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						:

 def __init__( self	:							int   ,      snake_case	:							int   ,      snake_case	:							str=1_3   ,      snake_case	:							Optional[Any]=7   ,      snake_case	:							Tuple=True   ,      snake_case	:							Optional[int]=True   ,      snake_case	:							List[str]=False   ,      snake_case	:							int=True   ,      snake_case	:							int=9_9   ,      snake_case	:							Optional[int]=3_2   ,      snake_case	:							Optional[Any]=5   ,      snake_case	:							Optional[Any]=4   ,      snake_case	:							int=3_7   ,      snake_case	:							Union[str, Any]="gelu"   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							Union[str, Any]=0.1   ,      snake_case	:							Tuple=5_1_2   ,      snake_case	:							List[str]=1_6   ,      snake_case	:							Union[str, Any]=2   ,      snake_case	:							Tuple=0.02   ,      snake_case	:							Tuple=3   ,      snake_case	:							Optional[int]=4   ,      snake_case	:							List[Any]=None   ,      )   ->						Tuple:

  """simple docstring"""





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

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

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

  UpperCamelCase_					:						Optional[int] =					None
  if self.use_input_mask:
   UpperCamelCase_					:						Tuple =					random_attention_mask([self.batch_size, self.seq_length]			)

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

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

  UpperCamelCase_					:						Optional[Any] =					self.get_config()

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						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=snake_case   ,      initializer_range=self.initializer_range   ,      )

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

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					BioGptModel(config=snake_case			)
  model.to(snake_case			)
  model.eval()
  UpperCamelCase_					:						str =					model(snake_case   ,      attention_mask=snake_case			)
  UpperCamelCase_					:						str =					model(snake_case			)
  self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							Tuple   ,      snake_case	:							Any   ,      snake_case	:							Tuple   ,      snake_case	:							List[str]   ,      snake_case	:							int   ,      snake_case	:							List[Any]   ,      )   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					BioGptForCausalLM(config=snake_case			)
  model.to(snake_case			)
  model.eval()
  UpperCamelCase_					:						int =					model(snake_case   ,      attention_mask=snake_case   ,      token_type_ids=snake_case   ,      labels=snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							int   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							List[str]   ,      snake_case	:							List[Any]   ,      *snake_case	:							str			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					BioGptModel(config=snake_case			)
  model.to(snake_case			)
  model.eval()

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

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

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

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

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

  # get two different outputs
  UpperCamelCase_					:						Optional[int] =					model(snake_case   ,      attention_mask=snake_case			)['last_hidden_state']
  UpperCamelCase_					:						Dict =					model(snake_case   ,      past_key_values=snake_case   ,      attention_mask=snake_case			)['last_hidden_state']

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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							str   ,      snake_case	:							int   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Any   ,      snake_case	:							Optional[int]   ,      *snake_case	:							str			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					BioGptModel(config=snake_case			).to(snake_case			).eval()

  UpperCamelCase_					:						List[str] =					torch.ones(input_ids.shape   ,      dtype=torch.long   ,      device=snake_case			)

  # first forward pass
  UpperCamelCase_					:						List[str] =					model(snake_case   ,      attention_mask=snake_case   ,      use_cache=snake_case			)

  UpperCamelCase_, UpperCamelCase_					:						int =					outputs.to_tuple()

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

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

  UpperCamelCase_					:						Optional[Any] =					model(snake_case   ,      attention_mask=snake_case			)['last_hidden_state']
  UpperCamelCase_					:						Optional[Any] =					model(snake_case   ,      attention_mask=snake_case   ,      past_key_values=snake_case			)[
      'last_hidden_state'
  ]

  # select random slice
  UpperCamelCase_					:						Dict =					ids_tensor((1,)   ,      output_from_past.shape[-1]			).item()
  UpperCamelCase_					:						Optional[Any] =					output_from_no_past[:, -3:, random_slice_idx].detach()
  UpperCamelCase_					:						Tuple =					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(snake_case   ,      snake_case   ,      atol=1e-3			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Tuple   ,      *snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]=False			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					BioGptForCausalLM(snake_case			)
  model.to(snake_case			)
  if gradient_checkpointing:
   model.gradient_checkpointing_enable()

