

'''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 						lowerCAmelCase__   :



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      seq_length
      __SCREAMING_SNAKE_CASE     =      is_training
      __SCREAMING_SNAKE_CASE     =      use_input_mask
      __SCREAMING_SNAKE_CASE     =      use_token_type_ids
      __SCREAMING_SNAKE_CASE     =      use_labels
      __SCREAMING_SNAKE_CASE     =      vocab_size
      __SCREAMING_SNAKE_CASE     =      hidden_size
      __SCREAMING_SNAKE_CASE     =      num_hidden_layers
      __SCREAMING_SNAKE_CASE     =      num_attention_heads
      __SCREAMING_SNAKE_CASE     =      intermediate_size
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      max_position_embeddings
      __SCREAMING_SNAKE_CASE     =      type_vocab_size
      __SCREAMING_SNAKE_CASE     =      type_sequence_label_size
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      num_labels
      __SCREAMING_SNAKE_CASE     =      num_choices
      __SCREAMING_SNAKE_CASE     =      scope
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size			)

      __SCREAMING_SNAKE_CASE     =      None
      if self.use_input_mask:
           __SCREAMING_SNAKE_CASE     =      random_attention_mask([self.batch_size, self.seq_length]			)

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

      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      None
      if self.use_labels:
           __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size]					,							self.type_sequence_label_size			)
           __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size, self.seq_length]					,							self.num_labels			)
           __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size]					,							self.num_choices			)

      __SCREAMING_SNAKE_CASE     =      self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def       UpperCAmelCase__		(		self					:       str			)  -> List[str]:


      """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=_UpperCAmelCase					,							initializer_range=self.initializer_range					,							)
 def       UpperCAmelCase__		(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]			)  -> List[Any]:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptForCausalLM(config=_UpperCAmelCase			)
      model.to(_UpperCAmelCase			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase					,							token_type_ids=_UpperCAmelCase					,							labels=_UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.seq_length, self.vocab_size)			)
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Any					,							*__SCREAMING_SNAKE_CASE					:       Dict			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptModel(config=_UpperCAmelCase			)
      model.to(_UpperCAmelCase			)
      model.eval()

      # create attention mask
      __SCREAMING_SNAKE_CASE     =      torch.ones(input_ids.shape					,							dtype=torch.long					,							device=_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      self.seq_length // 2
      __SCREAMING_SNAKE_CASE     =      0

      # first forward pass
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase			).to_tuple()

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

      # change a random masked slice from input_ids
      __SCREAMING_SNAKE_CASE     =      ids_tensor((1,)					,							_UpperCAmelCase			).item() + 1
      __SCREAMING_SNAKE_CASE     =      ids_tensor((self.batch_size, 1)					,							config.vocab_size			).squeeze(-1			)
      __SCREAMING_SNAKE_CASE     =      random_other_next_tokens

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

      # get two different outputs
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase			)['last_hidden_state']
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							past_key_values=_UpperCAmelCase					,							attention_mask=_UpperCAmelCase			)['last_hidden_state']

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

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(_UpperCAmelCase					,							_UpperCAmelCase					,							atol=1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							*__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptModel(config=_UpperCAmelCase			).to(_UpperCAmelCase			).eval()

      __SCREAMING_SNAKE_CASE     =      torch.ones(input_ids.shape					,							dtype=torch.long					,							device=_UpperCAmelCase			)

      # first forward pass
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase					,							use_cache=_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      outputs.to_tuple()

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

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

      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase			)['last_hidden_state']
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase					,							past_key_values=_UpperCAmelCase			)[
          'last_hidden_state'
      ]

      # select random slice
      __SCREAMING_SNAKE_CASE     =      ids_tensor((1,)					,							output_from_past.shape[-1]			).item()
      __SCREAMING_SNAKE_CASE     =      output_from_no_past[:, -3:, random_slice_idx].detach()
      __SCREAMING_SNAKE_CASE     =      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(_UpperCAmelCase					,							_UpperCAmelCase					,							atol=1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							*__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       int=False			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptForCausalLM(_UpperCAmelCase			)
      model.to(_UpperCAmelCase			)
      if gradient_checkpointing:
           model.gradient_checkpointing_enable()

