


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 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1_3  ,							SCREAMING_SNAKE_CASE__       : str=7  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : int=9_9  ,							SCREAMING_SNAKE_CASE__       : int=3_2  ,							SCREAMING_SNAKE_CASE__       : List[str]=5  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=4  ,							SCREAMING_SNAKE_CASE__       : int=3_7  ,							SCREAMING_SNAKE_CASE__       : Dict="gelu"  ,							SCREAMING_SNAKE_CASE__       : str=0.1  ,							SCREAMING_SNAKE_CASE__       : List[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : str=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Dict=1_6  ,							SCREAMING_SNAKE_CASE__       : Tuple=2  ,							SCREAMING_SNAKE_CASE__       : str=0.02  ,							SCREAMING_SNAKE_CASE__       : List[Any]=3  ,							SCREAMING_SNAKE_CASE__       : int=4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=None  ,							)	->    Any:
             a_      :	List[str]							=						parent
             a_      :	int							=						batch_size
             a_      :	Union[str, Any]							=						seq_length
             a_      :	Tuple							=						is_training
             a_      :	Any							=						use_input_mask
             a_      :	Dict							=						use_token_type_ids
             a_      :	List[str]							=						use_labels
             a_      :	str							=						vocab_size
             a_      :	List[str]							=						hidden_size
             a_      :	Dict							=						num_hidden_layers
             a_      :	str							=						num_attention_heads
             a_      :	List[str]							=						intermediate_size
             a_      :	List[str]							=						hidden_act
             a_      :	str							=						hidden_dropout_prob
             a_      :	Dict							=						attention_probs_dropout_prob
             a_      :	Optional[int]							=						max_position_embeddings
             a_      :	Optional[int]							=						type_vocab_size
             a_      :	Union[str, Any]							=						type_sequence_label_size
             a_      :	Dict							=						initializer_range
             a_      :	List[str]							=						num_labels
             a_      :	Optional[int]							=						num_choices
             a_      :	int							=						scope
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             a_      :	List[Any]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

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

             a_      :	List[Any]							=						None
             if self.use_token_type_ids:
                    a_      :	str							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.type_vocab_size  )

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

             a_      :	Dict							=						self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Any:
             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=SCREAMING_SNAKE_CASE__  ,							initializer_range=self.initializer_range  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    str:
             a_      :	Optional[int]							=						BioGptModel(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	str							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						model(SCREAMING_SNAKE_CASE__  )
             self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							)	->    List[str]:
             a_      :	Tuple							=						BioGptForCausalLM(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							token_type_ids=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.vocab_size)  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							*SCREAMING_SNAKE_CASE__       : Dict  )	->    int:
             a_      :	str							=						BioGptModel(config=SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()

             # create attention mask
             a_      :	Any							=						torch.ones(input_ids.shape  ,							dtype=torch.long  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						self.seq_length // 2
             a_      :	int							=						0

             # first forward pass
             a_							,	a_      :	List[str]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ).to_tuple()

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

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

             # append to next input_ids and attn_mask
             a_      :	List[str]							=						torch.cat([input_ids, next_tokens]  ,							dim=-1  )
             a_      :	Any							=						torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1)  ,							dtype=torch.long  ,							device=SCREAMING_SNAKE_CASE__  )]  ,							dim=1  ,							)

             # get two different outputs
             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )['last_hidden_state']
             a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )['last_hidden_state']

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

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : int  ,							*SCREAMING_SNAKE_CASE__       : Any  )	->    Dict:
             a_      :	Optional[Any]							=						BioGptModel(config=SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  ).eval()

             a_      :	List[Any]							=						torch.ones(input_ids.shape  ,							dtype=torch.long  ,							device=SCREAMING_SNAKE_CASE__  )

             # first forward pass
             a_      :	Tuple							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						outputs.to_tuple()

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

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

             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )['last_hidden_state']
             a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  )[
                 'last_hidden_state'
             ]

             # select random slice
             a_      :	str							=						ids_tensor((1,)  ,							output_from_past.shape[-1]  ).item()
             a_      :	Tuple							=						output_from_no_past[:, -3:, random_slice_idx].detach()
             a_      :	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(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Any  ,							*SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str=False  )	->    int:
             a_      :	List[str]							=						BioGptForCausalLM(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             if gradient_checkpointing:
                    model.gradient_checkpointing_enable()

             a_      :	Any							=						model(SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_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       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							*SCREAMING_SNAKE_CASE__       : Any  )	->    Dict:
             a_      :	Any							=						BioGptModel(SCREAMING_SNAKE_CASE__  )
             a_      :	List[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  )  ,							0.001  )
                           self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]  ) - 0.0  )  ,							0.01  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : int  ,							*SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    Optional[Any]:
             a_      :	Optional[int]							=						self.num_labels
             a_      :	Union[str, Any]							=						BioGptForTokenClassification(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							token_type_ids=SCREAMING_SNAKE_CASE__  )
             self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.num_labels)  )

