import baseaa







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     return baseaa.aaaencode(string.encode('utf-8' ) )







def     _a				(      lowercase__  :			bytes ):







     '''simple docstring'''


     return baseaa.aaadecode(lowercase__ ).decode('utf-8' )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




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       snake_case					:




  def __init__(      self     :	List[Any]     , a_     :	Dict     , a_     :	Any=13     , a_     :	Any=7     , a_     :	Tuple=True     , a_     :	Tuple=True     , a_     :	Optional[int]=False     , a_     :	Dict=True     , a_     :	Optional[Any]=99     , a_     :	Any=32     , a_     :	Dict=5     , a_     :	Tuple=4     , a_     :	List[str]=37     , a_     :	Union[str, Any]="gelu"     , a_     :	Dict=0.1     , a_     :	Tuple=0.1     , a_     :	List[str]=512     , a_     :	List[str]=16     , a_     :	List[str]=2     , a_     :	Optional[int]=0.02     , a_     :	List[str]=3     , a_     :	Union[str, Any]=4     , a_     :	Optional[Any]=None     , )->       Optional[int]:




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




  def 	__lowercase(      self     :	Tuple				)->       Optional[Any]:




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

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

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

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

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

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




  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """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=a_     , initializer_range=self.initializer_range     , )




  def 	__lowercase(      self     :	Any     , a_     :	str     , a_     :	Tuple     , a_     :	Dict     , a_     :	Optional[int]     , a_     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	Tuple				)->       Any:




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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		BioGptForCausalLM(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_     , attention_mask=a_     , token_type_ids=a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Optional[int]     , a_     :	Union[str, Any]     , a_     :	Any     , a_     :	Any     , a_     :	Optional[int]     , *a_     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptModel(config=a_				)
       model.to(a_				)
       model.eval()

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

       # first forward pass
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_     , attention_mask=a_				).to_tuple()

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

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

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

       # get two different outputs
       SCREAMING_SNAKE_CASE__				:				str			=		model(a_     , attention_mask=a_				)['last_hidden_state']
       SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_     , past_key_values=a_     , attention_mask=a_				)['last_hidden_state']

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

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




  def 	__lowercase(      self     :	str     , a_     :	List[Any]     , a_     :	str     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , *a_     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		BioGptModel(config=a_				).to(a_				).eval()

       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.ones(input_ids.shape     , dtype=torch.long     , device=a_				)

       # first forward pass
       SCREAMING_SNAKE_CASE__				:				Any			=		model(a_     , attention_mask=a_     , use_cache=a_				)

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Any			=		outputs.to_tuple()

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

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__				:				int			=		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__				:				int			=		model(a_     , attention_mask=a_				)['last_hidden_state']
       SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_     , attention_mask=a_     , past_key_values=a_				)[
           'last_hidden_state'
       ]

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

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

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




  def 	__lowercase(      self     :	Any     , a_     :	List[str]     , a_     :	Optional[int]     , a_     :	Any     , a_     :	Tuple     , a_     :	Any     , *a_     :	List[Any]     , a_     :	Union[str, Any]=False				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		BioGptForCausalLM(a_				)
       model.to(a_				)
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_     , labels=a_				)
       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 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[str]     , *a_     :	Optional[int]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptModel(a_				)
       SCREAMING_SNAKE_CASE__				:				List[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 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	Tuple     , a_     :	List[str]     , a_     :	Any     , a_     :	str     , *a_     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self.num_labels
       SCREAMING_SNAKE_CASE__				:				str			=		BioGptForTokenClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_     , attention_mask=a_     , token_type_ids=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)				)




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		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__
       )			,							
       )				:				Tuple			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
      if is_torch_available()
      else ()
  )
  lowercase_			=     (BioGptForCausalLM,) if is_torch_available() else ()
  lowercase_			=     (
      {
          'feature-extraction': BioGptModel,
          'text-classification': BioGptForSequenceClassification,
          'text-generation': BioGptForCausalLM,
          'token-classification': BioGptForTokenClassification,
          'zero-shot': BioGptForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowercase_			=     False




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		BioGptModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ConfigTester(self     , config_class=a_     , hidden_size=37				)




  def 	__lowercase(      self     :	Tuple				)->       int:




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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		type
            self.model_tester.create_and_check_model(*a_				)




  def 	__lowercase(      self     :	int				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*a_     , gradient_checkpointing=a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*a_				)




  def 	__lowercase(      self     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
       model.to(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptTokenizer.from_pretrained('microsoft/biogpt'				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		'left'

       # Define PAD Token = EOS Token = 50256
       SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer.eos_token
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model.config.eos_token_id

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

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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		model.generate(
           input_ids=a_     , attention_mask=inputs['attention_mask'].to(a_				)     , )

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

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

       SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer.batch_decode(a_     , skip_special_tokens=a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		tokenizer.decode(output_non_padded[0]     , skip_special_tokens=a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer.decode(output_padded[0]     , skip_special_tokens=a_				)

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




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




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




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




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




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




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




@require_torch
class       snake_case					(      unittest.TestCase						):




  @slow
  def 	__lowercase(      self     :	Union[str, Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([[2, 4805, 9, 656, 21]]				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_				)[0]

       SCREAMING_SNAKE_CASE__				:				List[str]			=		4_2384

       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.Size((1, 5, vocab_size)				)
       self.assertEqual(output.shape     , a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		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]     , a_     , atol=1e-4				)				)




  @slow
  def 	__lowercase(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		BioGptTokenizer.from_pretrained('microsoft/biogpt'				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
       model.to(a_				)

       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		tokenizer('COVID-19 is'     , return_tensors='pt'				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		model.generate(
           **a_     , min_length=100     , max_length=1024     , num_beams=5     , early_stopping=a_     , )
       SCREAMING_SNAKE_CASE__				:				int			=		tokenizer.decode(output_ids[0]     , skip_special_tokens=a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, 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(a_     , a_				)