      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							labels=_UpperCAmelCase			)
      self.parent.assertEqual(result.loss.shape					,							()			)
      self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.seq_length, self.vocab_size)			)
      result.loss.backward()
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							*__SCREAMING_SNAKE_CASE					:       Any			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptModel(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							*__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.num_labels
      __SCREAMING_SNAKE_CASE     =      BioGptForTokenClassification(_UpperCAmelCase			)
      model.to(_UpperCAmelCase			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase					,							token_type_ids=_UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.seq_length, self.num_labels)			)


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.prepare_config_and_inputs()
      (
          __SCREAMING_SNAKE_CASE
      )     =      config_and_inputs
      __SCREAMING_SNAKE_CASE     =      {'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict







@require_torch
class 						lowerCAmelCase__   (      a__ ,					a__ ,					a__ ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      (
     (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
     if is_torch_available()
     else ()
 )
 lowerCAmelCase__  					=      (BioGptForCausalLM,) if is_torch_available() else ()
 lowerCAmelCase__  					=      (
     {
         "feature-extraction": BioGptModel,
         "text-classification": BioGptForSequenceClassification,
         "text-generation": BioGptForCausalLM,
         "token-classification": BioGptForTokenClassification,
         "zero-shot": BioGptForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowerCAmelCase__  					=      False
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptModelTester(self			)
      __SCREAMING_SNAKE_CASE     =      ConfigTester(self					,							config_class=_UpperCAmelCase					,							hidden_size=37			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           __SCREAMING_SNAKE_CASE     =      type
           self.model_tester.create_and_check_model(*_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       int			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_forward_and_backwards(*_UpperCAmelCase					,							gradient_checkpointing=_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_weight_initialization(*_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_for_token_classification(*_UpperCAmelCase			)
 @slow
 def       UpperCAmelCase__		(		self					:       int			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""			)
      model.to(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      BioGptTokenizer.from_pretrained("""microsoft/biogpt"""			)

      __SCREAMING_SNAKE_CASE     =      'left'

      # Define PAD Token = EOS Token = 50256
      __SCREAMING_SNAKE_CASE     =      tokenizer.eos_token
      __SCREAMING_SNAKE_CASE     =      model.config.eos_token_id

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

      __SCREAMING_SNAKE_CASE     =      tokenizer(_UpperCAmelCase					,							return_tensors="""pt"""					,							padding=_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      inputs['input_ids'].to(_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      model.generate(
          input_ids=_UpperCAmelCase					,							attention_mask=inputs["""attention_mask"""].to(_UpperCAmelCase			)					,							)

      __SCREAMING_SNAKE_CASE     =      tokenizer(sentences[0]					,							return_tensors="""pt"""			).input_ids.to(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      model.generate(input_ids=_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
      __SCREAMING_SNAKE_CASE     =      tokenizer(sentences[1]					,							return_tensors="""pt"""			).input_ids.to(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      model.generate(input_ids=_UpperCAmelCase					,							max_length=model.config.max_length - num_paddings			)

      __SCREAMING_SNAKE_CASE     =      tokenizer.batch_decode(_UpperCAmelCase					,							skip_special_tokens=_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      tokenizer.decode(output_non_padded[0]					,							skip_special_tokens=_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      tokenizer.decode(output_padded[0]					,							skip_special_tokens=_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      [
          '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(_UpperCAmelCase					,							_UpperCAmelCase			)
      self.assertListEqual(_UpperCAmelCase					,							[non_padded_sentence, padded_sentence]			)
 @slow
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[Any]:


