'''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 			__lowercase		:







     """simple docstring"""






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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size    )

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

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

         __UpperCamelCase	:	Any		    =  None
         __UpperCamelCase	:	Dict		    =  None
         __UpperCamelCase	:	List[str]		    =  None
         if self.use_labels:
             __UpperCamelCase	:	Tuple		    =  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	:	Dict		    =  ids_tensor([self.batch_size]     ,			self.num_choices    )

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

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







     def lowerCAmelCase     (		self    ):
         return BioGptConfig(
             vocab_size=self.vocab_size     ,			hidden_size=self.hidden_size     ,			num_hidden_layers=self.num_hidden_layers     ,			num_attention_heads=self.num_attention_heads     ,			intermediate_size=self.intermediate_size     ,			hidden_act=self.hidden_act     ,			hidden_dropout_prob=self.hidden_dropout_prob     ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,			max_position_embeddings=self.max_position_embeddings     ,			type_vocab_size=self.type_vocab_size     ,			is_decoder=_lowerCamelCase     ,			initializer_range=self.initializer_range     ,			)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Any		    =  BioGptModel(config=_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	List[Any]		    =  model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  model(_lowerCamelCase    )
         self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         __UpperCamelCase	:	Optional[int]		    =  BioGptForCausalLM(config=_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  BioGptModel(config=_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()

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

         # first forward pass
         __UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase    ).to_tuple()

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

         # change a random masked slice from input_ids
         __UpperCamelCase	:	List[Any]		    =  ids_tensor((1,)     ,			_lowerCamelCase    ).item() + 1
         __UpperCamelCase	:	Dict		    =  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	:	str		    =  torch.cat([input_ids, next_tokens]     ,			dim=-1    )
         __UpperCamelCase	:	str		    =  torch.cat(
             [attn_mask, torch.ones((attn_mask.shape[0], 1)     ,			dtype=torch.long     ,			device=_lowerCamelCase    )]     ,			dim=1     ,			)

         # get two different outputs
         __UpperCamelCase	:	Tuple		    =  model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase    )['last_hidden_state']
         __UpperCamelCase	:	List[Any]		    =  model(_lowerCamelCase     ,			past_key_values=_lowerCamelCase     ,			attention_mask=_lowerCamelCase    )['last_hidden_state']

         # select random slice
         __UpperCamelCase	:	Optional[int]		    =  ids_tensor((1,)     ,			output_from_past.shape[-1]    ).item()
         __UpperCamelCase	:	Optional[Any]		    =  output_from_no_past[:, -1, random_slice_idx].detach()
         __UpperCamelCase	:	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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  BioGptModel(config=_lowerCamelCase    ).to(_lowerCamelCase    ).eval()

         __UpperCamelCase	:	str		    =  torch.ones(input_ids.shape     ,			dtype=torch.long     ,			device=_lowerCamelCase    )

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

         __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  outputs.to_tuple()

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

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

         __UpperCamelCase	:	Optional[Any]		    =  model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase    )['last_hidden_state']
         __UpperCamelCase	:	Union[str, Any]		    =  model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase     ,			past_key_values=_lowerCamelCase    )[
             'last_hidden_state'
         ]

         # select random slice
         __UpperCamelCase	:	Union[str, Any]		    =  ids_tensor((1,)     ,			output_from_past.shape[-1]    ).item()
         __UpperCamelCase	:	List[str]		    =  output_from_no_past[:, -3:, random_slice_idx].detach()
         __UpperCamelCase	:	Dict		    =  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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase     ,			_lowerCamelCase=False    ):
         __UpperCamelCase	:	Optional[Any]		    =  BioGptForCausalLM(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         if gradient_checkpointing:
             model.gradient_checkpointing_enable()

