



"""simple docstring"""






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      BioGptForCausalLM,
      BioGptForSequenceClassification,
      BioGptForTokenClassification,
      BioGptModel,
      BioGptTokenizer,
  )
  from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class 	__SCREAMING_SNAKE_CASE     :


     '''simple docstring'''





     def __init__(	self							:  str,  lowerCamelCase							:  int,  lowerCamelCase							:  Tuple=13,  lowerCamelCase							:  Any=7,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  Optional[int]=True,  lowerCamelCase							:  List[Any]=False,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  int=99,  lowerCamelCase							:  Optional[int]=32,  lowerCamelCase							:  int=5,  lowerCamelCase							:  List[str]=4,  lowerCamelCase							:  int=37,  lowerCamelCase							:  int="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Optional[int]=512,  lowerCamelCase							:  Dict=16,  lowerCamelCase							:  List[Any]=2,  lowerCamelCase							:  Optional[Any]=0.02,  lowerCamelCase							:  List[Any]=3,  lowerCamelCase							:  Any=4,  lowerCamelCase							:  Union[str, Any]=None,  )-> Dict:
        lowerCamelCase__		:		Optional[Any]		     =parent
        lowerCamelCase__		:		Optional[int]		     =batch_size
        lowerCamelCase__		:		Optional[int]		     =seq_length
        lowerCamelCase__		:		str		     =is_training
        lowerCamelCase__		:		int		     =use_input_mask
        lowerCamelCase__		:		Dict		     =use_token_type_ids
        lowerCamelCase__		:		Optional[int]		     =use_labels
        lowerCamelCase__		:		Tuple		     =vocab_size
        lowerCamelCase__		:		str		     =hidden_size
        lowerCamelCase__		:		Optional[int]		     =num_hidden_layers
        lowerCamelCase__		:		Union[str, Any]		     =num_attention_heads
        lowerCamelCase__		:		Optional[Any]		     =intermediate_size
        lowerCamelCase__		:		Optional[int]		     =hidden_act
        lowerCamelCase__		:		Tuple		     =hidden_dropout_prob
        lowerCamelCase__		:		Union[str, Any]		     =attention_probs_dropout_prob
        lowerCamelCase__		:		Union[str, Any]		     =max_position_embeddings
        lowerCamelCase__		:		Union[str, Any]		     =type_vocab_size
        lowerCamelCase__		:		Union[str, Any]		     =type_sequence_label_size
        lowerCamelCase__		:		Union[str, Any]		     =initializer_range
        lowerCamelCase__		:		List[Any]		     =num_labels
        lowerCamelCase__		:		Dict		     =num_choices
        lowerCamelCase__		:		Tuple		     =scope



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

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

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

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

        lowerCamelCase__		:		Union[str, Any]		     =self.get_config()

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



     def 		snake_case	(	self							:  Dict					)-> Optional[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=lowerCamelCase__,  initializer_range=self.initializer_range,  )



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



     def 		snake_case	(	self							:  int,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Union[str, Any],  )-> List[str]:
        lowerCamelCase__		:		Tuple		     =BioGptForCausalLM(config=lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()
        lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase__,  attention_mask=lowerCamelCase__,  token_type_ids=lowerCamelCase__,  labels=lowerCamelCase__					)
        self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)					)



     def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Tuple,  *lowerCamelCase							:  Optional[Any]					)-> Optional[int]:
        lowerCamelCase__		:		int		     =BioGptModel(config=lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()

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

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

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

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

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

        # get two different outputs
        lowerCamelCase__		:		Tuple		     =model(lowerCamelCase__,  attention_mask=lowerCamelCase__					)['''last_hidden_state''']
        lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase__,  past_key_values=lowerCamelCase__,  attention_mask=lowerCamelCase__					)['''last_hidden_state''']

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

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



     def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Dict,  *lowerCamelCase							:  str					)-> int:
        lowerCamelCase__		:		Optional[int]		     =BioGptModel(config=lowerCamelCase__					).to(lowerCamelCase__					).eval()

        lowerCamelCase__		:		Any		     =torch.ones(input_ids.shape,  dtype=torch.long,  device=lowerCamelCase__					)

        # first forward pass
        lowerCamelCase__		:		str		     =model(lowerCamelCase__,  attention_mask=lowerCamelCase__,  use_cache=lowerCamelCase__					)

        lowerCamelCase__		:		List[str]		     =outputs.to_tuple()

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

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

        lowerCamelCase__		:		int		     =model(lowerCamelCase__,  attention_mask=lowerCamelCase__					)['''last_hidden_state''']
        lowerCamelCase__		:		int		     =model(lowerCamelCase__,  attention_mask=lowerCamelCase__,  past_key_values=lowerCamelCase__					)[
            '''last_hidden_state'''
        ]

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



     def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any,  *lowerCamelCase							:  List[str],  lowerCamelCase							:  Tuple=False					)-> Optional[Any]:
        lowerCamelCase__		:		List[str]		     =BioGptForCausalLM(lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        if gradient_checkpointing:
           model.gradient_checkpointing_enable()

        lowerCamelCase__		:		List[str]		     =model(lowerCamelCase__,  labels=lowerCamelCase__					)
        self.parent.assertEqual(result.loss.shape,  ()					)
        self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)					)
        result.loss.backward()



     def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  Any,  *lowerCamelCase							:  List[Any]					)-> str:
        lowerCamelCase__		:		Tuple		     =BioGptModel(lowerCamelCase__					)
        lowerCamelCase__		:		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 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  int,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  str,  *lowerCamelCase							:  Optional[Any]					)-> int:
        lowerCamelCase__		:		List[str]		     =self.num_labels
        lowerCamelCase__		:		List[Any]		     =BioGptForTokenClassification(lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()
        lowerCamelCase__		:		str		     =model(lowerCamelCase__,  attention_mask=lowerCamelCase__,  token_type_ids=lowerCamelCase__					)
        self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)					)




     def 		snake_case	(	self							:  Any					)-> Optional[Any]:
        lowerCamelCase__		:		str		     =self.prepare_config_and_inputs()
        (
            lowerCamelCase__			
        )		:		int		     =config_and_inputs
        lowerCamelCase__		:		Optional[Any]		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict





