





'''simple docstring'''







import math





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	bool:
						UpperCAmelCase_			:      Optional[int]     						=  math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

						return exponent == int(SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			float = 1 / 12345   ) ->	int:
						UpperCAmelCase_			:      str     						=  0
						UpperCAmelCase_			:      int     						=  0

						UpperCAmelCase_			:      int     						=  3
						while True:
												UpperCAmelCase_			:      str     						=  (integer**2 - 1) / 4
												# if candidate is an integer, then there is a partition for k
												if partition_candidate == int(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Tuple     						=  int(SCREAMING_SNAKE_CASE__   )
																		total_partitions += 1
																		if check_partition_perfect(SCREAMING_SNAKE_CASE__   ):
																								perfect_partitions += 1
												if perfect_partitions > 0:
																		if perfect_partitions / total_partitions < max_proportion:
																								return int(SCREAMING_SNAKE_CASE__   )
												integer += 1


if __name__ == "__main__":
					print(f'''{solution() = }''')












'''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 					__a      :

							def __init__(							self							:     List[str]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     str=13       ,     __magic_name__							:     Union[str, Any]=7       ,     __magic_name__							:     Union[str, Any]=True       ,     __magic_name__							:     Any=True       ,     __magic_name__							:     List[str]=False       ,     __magic_name__							:     Optional[int]=True       ,     __magic_name__							:     Dict=99       ,     __magic_name__							:     Tuple=32       ,     __magic_name__							:     int=5       ,     __magic_name__							:     Dict=4       ,     __magic_name__							:     Tuple=37       ,     __magic_name__							:     Optional[int]="gelu"       ,     __magic_name__							:     List[str]=0.1       ,     __magic_name__							:     Union[str, Any]=0.1       ,     __magic_name__							:     str=5_12       ,     __magic_name__							:     Union[str, Any]=16       ,     __magic_name__							:     int=2       ,     __magic_name__							:     List[Any]=0.0_2       ,     __magic_name__							:     Tuple=3       ,     __magic_name__							:     Union[str, Any]=4       ,     __magic_name__							:     Optional[int]=None       ,     )					->       str:







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

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size   )

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

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

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

													UpperCAmelCase_			:      int     						=  self.get_config()

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

							def     UpperCAmelCase__	(							self							:     Any   )					->       List[Any]:







													"""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=__magic_name__       ,     initializer_range=self.initializer_range       ,     )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     str       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  BioGptModel(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      Dict     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  model(__magic_name__   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Optional[int]       ,     )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  BioGptForCausalLM(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      List[Any]     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     token_type_ids=__magic_name__       ,     labels=__magic_name__   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)   )

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     *__magic_name__							:     Any   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  BioGptModel(config=__magic_name__   )
													model.to(__magic_name__   )
													model.eval()

													# create attention mask
													UpperCAmelCase_			:      Optional[Any]     						=  torch.ones(input_ids.shape       ,     dtype=torch.long       ,     device=__magic_name__   )
													UpperCAmelCase_			:      Any     						=  self.seq_length // 2
													UpperCAmelCase_			:      Tuple     						=  0

													# first forward pass
													UpperCAmelCase_						,       UpperCAmelCase_			:      Dict     						=  model(__magic_name__       ,     attention_mask=__magic_name__   ).to_tuple()

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

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

													# append to next input_ids and attn_mask
													UpperCAmelCase_			:      Tuple     						=  torch.cat([input_ids, next_tokens]       ,     dim=-1   )
													UpperCAmelCase_			:      int     						=  torch.cat(
													    [attn_mask, torch.ones((attn_mask.shape[0], 1)       ,     dtype=torch.long       ,     device=__magic_name__   )]       ,     dim=1       ,     )

													# get two different outputs
													UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )['''last_hidden_state''']
													UpperCAmelCase_			:      int     						=  model(__magic_name__       ,     past_key_values=__magic_name__       ,     attention_mask=__magic_name__   )['''last_hidden_state''']

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

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

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[Any]       ,     *__magic_name__							:     str   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  BioGptModel(config=__magic_name__   ).to(__magic_name__   ).eval()