         __UpperCamelCase	:	Union[str, Any]		    =  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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	Any		    =  BioGptModel(_lowerCamelCase    )
         __UpperCamelCase	:	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    )     ,			0.0_0_1    )
                 self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]    ) - 0.0    )     ,			0.0_1    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			*_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.num_labels
         __UpperCamelCase	:	Tuple		    =  BioGptForTokenClassification(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	Tuple		    =  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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					
         )	:	Optional[Any]		    =  config_and_inputs
         __UpperCamelCase	:	int		    =  {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  BioGptModelTester(self    )
         __UpperCamelCase	:	List[Any]		    =  ConfigTester(self     ,			config_class=_lowerCamelCase     ,			hidden_size=3_7    )







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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __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(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_forward_and_backwards(*_lowerCamelCase     ,			gradient_checkpointing=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_weight_initialization(*_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_for_token_classification(*_lowerCamelCase    )







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
         model.to(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  BioGptTokenizer.from_pretrained('microsoft/biogpt'    )

         __UpperCamelCase	:	Any		    =  'left'

         # Define PAD Token = EOS Token = 50256
         __UpperCamelCase	:	Dict		    =  tokenizer.eos_token
         __UpperCamelCase	:	Tuple		    =  model.config.eos_token_id

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

         __UpperCamelCase	:	Tuple		    =  tokenizer(_lowerCamelCase     ,			return_tensors='pt'     ,			padding=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  inputs['input_ids'].to(_lowerCamelCase    )

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

         __UpperCamelCase	:	str		    =  tokenizer(sentences[0]     ,			return_tensors='pt'    ).input_ids.to(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  model.generate(input_ids=_lowerCamelCase    )

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

         __UpperCamelCase	:	List[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  tokenizer.decode(output_non_padded[0]     ,			skip_special_tokens=_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  tokenizer.decode(output_padded[0]     ,			skip_special_tokens=_lowerCamelCase    )

         __UpperCamelCase	:	Optional[int]		    =  [
             '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 lowerCAmelCase     (		self    ):
         for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase	:	Any		    =  BioGptModel.from_pretrained(_lowerCamelCase    )
             self.assertIsNotNone(_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	Tuple		    =  self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase	:	str		    =  3
         __UpperCamelCase	:	List[Any]		    =  input_dict['input_ids']
         __UpperCamelCase	:	List[Any]		    =  input_ids.ne(1    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  ids_tensor([self.model_tester.batch_size]     ,			self.model_tester.type_sequence_label_size    )
         __UpperCamelCase	:	Dict		    =  BioGptForSequenceClassification(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	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)    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	Dict		    =  self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase	:	Optional[Any]		    =  3
         __UpperCamelCase	:	Tuple		    =  'multi_label_classification'
         __UpperCamelCase	:	Union[str, Any]		    =  input_dict['input_ids']
         __UpperCamelCase	:	List[Any]		    =  input_ids.ne(1    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  ids_tensor(
             [self.model_tester.batch_size, config.num_labels]     ,			self.model_tester.type_sequence_label_size    ).to(torch.float    )
         __UpperCamelCase	:	Tuple		    =  BioGptForSequenceClassification(_lowerCamelCase    )
         model.to(_lowerCamelCase    )
         model.eval()
         __UpperCamelCase	:	Optional[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 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
         __UpperCamelCase	:	Dict		    =  torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]    )
         __UpperCamelCase	:	Union[str, Any]		    =  model(_lowerCamelCase    )[0]

         __UpperCamelCase	:	Union[str, Any]		    =  4_2_3_8_4

         __UpperCamelCase	:	List[str]		    =  torch.Size((1, 5, vocab_size)    )
         self.assertEqual(output.shape     ,			_lowerCamelCase    )

         __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]     ,			_lowerCamelCase     ,			atol=1E-4    )    )







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  BioGptTokenizer.from_pretrained('microsoft/biogpt'    )
         __UpperCamelCase	:	Union[str, Any]		    =  BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
         model.to(_lowerCamelCase    )

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

         __UpperCamelCase	:	Dict		    =  (
             '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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



