initializer_range				,       )

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
								_lowerCamelCase        =				BioGptModel(config=SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_   )
								self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
								_lowerCamelCase        =				BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_				,       token_type_ids=SCREAMING_SNAKE_CASE_				,       labels=SCREAMING_SNAKE_CASE_   )
								self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__   ):
								_lowerCamelCase        =				BioGptModel(config=SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()

								# create attention mask
								_lowerCamelCase        =				torch.ones(input_ids.shape				,       dtype=torch.long				,       device=SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				self.seq_length // 2
								_lowerCamelCase        =				0

								# first forward pass
								_lowerCamelCase  ,     _lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_   ).to_tuple()

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

								# change a random masked slice from input_ids
								_lowerCamelCase        =				ids_tensor((1,)				,       SCREAMING_SNAKE_CASE_   ).item() + 1
								_lowerCamelCase        =				ids_tensor((self.batch_size, 1)				,       config.vocab_size   ).squeeze(-1   )
								_lowerCamelCase        =				random_other_next_tokens

								# append to next input_ids and attn_mask
								_lowerCamelCase        =				torch.cat([input_ids, next_tokens]				,       dim=-1   )
								_lowerCamelCase        =				torch.cat(
								    [attn_mask, torch.ones((attn_mask.shape[0], 1)				,       dtype=torch.long				,       device=SCREAMING_SNAKE_CASE_   )]				,       dim=1				,       )

								# get two different outputs
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_   )['''last_hidden_state''']
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       past_key_values=SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_   )['''last_hidden_state''']

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

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

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__   ):
								_lowerCamelCase        =				BioGptModel(config=SCREAMING_SNAKE_CASE_   ).to(SCREAMING_SNAKE_CASE_   ).eval()

								_lowerCamelCase        =				torch.ones(input_ids.shape				,       dtype=torch.long				,       device=SCREAMING_SNAKE_CASE_   )

								# first forward pass
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_				,       use_cache=SCREAMING_SNAKE_CASE_   )

								_lowerCamelCase  ,     _lowerCamelCase        =				outputs.to_tuple()

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

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

								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_   )['''last_hidden_state''']
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_				,       past_key_values=SCREAMING_SNAKE_CASE_   )[
								    '''last_hidden_state'''
								]

								# select random slice
								_lowerCamelCase        =				ids_tensor((1,)				,       output_from_past.shape[-1]   ).item()
								_lowerCamelCase        =				output_from_no_past[:, -3:, random_slice_idx].detach()
								_lowerCamelCase        =				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(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_				,       atol=1e-3   )   )

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__				,       lowerCamelCase__=False   ):
								_lowerCamelCase        =				BioGptForCausalLM(SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								if gradient_checkpointing:
												model.gradient_checkpointing_enable()

								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       labels=SCREAMING_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 			snake_case__      (    self				,       lowerCamelCase__				,       *lowerCamelCase__   ):
								_lowerCamelCase        =				BioGptModel(SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       *lowerCamelCase__   ):
								_lowerCamelCase        =				self.num_labels
								_lowerCamelCase        =				BioGptForTokenClassification(SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_				,       token_type_ids=SCREAMING_SNAKE_CASE_   )
								self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)   )





				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.prepare_config_and_inputs()
								(
								    (
								    _lowerCamelCase 
								)  ,     (
								    _lowerCamelCase 
								)  ,     (
								    _lowerCamelCase 
								)  ,     (
								    _lowerCamelCase 
								)  ,     (
								    _lowerCamelCase 
								)  ,     (
								    _lowerCamelCase 
								)  ,     (
								    _lowerCamelCase 
								)  ,      
								)        =				config_and_inputs
								_lowerCamelCase        =				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict


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





				'''simple docstring'''



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

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				BioGptModelTester(self   )
								_lowerCamelCase        =				ConfigTester(self				,       config_class=SCREAMING_SNAKE_CASE_				,       hidden_size=3_7   )

				def 			snake_case__      (    self   ):
								self.config_tester.run_common_tests()

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												_lowerCamelCase        =				type
												self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE_				,       gradient_checkpointing=SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_   )