      """simple docstring"""




      for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE     =      BioGptModel.from_pretrained(_UpperCAmelCase			)
           self.assertIsNotNone(_UpperCAmelCase			)
 def       UpperCAmelCase__		(		self					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE     =      3
      __SCREAMING_SNAKE_CASE     =      input_dict['input_ids']
      __SCREAMING_SNAKE_CASE     =      input_ids.ne(1			).to(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      ids_tensor([self.model_tester.batch_size]					,							self.model_tester.type_sequence_label_size			)
      __SCREAMING_SNAKE_CASE     =      BioGptForSequenceClassification(_UpperCAmelCase			)
      model.to(_UpperCAmelCase			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase					,							labels=_UpperCAmelCase			)
      self.assertEqual(result.logits.shape					,							(self.model_tester.batch_size, self.model_tester.num_labels)			)


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE     =      3
      __SCREAMING_SNAKE_CASE     =      'multi_label_classification'
      __SCREAMING_SNAKE_CASE     =      input_dict['input_ids']
      __SCREAMING_SNAKE_CASE     =      input_ids.ne(1			).to(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,							self.model_tester.type_sequence_label_size			).to(torch.float			)
      __SCREAMING_SNAKE_CASE     =      BioGptForSequenceClassification(_UpperCAmelCase			)
      model.to(_UpperCAmelCase			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase					,							attention_mask=_UpperCAmelCase					,							labels=_UpperCAmelCase			)
      self.assertEqual(result.logits.shape					,							(self.model_tester.batch_size, self.model_tester.num_labels)			)







@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""			)
      __SCREAMING_SNAKE_CASE     =      torch.tensor([[2, 4_805, 9, 656, 21]]			)
      __SCREAMING_SNAKE_CASE     =      model(_UpperCAmelCase			)[0]

      __SCREAMING_SNAKE_CASE     =      42_384

      __SCREAMING_SNAKE_CASE     =      torch.Size((1, 5, vocab_size)			)
      self.assertEqual(output.shape					,							_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      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]					,							_UpperCAmelCase					,							atol=1E-4			)			)


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BioGptTokenizer.from_pretrained("""microsoft/biogpt"""			)
      __SCREAMING_SNAKE_CASE     =      BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""			)
      model.to(_UpperCAmelCase			)

      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      tokenizer("""COVID-19 is"""					,							return_tensors="""pt"""			).to(_UpperCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      model.generate(
          **_UpperCAmelCase					,							min_length=100					,							max_length=1_024					,							num_beams=5					,							early_stopping=_UpperCAmelCase					,							)
      __SCREAMING_SNAKE_CASE     =      tokenizer.decode(output_ids[0]					,							skip_special_tokens=_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      (
          '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(_UpperCAmelCase					,							_UpperCAmelCase			)













'''simple docstring'''




from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def        _lowerCAmelCase			(  __snake_case  : float ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     list[float]:
     if radian_mode:
          return [magnitude * cos(__snake_case			), magnitude * sin(__snake_case			)]
     return [magnitude * cos(radians(__snake_case			)			), magnitude * sin(radians(__snake_case			)			)]






def        _lowerCAmelCase			(  __snake_case  : NDArray[floataa] ,							__snake_case  : NDArray[floataa] ,							__snake_case  : float = 10**-1			)     ->     bool:
     __A						: NDArray[floataa]      =       cross(__snake_case ,							__snake_case			)
     __A						: float      =       sum(__snake_case			)
     return abs(__snake_case			) < eps


if __name__ == "__main__":
  # Test to check if it works
  lowercase__    :      List[Any]						=    array(
      [
          polar_force(718.4, 1_80 - 30),
          polar_force(879.54, 45),
          polar_force(1_00, -90),
      ]
  )

  lowercase__    :      NDArray[floataa]						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  lowercase__    :      Dict						=    array(
      [
          polar_force(30 * 9.81, 15),
          polar_force(2_15, 1_80 - 45),
          polar_force(2_64, 90 - 30),
      ]
  )

  lowercase__    :      Any						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  lowercase__    :      Optional[int]						=    array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

  lowercase__    :      Optional[int]						=    array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()