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


     '''simple docstring'''





     _a             =							(
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     _a             =							(BioGptForCausalLM,) if is_torch_available() else ()
     _a             =							(
         {
             "feature-extraction": BioGptModel,
             "text-classification": BioGptForSequenceClassification,
             "text-generation": BioGptForCausalLM,
             "token-classification": BioGptForTokenClassification,
             "zero-shot": BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _a             =							False



     def 		snake_case	(	self							:  int					)-> List[Any]:
        lowerCamelCase__		:		Optional[int]		     =BioGptModelTester(self					)
        lowerCamelCase__		:		Any		     =ConfigTester(self,  config_class=lowerCamelCase__,  hidden_size=37					)



     def 		snake_case	(	self							:  Tuple					)-> str:
        self.config_tester.run_common_tests()



     def 		snake_case	(	self							:  Union[str, Any]					)-> Tuple:
        lowerCamelCase__		:		int		     =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__					)



     def 		snake_case	(	self							:  List[str]					)-> Dict:
        lowerCamelCase__		:		Tuple		     =self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           lowerCamelCase__		:		Optional[int]		     =type
           self.model_tester.create_and_check_model(*lowerCamelCase__					)



     def 		snake_case	(	self							:  Optional[int]					)-> List[str]:
        lowerCamelCase__		:		List[str]		     =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase__					)



     def 		snake_case	(	self							:  Any					)-> List[Any]:
        lowerCamelCase__		:		Tuple		     =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase__,  gradient_checkpointing=lowerCamelCase__					)



     def 		snake_case	(	self							:  Union[str, Any]					)-> Tuple:
        lowerCamelCase__		:		Optional[Any]		     =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase__					)



     def 		snake_case	(	self							:  int					)-> Optional[int]:
        lowerCamelCase__		:		Any		     =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase__					)



     def 		snake_case	(	self							:  Optional[Any]					)-> List[Any]:
        lowerCamelCase__		:		int		     =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase__					)



     @slow
     def 		snake_case	(	self							:  str					)-> int:
        lowerCamelCase__		:		Any		     =BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
        model.to(lowerCamelCase__					)
        lowerCamelCase__		:		List[str]		     =BioGptTokenizer.from_pretrained('''microsoft/biogpt'''					)

        lowerCamelCase__		:		int		     ='''left'''

        # Define PAD Token = EOS Token = 50256
        lowerCamelCase__		:		Tuple		     =tokenizer.eos_token
        lowerCamelCase__		:		Optional[Any]		     =model.config.eos_token_id

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

        lowerCamelCase__		:		Union[str, Any]		     =tokenizer(lowerCamelCase__,  return_tensors='''pt''',  padding=lowerCamelCase__					)
        lowerCamelCase__		:		Any		     =inputs['''input_ids'''].to(lowerCamelCase__					)

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

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

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

        lowerCamelCase__		:		Dict		     =tokenizer.batch_decode(lowerCamelCase__,  skip_special_tokens=lowerCamelCase__					)
        lowerCamelCase__		:		Dict		     =tokenizer.decode(output_non_padded[0],  skip_special_tokens=lowerCamelCase__					)
        lowerCamelCase__		:		int		     =tokenizer.decode(output_padded[0],  skip_special_tokens=lowerCamelCase__					)

        lowerCamelCase__		:		str		     =[
            '''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(lowerCamelCase__,  lowerCamelCase__					)
        self.assertListEqual(lowerCamelCase__,  [non_padded_sentence, padded_sentence]					)



     @slow
     def 		snake_case	(	self							:  Union[str, Any]					)-> int:






        for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCamelCase__		:		str		     =BioGptModel.from_pretrained(lowerCamelCase__					)
           self.assertIsNotNone(lowerCamelCase__					)



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




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





@require_torch
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


     '''simple docstring'''





     @slow
     def 		snake_case	(	self							:  Optional[int]					)-> List[str]:
        lowerCamelCase__		:		Dict		     =BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
        lowerCamelCase__		:		Any		     =torch.tensor([[2, 4805, 9, 656, 21]]					)
        lowerCamelCase__		:		List[Any]		     =model(lowerCamelCase__					)[0]

        lowerCamelCase__		:		Any		     =4_2384

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

        lowerCamelCase__		:		Dict		     =torch.tensor(
            [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]					)

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




     @slow
     def 		snake_case	(	self							:  Tuple					)-> str:
        lowerCamelCase__		:		Union[str, Any]		     =BioGptTokenizer.from_pretrained('''microsoft/biogpt'''					)
        lowerCamelCase__		:		Any		     =BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
        model.to(lowerCamelCase__					)

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

        lowerCamelCase__		:		List[str]		     =(
            '''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(lowerCamelCase__,  lowerCamelCase__					)









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			return int((input_a, input_a).count(0     ) != 0     )






def 					snake_case__       (		):
			"""simple docstring"""
			assert nand_gate(0							,		0     ) == 1
			assert nand_gate(0							,		1     ) == 1
			assert nand_gate(1							,		0     ) == 1
			assert nand_gate(1							,		1     ) == 0


if __name__ == "__main__":
		print(nand_gate(0, 0))
		print(nand_gate(0, 1))
		print(nand_gate(1, 0))
		print(nand_gate(1, 1))




