'''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:




						def __init__(    self :       Tuple							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Any=13							,							UpperCamelCase_ :       Optional[Any]=7							,							UpperCamelCase_ :       int=True							,							UpperCamelCase_ :       Tuple=True							,							UpperCamelCase_ :       Optional[int]=False							,							UpperCamelCase_ :       Dict=True							,							UpperCamelCase_ :       Any=99							,							UpperCamelCase_ :       Optional[int]=32							,							UpperCamelCase_ :       Any=5							,							UpperCamelCase_ :       int=4							,							UpperCamelCase_ :       List[str]=37							,							UpperCamelCase_ :       Dict="gelu"							,							UpperCamelCase_ :       int=0.1							,							UpperCamelCase_ :       Tuple=0.1							,							UpperCamelCase_ :       Dict=5_12							,							UpperCamelCase_ :       Tuple=16							,							UpperCamelCase_ :       Optional[Any]=2							,							UpperCamelCase_ :       str=0.02							,							UpperCamelCase_ :       List[Any]=3							,							UpperCamelCase_ :       List[str]=4							,							UpperCamelCase_ :       Optional[int]=None							,							)     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :int						    = parent
													SCREAMING_SNAKE_CASE__     :List[str]						    = batch_size
													SCREAMING_SNAKE_CASE__     :int						    = seq_length
													SCREAMING_SNAKE_CASE__     :int						    = is_training
													SCREAMING_SNAKE_CASE__     :Dict						    = use_input_mask
													SCREAMING_SNAKE_CASE__     :Dict						    = use_token_type_ids
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = use_labels
													SCREAMING_SNAKE_CASE__     :Any						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :str						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :int						    = hidden_act
													SCREAMING_SNAKE_CASE__     :List[str]						    = hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :int						    = attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :str						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = type_vocab_size
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = type_sequence_label_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = initializer_range
													SCREAMING_SNAKE_CASE__     :Tuple						    = num_labels
													SCREAMING_SNAKE_CASE__     :int						    = num_choices
													SCREAMING_SNAKE_CASE__     :Any						    = scope




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :int						    = ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size      )

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

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

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

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

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




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Union[str, 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 :       Dict							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       int      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = BioGptModel(config=UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.eval()
													SCREAMING_SNAKE_CASE__     :Dict						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = model(UpperCamelCase_      )
													self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)      )




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Dict							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = BioGptForCausalLM(config=UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.eval()
													SCREAMING_SNAKE_CASE__     :List[Any]						    = 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 :       Union[str, Any]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       int							,							*UpperCamelCase_ :       int      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = BioGptModel(config=UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.eval()

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

													# first forward pass
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_      ).to_tuple()

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

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

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

													# get two different outputs
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_      )['last_hidden_state']
													SCREAMING_SNAKE_CASE__     :int						    = model(UpperCamelCase_							,							past_key_values=UpperCamelCase_							,							attention_mask=UpperCamelCase_      )['last_hidden_state']

													# select random slice
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = ids_tensor((1,)							,							output_from_past.shape[-1]      ).item()
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = output_from_no_past[:, -1, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE__     :Dict						    = 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 :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       List[Any]							,							*UpperCamelCase_ :       Union[str, Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Dict						    = BioGptModel(config=UpperCamelCase_      ).to(UpperCamelCase_      ).eval()

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.ones(input_ids.shape							,							dtype=torch.long							,							device=UpperCamelCase_      )

													# first forward pass
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							use_cache=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = outputs.to_tuple()

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

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

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_      )['last_hidden_state']
													SCREAMING_SNAKE_CASE__     :Tuple						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							past_key_values=UpperCamelCase_      )[
													    'last_hidden_state'
													]

													# select random slice
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = ids_tensor((1,)							,							output_from_past.shape[-1]      ).item()
													SCREAMING_SNAKE_CASE__     :str						    = output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE__     :str						    = 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 :       Optional[Any]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Tuple							,							*UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Any=False      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :str						    = BioGptForCausalLM(UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													SCREAMING_SNAKE_CASE__     :int						    = 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 :       Any							,							UpperCamelCase_ :       List[Any]							,							*UpperCamelCase_ :       Tuple      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = BioGptModel(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :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      )							,							0.001      )
																											self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]      ) - 0.0      )							,							0.01      )




