






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_  :




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=13	,						_lowerCAmelCase=7	,						_lowerCAmelCase=True	,						_lowerCAmelCase=True	,						_lowerCAmelCase=False	,						_lowerCAmelCase=True	,						_lowerCAmelCase=99	,						_lowerCAmelCase=32	,						_lowerCAmelCase=5	,						_lowerCAmelCase=4	,						_lowerCAmelCase=37	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=16	,						_lowerCAmelCase=2	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=3	,						_lowerCAmelCase=4	,						_lowerCAmelCase=None	,						):
          UpperCAmelCase__      :  int				      =			parent
          UpperCAmelCase__      :  Tuple				      =			batch_size
          UpperCAmelCase__      :  Optional[Any]				      =			seq_length
          UpperCAmelCase__      :  str				      =			is_training
          UpperCAmelCase__      :  List[Any]				      =			use_input_mask
          UpperCAmelCase__      :  Tuple				      =			use_token_type_ids
          UpperCAmelCase__      :  Any				      =			use_labels
          UpperCAmelCase__      :  List[str]				      =			vocab_size
          UpperCAmelCase__      :  Optional[int]				      =			hidden_size
          UpperCAmelCase__      :  Optional[int]				      =			num_hidden_layers
          UpperCAmelCase__      :  Union[str, Any]				      =			num_attention_heads
          UpperCAmelCase__      :  Optional[int]				      =			intermediate_size
          UpperCAmelCase__      :  List[Any]				      =			hidden_act
          UpperCAmelCase__      :  int				      =			hidden_dropout_prob
          UpperCAmelCase__      :  Tuple				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  Tuple				      =			max_position_embeddings
          UpperCAmelCase__      :  List[str]				      =			type_vocab_size
          UpperCAmelCase__      :  str				      =			type_sequence_label_size
          UpperCAmelCase__      :  Union[str, Any]				      =			initializer_range
          UpperCAmelCase__      :  Optional[int]				      =			num_labels
          UpperCAmelCase__      :  Tuple				      =			num_choices
          UpperCAmelCase__      :  Optional[Any]				      =			scope




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size		)

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

          UpperCAmelCase__      :  Optional[int]				      =			None
          if self.use_token_type_ids:
               UpperCAmelCase__      :  List[Any]				      =			ids_tensor([self.batch_size, self.seq_length]	,						self.type_vocab_size		)

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

          UpperCAmelCase__      :  str				      =			self.get_config()

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




     def   __UpperCAmelCase  (				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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Any				      =			BioGptModel(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          UpperCAmelCase__      :  Dict				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			model(_lowerCAmelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, self.seq_length, self.hidden_size)		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						):
          UpperCAmelCase__      :  Dict				      =			BioGptForCausalLM(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          UpperCAmelCase__      :  Optional[Any]				      =			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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						*_lowerCAmelCase		):
          UpperCAmelCase__      :  Tuple				      =			BioGptModel(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()

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

          # first forward pass
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		).to_tuple()

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

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

          # get two different outputs
          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)["""last_hidden_state"""]
          UpperCAmelCase__      :  Optional[int]				      =			model(_lowerCAmelCase	,						past_key_values=_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)["""last_hidden_state"""]

          # select random slice
          UpperCAmelCase__      :  List[Any]				      =			ids_tensor((1,)	,						output_from_past.shape[-1]		).item()
          UpperCAmelCase__      :  List[str]				      =			output_from_no_past[:, -1, random_slice_idx].detach()
          UpperCAmelCase__      :  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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						*_lowerCAmelCase		):
          UpperCAmelCase__      :  Tuple				      =			BioGptModel(config=_lowerCAmelCase		).to(_lowerCAmelCase		).eval()

          UpperCAmelCase__      :  Optional[Any]				      =			torch.ones(input_ids.shape	,						dtype=torch.long	,						device=_lowerCAmelCase		)

          # first forward pass
          UpperCAmelCase__      :  Optional[int]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						use_cache=_lowerCAmelCase		)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[str]				      =			outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          UpperCAmelCase__      :  Union[str, Any]				      =			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__      :  Tuple				      =			torch.cat([input_ids, next_tokens]	,						dim=-1		)
          UpperCAmelCase__      :  Tuple				      =			torch.cat([attention_mask, next_attn_mask]	,						dim=-1		)

