

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  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	List[Any]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	List[str]=13			,     __lowerCamelCase		:	List[str]=7			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	List[str]=True			,     __lowerCamelCase		:	List[Any]=False			,     __lowerCamelCase		:	Tuple=True			,     __lowerCamelCase		:	Union[str, Any]=99			,     __lowerCamelCase		:	List[str]=32			,     __lowerCamelCase		:	Tuple=5			,     __lowerCamelCase		:	Tuple=4			,     __lowerCamelCase		:	Dict=37			,     __lowerCamelCase		:	Dict="gelu"			,     __lowerCamelCase		:	Optional[Any]=0.1			,     __lowerCamelCase		:	List[Any]=0.1			,     __lowerCamelCase		:	Optional[int]=512			,     __lowerCamelCase		:	int=16			,     __lowerCamelCase		:	int=2			,     __lowerCamelCase		:	Union[str, Any]=0.02			,     __lowerCamelCase		:	str=3			,     __lowerCamelCase		:	int=4			,     __lowerCamelCase		:	List[Any]=None			,     )	->							str:
       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    lowercase_	(							self		:	Dict      )	->							Tuple:
       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    lowercase_	(							self		:	List[str]      )	->							int:
       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    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	List[Any]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					BioGptModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Dict			,     )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					BioGptForCausalLM(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[Any]			,     *__lowerCamelCase		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					BioGptModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()

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

       # first forward pass
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase      ).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,)			,     __lowerCamelCase      ).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=__lowerCamelCase      )]			,     dim=1			,     )

       # get two different outputs
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase      )['''last_hidden_state''']
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     past_key_values=__lowerCamelCase			,     attention_mask=__lowerCamelCase      )['''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(__lowerCamelCase			,     __lowerCamelCase			,     atol=1e-3      )      )






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[str]			,     *__lowerCamelCase		:	int      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					BioGptModel(config=__lowerCamelCase      ).to(__lowerCamelCase      ).eval()

       SCREAMING_SNAKE_CASE__					=					torch.ones(input_ids.shape			,     dtype=torch.long			,     device=__lowerCamelCase      )

       # first forward pass
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     use_cache=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__,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(__lowerCamelCase			,     attention_mask=__lowerCamelCase      )['''last_hidden_state''']
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     past_key_values=__lowerCamelCase      )[
           '''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(__lowerCamelCase			,     __lowerCamelCase			,     atol=1e-3      )      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	int			,     *__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Tuple=False      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					BioGptForCausalLM(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[Any]			,     *__lowerCamelCase		:	Optional[int]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					BioGptModel(__lowerCamelCase      )
       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    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     *__lowerCamelCase		:	Any      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					BioGptForTokenClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[int]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),
       )					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( A__		,  A__		,  A__		,  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    lowercase_	(							self		:	int      )	->							str:
       SCREAMING_SNAKE_CASE__					=					BioGptModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     hidden_size=37      )






  def    lowercase_	(							self		:	Optional[Any]      )	->							Any:
       self.config_tester.run_common_tests()






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	List[Any]      )	->							int:
       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(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[Any]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*__lowerCamelCase			,     gradient_checkpointing=__lowerCamelCase      )






  def    lowercase_	(							self		:	Tuple      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__lowerCamelCase      )






  def    lowercase_	(							self		:	List[str]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*__lowerCamelCase      )






  @slow
  def    lowercase_	(							self		:	str      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''      )
       model.to(__lowerCamelCase      )
       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(__lowerCamelCase			,     return_tensors='''pt'''			,     padding=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					inputs['''input_ids'''].to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					model.generate(
           input_ids=__lowerCamelCase			,     attention_mask=inputs['''attention_mask'''].to(__lowerCamelCase      )			,     )

       SCREAMING_SNAKE_CASE__					=					tokenizer(sentences[0]			,     return_tensors='''pt'''      ).input_ids.to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model.generate(input_ids=__lowerCamelCase      )

       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(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model.generate(input_ids=__lowerCamelCase			,     max_length=model.config.max_length - num_paddings      )

       SCREAMING_SNAKE_CASE__					=					tokenizer.batch_decode(__lowerCamelCase			,     skip_special_tokens=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					tokenizer.decode(output_non_padded[0]			,     skip_special_tokens=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					tokenizer.decode(output_padded[0]			,     skip_special_tokens=__lowerCamelCase      )

       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(__lowerCamelCase			,     __lowerCamelCase      )
       self.assertListEqual(__lowerCamelCase			,     [non_padded_sentence, padded_sentence]      )






  @slow
  def    lowercase_	(							self		:	str      )	->							int:
       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					=					BioGptModel.from_pretrained(__lowerCamelCase      )
            self.assertIsNotNone(__lowerCamelCase      )






  def    lowercase_	(							self		:	str      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__,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(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size      )
       SCREAMING_SNAKE_CASE__					=					BioGptForSequenceClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     labels=__lowerCamelCase      )
       self.assertEqual(result.logits.shape			,     (self.model_tester.batch_size, self.model_tester.num_labels)      )







  def    lowercase_	(							self		:	List[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__,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(__lowerCamelCase      )
       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(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					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  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @slow
  def    lowercase_	(							self		:	Union[str, Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''      )
       SCREAMING_SNAKE_CASE__					=					torch.tensor([[2, 4805, 9, 656, 21]]      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )[0]

       SCREAMING_SNAKE_CASE__					=					4_2384

       SCREAMING_SNAKE_CASE__					=					torch.Size((1, 5, vocab_size)      )
       self.assertEqual(output.shape			,     __lowerCamelCase      )

       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]			,     __lowerCamelCase			,     atol=1e-4      )      )







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

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

       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(__lowerCamelCase			,     __lowerCamelCase      )





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=A__ )
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      field(default="question-answering-extractive"		,  metadata={"include_in_asdict_even_if_is_default": True} )
  a     =      Features({"question": Value("string" ), "context": Value("string" )} )
  a     =      Features(
      {
          "answers": Sequence(
              {
                  "text": Value("string" ),
                  "answer_start": Value("int32" ),
              } )
      } )
  a     =      "question"
  a     =      "context"
  a     =      "answers"






  @property
  def    lowercase_	(							self		:	Dict      )	->							Dict[str, str]:
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}