      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             a_      :	Dict							=						self.prepare_config_and_inputs()
             (
                 (
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	
             )      :	int							=						config_and_inputs
             a_      :	List[str]							=						{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Union[str, Any]       =		(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      snake_case__       :  int       =		(BioGptForCausalLM,) if is_torch_available() else ()
      snake_case__       :  Optional[Any]       =		(
          {
              '''feature-extraction''': BioGptModel,
              '''text-classification''': BioGptForSequenceClassification,
              '''text-generation''': BioGptForCausalLM,
              '''token-classification''': BioGptForTokenClassification,
              '''zero-shot''': BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__       :  Optional[int]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    str:
             a_      :	Dict							=						BioGptModelTester(self  )
             a_      :	Dict							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  ,							hidden_size=3_7  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[int]:
             self.config_tester.run_common_tests()
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Tuple:
             a_      :	int							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	Dict							=						self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    a_      :	Any							=						type
                    self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             a_      :	int							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE__  ,							gradient_checkpointing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[Any]:
             a_      :	Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             a_      :	Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[int]:
             a_      :	Optional[int]							=						BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
             model.to(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						BioGptTokenizer.from_pretrained('microsoft/biogpt'  )

             a_      :	List[Any]							=						'left'

             # Define PAD Token = EOS Token = 50256
             a_      :	Union[str, Any]							=						tokenizer.eos_token
             a_      :	Optional[Any]							=						model.config.eos_token_id

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

             a_      :	Optional[int]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ,							padding=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						inputs['input_ids'].to(SCREAMING_SNAKE_CASE__  )

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

             a_      :	Dict							=						tokenizer(sentences[0]  ,							return_tensors='pt'  ).input_ids.to(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						model.generate(input_ids=SCREAMING_SNAKE_CASE__  )

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

             a_      :	Optional[Any]							=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						tokenizer.decode(output_non_padded[0]  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						tokenizer.decode(output_padded[0]  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )

             a_      :	Any							=						[
                 '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(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							[non_padded_sentence, padded_sentence]  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    str:
             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_      :	Any							=						BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Any:
             a_							,	a_      :	Union[str, Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
             a_      :	Union[str, Any]							=						3
             a_      :	Optional[int]							=						input_dict['input_ids']
             a_      :	Tuple							=						input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						ids_tensor([self.model_tester.batch_size]  ,							self.model_tester.type_sequence_label_size  )
             a_      :	List[str]							=						BioGptForSequenceClassification(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	Tuple							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )

      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             a_							,	a_      :	Union[str, Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
             a_      :	List[Any]							=						3
             a_      :	List[str]							=						'multi_label_classification'
             a_      :	int							=						input_dict['input_ids']
             a_      :	Dict							=						input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]  ,							self.model_tester.type_sequence_label_size  ).to(torch.float  )
             a_      :	Any							=						BioGptForSequenceClassification(SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()
             a_      :	int							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(result.logits.shape  ,							(self.model_tester.batch_size, self.model_tester.num_labels)  )
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             a_      :	int							=						BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
             a_      :	Optional[Any]							=						torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]  )
             a_      :	Optional[Any]							=						model(SCREAMING_SNAKE_CASE__  )[0]

             a_      :	Optional[Any]							=						4_2_3_8_4

             a_      :	int							=						torch.Size((1, 5, vocab_size)  )
             self.assertEqual(output.shape  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	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]  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-4  )  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             a_      :	str							=						BioGptTokenizer.from_pretrained('microsoft/biogpt'  )
             a_      :	Union[str, Any]							=						BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
             model.to(SCREAMING_SNAKE_CASE__  )

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

             a_      :	List[Any]							=						(
                 '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(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )






import random
class 				SCREAMING_SNAKE_CASE__						:
      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : str  )	->    tuple[list[int], list[int]]:
             a_      :	int							=						[ord(SCREAMING_SNAKE_CASE__  ) for i in text]
             a_      :	Any							=						[]
             a_      :	Optional[int]							=						[]
             for i in plain:
                    a_      :	Tuple							=						random.randint(1  ,							3_0_0  )
                    a_      :	Optional[int]							=						(i + k) * k
                    cipher.append(SCREAMING_SNAKE_CASE__  )
                    key.append(SCREAMING_SNAKE_CASE__  )
             return cipher, key

      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : list[int]  ,							SCREAMING_SNAKE_CASE__       : list[int]  )	->    str:
             a_      :	List[Any]							=						[]
             for i in range(len(SCREAMING_SNAKE_CASE__  )  ):
                    a_      :	str							=						int((cipher[i] - (key[i]) ** 2) / key[i]  )
                    plain.append(chr(SCREAMING_SNAKE_CASE__  )  )
             return "".join(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   UpperCAmelCase_      ,     UpperCAmelCase_   :				Any						      = Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))