          UpperCAmelCase__      :  Union[str, Any]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)["""last_hidden_state"""]
          UpperCAmelCase__      :  str				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						past_key_values=_lowerCAmelCase		)[
              """last_hidden_state"""
          ]

          # select random slice
          UpperCAmelCase__      :  str				      =			ids_tensor((1,)	,						output_from_past.shape[-1]		).item()
          UpperCAmelCase__      :  Optional[int]				      =			output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCAmelCase__      :  Optional[Any]				      =			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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						*_lowerCAmelCase	,						_lowerCAmelCase=False		):
          UpperCAmelCase__      :  Any				      =			BioGptForCausalLM(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          if gradient_checkpointing:
               model.gradient_checkpointing_enable()

          UpperCAmelCase__      :  Tuple				      =			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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						*_lowerCAmelCase		):
          UpperCAmelCase__      :  Any				      =			BioGptModel(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			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   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						*_lowerCAmelCase		):
          UpperCAmelCase__      :  Dict				      =			self.num_labels
          UpperCAmelCase__      :  int				      =			BioGptForTokenClassification(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          UpperCAmelCase__      :  Any				      =			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   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )      ,		(
              UpperCAmelCase__
          )      ,		(
              UpperCAmelCase__
          )      ,		(
              UpperCAmelCase__
          )      ,		(
              UpperCAmelCase__
          )      ,		(
              UpperCAmelCase__
          )      ,		(
              UpperCAmelCase__
          )      ,		
          )      :  Optional[int]				      =			config_and_inputs
          UpperCAmelCase__      :  List[str]				      =			{"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict







@require_torch
class 				UpperCAmelCase_  (		__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase							,      unittest.TestCase		):
     __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		):
          UpperCAmelCase__      :  Optional[Any]				      =			BioGptModelTester(self		)
          UpperCAmelCase__      :  List[str]				      =			ConfigTester(self	,						config_class=_lowerCAmelCase	,						hidden_size=37		)




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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               UpperCAmelCase__      :  Optional[int]				      =			type
               self.model_tester.create_and_check_model(*_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  str				      =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_forward_and_backwards(*_lowerCAmelCase	,						gradient_checkpointing=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_weight_initialization(*_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_for_token_classification(*_lowerCAmelCase		)




     @slow
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""		)
          model.to(_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)

          UpperCAmelCase__      :  Tuple				      =			"""left"""

          # Define PAD Token = EOS Token = 50256
          UpperCAmelCase__      :  Dict				      =			tokenizer.eos_token
          UpperCAmelCase__      :  str				      =			model.config.eos_token_id

          # use different length sentences to test batching
          UpperCAmelCase__      :  Tuple				      =			[
              """Hello, my dog is a little""",
              """Today, I""",
          ]

          UpperCAmelCase__      :  List[Any]				      =			tokenizer(_lowerCAmelCase	,						return_tensors="""pt"""	,						padding=_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[Any]				      =			inputs["""input_ids"""].to(_lowerCAmelCase		)

          UpperCAmelCase__      :  Union[str, Any]				      =			model.generate(
              input_ids=_lowerCAmelCase	,						attention_mask=inputs["""attention_mask"""].to(_lowerCAmelCase		)	,						)

          UpperCAmelCase__      :  Tuple				      =			tokenizer(sentences[0]	,						return_tensors="""pt"""		).input_ids.to(_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			model.generate(input_ids=_lowerCAmelCase		)

          UpperCAmelCase__      :  List[Any]				      =			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(_lowerCAmelCase		)
          UpperCAmelCase__      :  Union[str, Any]				      =			model.generate(input_ids=_lowerCAmelCase	,						max_length=model.config.max_length - num_paddings		)

          UpperCAmelCase__      :  Union[str, Any]				      =			tokenizer.batch_decode(_lowerCAmelCase	,						skip_special_tokens=_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			tokenizer.decode(output_non_padded[0]	,						skip_special_tokens=_lowerCAmelCase		)
          UpperCAmelCase__      :  int				      =			tokenizer.decode(output_padded[0]	,						skip_special_tokens=_lowerCAmelCase		)