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Union[str, Any]							,							*UpperCamelCase_ :       Optional[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.num_labels
													SCREAMING_SNAKE_CASE__     :Tuple						    = BioGptForTokenClassification(UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.eval()
													SCREAMING_SNAKE_CASE__     :Tuple						    = 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 :       str      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.prepare_config_and_inputs()
													(
													    (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      (
													    SCREAMING_SNAKE_CASE__
													)    ,      
													)     :Dict						    = config_and_inputs
													SCREAMING_SNAKE_CASE__     :Dict						    = {'input_ids': input_ids, 'attention_mask': input_mask}
													return config, inputs_dict




@require_torch
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Dict     		= (
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						A_			:							List[Any]     		= (BioGptForCausalLM,) if is_torch_available() else ()
						A_			:							Union[str, Any]     		= (
						    {
						        'feature-extraction': BioGptModel,
						        'text-classification': BioGptForSequenceClassification,
						        'text-generation': BioGptForCausalLM,
						        'token-classification': BioGptForTokenClassification,
						        'zero-shot': BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						A_			:							List[Any]     		= False




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Dict						    = BioGptModelTester(self      )
													SCREAMING_SNAKE_CASE__     :int						    = ConfigTester(self							,							config_class=UpperCamelCase_							,							hidden_size=37      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Tuple:
													self.config_tester.run_common_tests()




						def      __lowerCamelCase       (    self :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Any						    = self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = type
																				self.model_tester.create_and_check_model(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :str						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*UpperCamelCase_							,							gradient_checkpointing=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :int						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       List[str]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :str						    = BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
													model.to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = BioGptTokenizer.from_pretrained('microsoft/biogpt'      )

													SCREAMING_SNAKE_CASE__     :str						    = 'left'

													# Define PAD Token = EOS Token = 50256
													SCREAMING_SNAKE_CASE__     :str						    = tokenizer.eos_token
													SCREAMING_SNAKE_CASE__     :Dict						    = model.config.eos_token_id

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

													SCREAMING_SNAKE_CASE__     :str						    = tokenizer(UpperCamelCase_							,							return_tensors='pt'							,							padding=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = inputs['input_ids'].to(UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer(sentences[0]							,							return_tensors='pt'      ).input_ids.to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = model.generate(input_ids=UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :str						    = tokenizer.batch_decode(UpperCamelCase_							,							skip_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.decode(output_non_padded[0]							,							skip_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.decode(output_padded[0]							,							skip_special_tokens=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :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(UpperCamelCase_							,							UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							[non_padded_sentence, padded_sentence]      )




						@slow
						def      __lowerCamelCase       (    self :       str      )     ->				Optional[Any]:
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE__     :Any						    = BioGptModel.from_pretrained(UpperCamelCase_      )
																				self.assertIsNotNone(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Any      )     ->				Dict:
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__     :List[str]						    = 3
													SCREAMING_SNAKE_CASE__     :Tuple						    = input_dict['input_ids']
													SCREAMING_SNAKE_CASE__     :Dict						    = input_ids.ne(1      ).to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = ids_tensor([self.model_tester.batch_size]							,							self.model_tester.type_sequence_label_size      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = BioGptForSequenceClassification(UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.eval()
													SCREAMING_SNAKE_CASE__     :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      )     ->				int:
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__     :Tuple						    = 3
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'multi_label_classification'
													SCREAMING_SNAKE_CASE__     :Any						    = input_dict['input_ids']
													SCREAMING_SNAKE_CASE__     :int						    = input_ids.ne(1      ).to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]							,							self.model_tester.type_sequence_label_size      ).to(torch.float      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = BioGptForSequenceClassification(UpperCamelCase_      )
													model.to(UpperCamelCase_      )
													model.eval()
													SCREAMING_SNAKE_CASE__     :Dict						    = 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 _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@slow
						def      __lowerCamelCase       (    self :       Any      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.tensor([[2, 48_05, 9, 6_56, 21]]      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model(UpperCamelCase_      )[0]

													SCREAMING_SNAKE_CASE__     :str						    = 4_23_84

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.Size((1, 5, vocab_size)      )
													self.assertEqual(output.shape							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.tensor(
													    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]      )