  UpperCamelCase_					:						Union[str, Any] =					model(snake_case   ,      labels=snake_case			)
  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 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Union[str, Any]   ,      *snake_case	:							int			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Any =					BioGptModel(snake_case			)
  UpperCamelCase_					:						Tuple =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Dict   ,      snake_case	:							Optional[Any]   ,      snake_case	:							str   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[int]   ,      *snake_case	:							Any			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.num_labels
  UpperCamelCase_					:						Dict =					BioGptForTokenClassification(snake_case			)
  model.to(snake_case			)
  model.eval()
  UpperCamelCase_					:						str =					model(snake_case   ,      attention_mask=snake_case   ,      token_type_ids=snake_case			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)			)


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

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.prepare_config_and_inputs()
  (
      (
      UpperCamelCase_
  ), (
      UpperCamelCase_
  ), (
      UpperCamelCase_
  ), (
      UpperCamelCase_
  ), (
      UpperCamelCase_
  ), (
      UpperCamelCase_
  ), (
      UpperCamelCase_
  ), 
  )					:						Optional[int] =					config_and_inputs
  UpperCamelCase_					:						str =					{'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict





@require_torch
class 		_lowercase						(					snake_case_						,      snake_case_						,      snake_case_						,      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 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					BioGptModelTester(self			)
  UpperCamelCase_					:						str =					ConfigTester(self   ,      config_class=snake_case   ,      hidden_size=3_7			)

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

  """simple docstring"""





  self.config_tester.run_common_tests()

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*snake_case			)

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   UpperCamelCase_					:						List[Any] =					type
   self.model_tester.create_and_check_model(*snake_case			)

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

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( 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(*snake_case   ,      gradient_checkpointing=snake_case			)

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

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case			)

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

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_biogpt_for_token_classification(*snake_case			)

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Any =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'			)
  model.to(snake_case			)
  UpperCamelCase_					:						Any =					BioGptTokenizer.from_pretrained('microsoft/biogpt'			)

  UpperCamelCase_					:						str =					'left'

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

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

  UpperCamelCase_					:						int =					tokenizer(snake_case   ,      return_tensors='pt'   ,      padding=snake_case			)
  UpperCamelCase_					:						List[Any] =					inputs['input_ids'].to(snake_case			)

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

  UpperCamelCase_					:						Optional[Any] =					tokenizer(sentences[0]   ,      return_tensors='pt'			).input_ids.to(snake_case			)
  UpperCamelCase_					:						List[str] =					model.generate(input_ids=snake_case			)

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

  UpperCamelCase_					:						str =					tokenizer.batch_decode(snake_case   ,      skip_special_tokens=snake_case			)
  UpperCamelCase_					:						List[Any] =					tokenizer.decode(output_non_padded[0]   ,      skip_special_tokens=snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					tokenizer.decode(output_padded[0]   ,      skip_special_tokens=snake_case			)

  UpperCamelCase_					:						Dict =					[
      '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(snake_case   ,      snake_case			)
  self.assertListEqual(snake_case   ,      [non_padded_sentence, padded_sentence]			)

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						str:

  """simple docstring"""





  for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCamelCase_					:						List[Any] =					BioGptModel.from_pretrained(snake_case			)
   self.assertIsNotNone(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Tuple:

  """simple docstring"""





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


 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Dict:

  """simple docstring"""





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





@require_torch
class 		_lowercase						(					unittest.TestCase       ):

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'			)
  UpperCamelCase_					:						Union[str, Any] =					torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]			)
  UpperCamelCase_					:						Optional[int] =					model(snake_case			)[0]

  UpperCamelCase_					:						Union[str, Any] =					4_2_3_8_4

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

  UpperCamelCase_					:						Optional[Any] =					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]   ,      snake_case   ,      atol=1e-4			)			)


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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					BioGptTokenizer.from_pretrained('microsoft/biogpt'			)
  UpperCamelCase_					:						int =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'			)
  model.to(snake_case			)

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

  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(snake_case   ,      snake_case			)




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