          UpperCAmelCase__      :  List[str]				      =			[
              """Hello, my dog is a little bit bigger than a little bit.""",
              """Today, I have a good idea of how to use the information""",
          ]
          self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase	,						[non_padded_sentence, padded_sentence]		)




     @slow
     def   __UpperCAmelCase  (				self		):
          for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__      :  Dict				      =			BioGptModel.from_pretrained(_lowerCAmelCase		)
               self.assertIsNotNone(_lowerCAmelCase		)




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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      ,		UpperCAmelCase__      :  str				      =			self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__      :  List[str]				      =			3
          UpperCAmelCase__      :  Tuple				      =			"""multi_label_classification"""
          UpperCAmelCase__      :  Optional[int]				      =			input_dict["""input_ids"""]
          UpperCAmelCase__      :  int				      =			input_ids.ne(1		).to(_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			ids_tensor(
              [self.model_tester.batch_size, config.num_labels]	,						self.model_tester.type_sequence_label_size		).to(torch.float		)
          UpperCAmelCase__      :  str				      =			BioGptForSequenceClassification(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          UpperCAmelCase__      :  str				      =			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		):




     @slow
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""		)
          UpperCAmelCase__      :  List[Any]				      =			torch.tensor([[2, 4805, 9, 656, 21]]		)
          UpperCAmelCase__      :  Optional[int]				      =			model(_lowerCAmelCase		)[0]

          UpperCAmelCase__      :  str				      =			42384

          UpperCAmelCase__      :  str				      =			torch.Size((1, 5, vocab_size)		)
          self.assertEqual(output.shape	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  Optional[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   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)
          UpperCAmelCase__      :  Optional[int]				      =			BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""		)
          model.to(_lowerCAmelCase		)

          torch.manual_seed(0		)
          UpperCAmelCase__      :  Union[str, Any]				      =			tokenizer("""COVID-19 is"""	,						return_tensors="""pt"""		).to(_lowerCAmelCase		)
          UpperCAmelCase__      :  Tuple				      =			model.generate(
              **_lowerCAmelCase	,						min_length=100	,						max_length=1024	,						num_beams=5	,						early_stopping=_lowerCAmelCase	,						)
          UpperCAmelCase__      :  Optional[Any]				      =			tokenizer.decode(output_ids[0]	,						skip_special_tokens=_lowerCAmelCase		)

          UpperCAmelCase__      :  int				      =			(
              """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 ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Any            =    {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				int          =    'openai-gpt'
    SCREAMING_SNAKE_CASE:				List[str]          =    {
        'max_position_embeddings': 'n_positions',
        'hidden_size': 'n_embd',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=40_478       ,						lowerCamelCase__=512       ,						lowerCamelCase__=768       ,						lowerCamelCase__=12       ,						lowerCamelCase__=12       ,						lowerCamelCase__="gelu"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__="cls_index"       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	vocab_size
       lowerCAmelCase_:							List[Any]       =	n_positions
       lowerCAmelCase_:							Tuple       =	n_embd
       lowerCAmelCase_:							Optional[int]       =	n_layer
       lowerCAmelCase_:							Optional[int]       =	n_head
       lowerCAmelCase_:							int       =	afn
       lowerCAmelCase_:							str       =	resid_pdrop
       lowerCAmelCase_:							Optional[int]       =	embd_pdrop
       lowerCAmelCase_:							Optional[int]       =	attn_pdrop
       lowerCAmelCase_:							Dict       =	layer_norm_epsilon
       lowerCAmelCase_:							List[Any]       =	initializer_range
       lowerCAmelCase_:							Union[str, Any]       =	summary_type
       lowerCAmelCase_:							Any       =	summary_use_proj
       lowerCAmelCase_:							Dict       =	summary_activation
       lowerCAmelCase_:							Dict       =	summary_first_dropout
       lowerCAmelCase_:							List[Any]       =	summary_proj_to_labels
       super().__init__(**lowerCamelCase__ )