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




						@slow
						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Any						    = BioGptTokenizer.from_pretrained('microsoft/biogpt'      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
													model.to(UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :Any						    = (
													    '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'''







from __future__ import annotations

import math
import random
from typing import Any




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       str      )     ->				None:
													SCREAMING_SNAKE_CASE__     :list[Any]						    = []
													SCREAMING_SNAKE_CASE__     :int						    = 0
													SCREAMING_SNAKE_CASE__     :int						    = 0




						def      __lowerCamelCase       (    self :       Any      )     ->				bool:
													return self.head == self.tail




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       Any      )     ->				None:
													self.data.append(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = self.tail + 1




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.data[self.head]
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.head + 1
													return ret




						def      __lowerCamelCase       (    self :       str      )     ->				int:
													return self.tail - self.head




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				None:
													print(self.data      )
													print('**************'      )
													print(self.data[self.head : self.tail]      )




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       Any      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Tuple						    = data
													SCREAMING_SNAKE_CASE__     :MyNode | None						    = None
													SCREAMING_SNAKE_CASE__     :MyNode | None						    = None
													SCREAMING_SNAKE_CASE__     :int						    = 1




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													return self.data




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				MyNode | None:
													return self.left




						def      __lowerCamelCase       (    self :       List[str]      )     ->				MyNode | None:
													return self.right




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				int:
													return self.height




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Any      )     ->				None:
													SCREAMING_SNAKE_CASE__     :List[str]						    = data




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       MyNode | None      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Dict						    = node




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       MyNode | None      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       int      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Dict						    = height


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode | None		)			->	int:


							'''simple docstring'''






							if node is None:
														return 0
							return node.get_height()


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							if a > b:
														return a
							return b


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							print('left rotation node:'				,						node.get_data()		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node.get_left()
							assert ret is not None
							node.set_left(ret.get_right()		)
							ret.set_right(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Any						    = my_max(get_height(node.get_right()		)				,						get_height(node.get_left()		)		) + 1
							node.set_height(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Dict						    = my_max(get_height(ret.get_right()		)				,						get_height(ret.get_left()		)		) + 1
							ret.set_height(UpperCAmelCase__		)
							return ret


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							print('right rotation node:'				,						node.get_data()		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = node.get_right()
							assert ret is not None
							node.set_right(ret.get_left()		)
							ret.set_left(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = my_max(get_height(node.get_right()		)				,						get_height(node.get_left()		)		) + 1
							node.set_height(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = my_max(get_height(ret.get_right()		)				,						get_height(ret.get_left()		)		) + 1
							ret.set_height(UpperCAmelCase__		)
							return ret


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = node.get_left()
							assert left_child is not None
							node.set_left(left_rotation(UpperCAmelCase__		)		)
							return right_rotation(UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = node.get_right()
							assert right_child is not None
							node.set_right(right_rotation(UpperCAmelCase__		)		)
							return left_rotation(UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode | None				,						UpperCAmelCase__				:  Any		)			->	MyNode | None:


							'''simple docstring'''






							if node is None:
														return MyNode(UpperCAmelCase__		)
							if data < node.get_data():
														node.set_left(insert_node(node.get_left()				,						UpperCAmelCase__		)		)
														if (
														    get_height(node.get_left()		) - get_height(node.get_right()		) == 2
														):  # an unbalance detected
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node.get_left()
																					assert left_child is not None
																					if (
																					    data < left_child.get_data()
																					):  # new node is the left child of the left child
																												SCREAMING_SNAKE_CASE__     :Dict						    = right_rotation(UpperCAmelCase__		)
																					else:
																												SCREAMING_SNAKE_CASE__     :str						    = lr_rotation(UpperCAmelCase__		)
							else:
														node.set_right(insert_node(node.get_right()				,						UpperCAmelCase__		)		)
														if get_height(node.get_right()		) - get_height(node.get_left()		) == 2:
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = node.get_right()
																					assert right_child is not None
																					if data < right_child.get_data():
																												SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rl_rotation(UpperCAmelCase__		)
																					else:
																												SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = left_rotation(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = my_max(get_height(node.get_right()		)				,						get_height(node.get_left()		)		) + 1
							node.set_height(UpperCAmelCase__		)
							return node


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	Any:


							'''simple docstring'''






							while True:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = root.get_right()
														if right_child is None:
																					break
														SCREAMING_SNAKE_CASE__     :str						    = right_child
							return root.get_data()


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	Any:


							'''simple docstring'''






							while True:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = root.get_left()
														if left_child is None:
																					break
														SCREAMING_SNAKE_CASE__     :List[Any]						    = left_child
							return root.get_data()