				@slow
				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
								model.to(SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )

								_lowerCamelCase        =				'''left'''

								# Define PAD Token = EOS Token = 50256
								_lowerCamelCase        =				tokenizer.eos_token
								_lowerCamelCase        =				model.config.eos_token_id

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

								_lowerCamelCase        =				tokenizer(SCREAMING_SNAKE_CASE_				,       return_tensors='''pt'''				,       padding=SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				inputs['''input_ids'''].to(SCREAMING_SNAKE_CASE_   )

								_lowerCamelCase        =				model.generate(
								    input_ids=SCREAMING_SNAKE_CASE_				,       attention_mask=inputs['''attention_mask'''].to(SCREAMING_SNAKE_CASE_   )				,       )

								_lowerCamelCase        =				tokenizer(sentences[0]				,       return_tensors='''pt'''   ).input_ids.to(SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				model.generate(input_ids=SCREAMING_SNAKE_CASE_   )

								_lowerCamelCase        =				inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
								_lowerCamelCase        =				tokenizer(sentences[1]				,       return_tensors='''pt'''   ).input_ids.to(SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				model.generate(input_ids=SCREAMING_SNAKE_CASE_				,       max_length=model.config.max_length - num_paddings   )

								_lowerCamelCase        =				tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				,       skip_special_tokens=SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				tokenizer.decode(output_padded[0]				,       skip_special_tokens=SCREAMING_SNAKE_CASE_   )

								_lowerCamelCase        =				[
								    '''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(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_   )
								self.assertListEqual(SCREAMING_SNAKE_CASE_				,       [non_padded_sentence, padded_sentence]   )

				@slow
				def 			snake_case__      (    self   ):
								for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCamelCase        =				BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_   )
												self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCamelCase        =				3
								_lowerCamelCase        =				input_dict['''input_ids''']
								_lowerCamelCase        =				input_ids.ne(1   ).to(SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				ids_tensor([self.model_tester.batch_size]				,       self.model_tester.type_sequence_label_size   )
								_lowerCamelCase        =				BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_				,       labels=SCREAMING_SNAKE_CASE_   )
								self.assertEqual(result.logits.shape				,       (self.model_tester.batch_size, self.model_tester.num_labels)   )





				def 			snake_case__      (    self   ):
								_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCamelCase        =				3
								_lowerCamelCase        =				'''multi_label_classification'''
								_lowerCamelCase        =				input_dict['''input_ids''']
								_lowerCamelCase        =				input_ids.ne(1   ).to(SCREAMING_SNAKE_CASE_   )
								_lowerCamelCase        =				ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]				,       self.model_tester.type_sequence_label_size   ).to(torch.float   )
								_lowerCamelCase        =				BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_				,       attention_mask=SCREAMING_SNAKE_CASE_				,       labels=SCREAMING_SNAKE_CASE_   )
								self.assertEqual(result.logits.shape				,       (self.model_tester.batch_size, self.model_tester.num_labels)   )


@require_torch
class 							lowerCamelCase_(   unittest.TestCase    ):





				'''simple docstring'''



				@slow
				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
								_lowerCamelCase        =				torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]   )
								_lowerCamelCase        =				model(SCREAMING_SNAKE_CASE_   )[0]

								_lowerCamelCase        =				4_2_3_8_4

								_lowerCamelCase        =				torch.Size((1, 5, vocab_size)   )
								self.assertEqual(output.shape				,       SCREAMING_SNAKE_CASE_   )

								_lowerCamelCase        =				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]				,       SCREAMING_SNAKE_CASE_				,       atol=1e-4   )   )





				@slow
				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )
								_lowerCamelCase        =				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
								model.to(SCREAMING_SNAKE_CASE_   )

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

								_lowerCamelCase        =				(
								    '''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(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_   )





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   input_matrix.conj().T							)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

				while not convergence:
								# Multiple matrix by the vector.
								_lowerCamelCase        =				np.dot(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								_lowerCamelCase        =				vector.conj().T if is_complex else vector.T
								_lowerCamelCase        =				np.dot(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