													UpperCAmelCase_			:      Optional[int]     						=  torch.ones(input_ids.shape       ,     dtype=torch.long       ,     device=__magic_name__   )

													# first forward pass
													UpperCAmelCase_			:      Union[str, Any]     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     use_cache=__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  outputs.to_tuple()

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

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

													UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )['''last_hidden_state''']
													UpperCAmelCase_			:      Optional[Any]     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     past_key_values=__magic_name__   )[
													    '''last_hidden_state'''
													]

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

							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[int]       ,     *__magic_name__							:     Any       ,     __magic_name__							:     List[Any]=False   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  BioGptForCausalLM(__magic_name__   )
													model.to(__magic_name__   )
													if gradient_checkpointing:
																			model.gradient_checkpointing_enable()

													UpperCAmelCase_			:      List[str]     						=  model(__magic_name__       ,     labels=__magic_name__   )
													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							:     List[str]       ,     __magic_name__							:     Optional[int]       ,     *__magic_name__							:     List[str]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  BioGptModel(__magic_name__   )
													UpperCAmelCase_			:      Dict     						=  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     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Optional[Any]       ,     *__magic_name__							:     Any   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.num_labels
													UpperCAmelCase_			:      Any     						=  BioGptForTokenClassification(__magic_name__   )
													model.to(__magic_name__   )
													model.eval()
													UpperCAmelCase_			:      Any     						=  model(__magic_name__       ,     attention_mask=__magic_name__       ,     token_type_ids=__magic_name__   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)   )



							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.prepare_config_and_inputs()
													(
													    (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       
													)			:      int     						=  config_and_inputs
													UpperCAmelCase_			:      Any     						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict





@require_torch
class 					__a      (lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   str									=       (
							    (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[str]									=       False

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  BioGptModelTester(self   )
													UpperCAmelCase_			:      Optional[Any]     						=  ConfigTester(self       ,     config_class=__magic_name__       ,     hidden_size=37   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Tuple:







													"""simple docstring"""
													self.config_tester.run_common_tests()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			UpperCAmelCase_			:      str     						=  type
																			self.model_tester.create_and_check_model(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*__magic_name__       ,     gradient_checkpointing=__magic_name__   )

							def     UpperCAmelCase__	(							self							:     str   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*__magic_name__   )

							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*__magic_name__   )

							@slow
							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
													model.to(__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )

													UpperCAmelCase_			:      Tuple     						=  '''left'''

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

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

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

													UpperCAmelCase_			:      Any     						=  model.generate(
													    input_ids=__magic_name__       ,     attention_mask=inputs['''attention_mask'''].to(__magic_name__   )       ,     )

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

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

													UpperCAmelCase_			:      int     						=  tokenizer.batch_decode(__magic_name__       ,     skip_special_tokens=__magic_name__   )
													UpperCAmelCase_			:      Dict     						=  tokenizer.decode(output_non_padded[0]       ,     skip_special_tokens=__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  tokenizer.decode(output_padded[0]       ,     skip_special_tokens=__magic_name__   )

													UpperCAmelCase_			:      Optional[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(__magic_name__       ,     __magic_name__   )
													self.assertListEqual(__magic_name__       ,     [non_padded_sentence, padded_sentence]   )

							@slow
							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			UpperCAmelCase_			:      List[Any]     						=  BioGptModel.from_pretrained(__magic_name__   )
																			self.assertIsNotNone(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       str:







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



							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       List[Any]:







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





@require_torch
class 					__a      (unittest.TestCase     ):

							@slow
							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
													UpperCAmelCase_			:      List[str]     						=  torch.tensor([[2, 48_05, 9, 6_56, 21]]   )
													UpperCAmelCase_			:      str     						=  model(__magic_name__   )[0]

													UpperCAmelCase_			:      Optional[int]     						=  4_23_84

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

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

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



							@slow
							def     UpperCAmelCase__	(							self							:     Any   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )
													UpperCAmelCase_			:      str     						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''   )
													model.to(__magic_name__   )

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

													UpperCAmelCase_			:      Optional[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(__magic_name__       ,     __magic_name__   )