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode				,						UpperCAmelCase__				:  Any		)			->	MyNode | None:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = root.get_left()
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = root.get_right()
							if root.get_data() == data:
														if left_child is not None and right_child is not None:
																					SCREAMING_SNAKE_CASE__     :int						    = get_left_most(UpperCAmelCase__		)
																					root.set_data(UpperCAmelCase__		)
																					root.set_right(del_node(UpperCAmelCase__				,						UpperCAmelCase__		)		)
														elif left_child is not None:
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = left_child
														elif right_child is not None:
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = right_child
														else:
																					return None
							elif root.get_data() > data:
														if left_child is None:
																					print('No such data'		)
																					return root
														else:
																					root.set_left(del_node(UpperCAmelCase__				,						UpperCAmelCase__		)		)
							else:  # root.get_data() < data
														if right_child is None:
																					return root
														else:
																					root.set_right(del_node(UpperCAmelCase__				,						UpperCAmelCase__		)		)

							if get_height(UpperCAmelCase__		) - get_height(UpperCAmelCase__		) == 2:
														assert right_child is not None
														if get_height(right_child.get_right()		) > get_height(right_child.get_left()		):
																					SCREAMING_SNAKE_CASE__     :Any						    = left_rotation(UpperCAmelCase__		)
														else:
																					SCREAMING_SNAKE_CASE__     :int						    = rl_rotation(UpperCAmelCase__		)
							elif get_height(UpperCAmelCase__		) - get_height(UpperCAmelCase__		) == -2:
														assert left_child is not None
														if get_height(left_child.get_left()		) > get_height(left_child.get_right()		):
																					SCREAMING_SNAKE_CASE__     :Any						    = right_rotation(UpperCAmelCase__		)
														else:
																					SCREAMING_SNAKE_CASE__     :str						    = lr_rotation(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = my_max(get_height(root.get_right()		)				,						get_height(root.get_left()		)		) + 1
							root.set_height(UpperCAmelCase__		)
							return root




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       List[Any]      )     ->				None:
													SCREAMING_SNAKE_CASE__     :MyNode | None						    = None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				int:
													return get_height(self.root      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				None:
													print('insert:' + str(UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :Dict						    = insert_node(self.root							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Any      )     ->				None:
													print('delete:' + str(UpperCamelCase_      )      )
													if self.root is None:
																				print('Tree is empty!'      )
																				return
													SCREAMING_SNAKE_CASE__     :List[Any]						    = del_node(self.root							,							UpperCamelCase_      )




						def __str__(    self :       List[Any]							,							)     ->				str:  # a level traversale, gives a more intuitive look on the tree
													SCREAMING_SNAKE_CASE__     :List[str]						    = ''
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = MyQueue()
													q.push(self.root      )
													SCREAMING_SNAKE_CASE__     :int						    = self.get_height()
													if layer == 0:
																				return output
													SCREAMING_SNAKE_CASE__     :str						    = 0
													while not q.is_empty():
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = q.pop()
																				SCREAMING_SNAKE_CASE__     :List[str]						    = ' ' * int(math.pow(2							,							layer - 1      )      )
																				output += space
																				if node is None:
																											output += "*"
																											q.push(UpperCamelCase_      )
																											q.push(UpperCamelCase_      )
																				else:
																											output += str(node.get_data()      )
																											q.push(node.get_left()      )
																											q.push(node.get_right()      )
																				output += space
																				SCREAMING_SNAKE_CASE__     :Tuple						    = cnt + 1
																				for i in range(1_00      ):
																											if cnt == math.pow(2							,							UpperCamelCase_      ) - 1:
																																		SCREAMING_SNAKE_CASE__     :Optional[int]						    = layer - 1
																																		if layer == 0:
																																									output += "\n*************************************"
																																									return output
																																		output += "\n"
																																		break
													output += "\n*************************************"
													return output


def    lowerCamelCase       (			)			->	None:


							'''simple docstring'''






							import doctest

							doctest.testmod()


if __name__ == "__main__":
		_test()
		UpperCamelCase_      	=	AVLtree()
		UpperCamelCase_      	=	list(range(10))
		random.shuffle(lst)
		for i in lst:
				t.insert(i)
				print(str(t))
		random.shuffle(lst)
		for i in lst:
				t.del_node(i)
				print(str(t))






