
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							:							str     ,						_A							:							List[Any]     ,						_A							:							Union[str, Any]=13     ,						_A							:							List[Any]=7     ,						_A							:							Any=True     ,						_A							:							Dict=True     ,						_A							:							Union[str, Any]=False     ,						_A							:							Optional[Any]=True     ,						_A							:							str=99     ,						_A							:							Any=32     ,						_A							:							Optional[int]=5     ,						_A							:							Dict=4     ,						_A							:							Dict=37     ,						_A							:							Any="gelu"     ,						_A							:							Union[str, Any]=0.1     ,						_A							:							Tuple=0.1     ,						_A							:							List[Any]=512     ,						_A							:							Dict=16     ,						_A							:							Any=2     ,						_A							:							List[Any]=0.02     ,						_A							:							Tuple=3     ,						_A							:							Dict=4     ,						_A							:							Tuple=None     ,						):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




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

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

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

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

       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.get_config()

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



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       return BioGptConfig(
           vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout_prob=self.hidden_dropout_prob     ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						is_decoder=_A     ,						initializer_range=self.initializer_range     ,						)



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							Optional[Any]     ,						_A							:							Optional[int]     ,						_A							:							Tuple     ,						_A							:							Tuple     ,						_A							:							Optional[Any]     ,						_A							:							List[Any]     ,						_A							:							int ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							List[str]     ,						_A							:							Optional[Any]     ,						_A							:							Any     ,						_A							:							List[str]     ,						_A							:							List[str]     ,						_A							:							Optional[Any]     ,						_A							:							Optional[Any]     ,						_A							:							int     ,						_A							:							Tuple     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		BioGptForCausalLM(config=_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	List[str]					=		model(_A     ,						attention_mask=_A     ,						token_type_ids=_A     ,						labels=_A )
       self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size) )



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							Any     ,						_A							:							Union[str, Any]     ,						_A							:							Dict     ,						_A							:							List[str]     ,						_A							:							Union[str, Any]     ,						*_A							:							Union[str, Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		BioGptModel(config=_A )
       model.to(_A )
       model.eval()

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

       # first forward pass
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		model(_A     ,						attention_mask=_A ).to_tuple()

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

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

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

       # get two different outputs
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model(_A     ,						attention_mask=_A )['''last_hidden_state''']
       __SCREAMING_SNAKE_CASE      :	List[str]					=		model(_A     ,						past_key_values=_A     ,						attention_mask=_A )['''last_hidden_state''']

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

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



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							List[str]     ,						_A							:							List[Any]     ,						_A							:							List[str]     ,						_A							:							Dict     ,						_A							:							int     ,						*_A							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		BioGptModel(config=_A ).to(_A ).eval()

       __SCREAMING_SNAKE_CASE      :	str					=		torch.ones(input_ids.shape     ,						dtype=torch.long     ,						device=_A )

       # first forward pass
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		model(_A     ,						attention_mask=_A     ,						use_cache=_A )

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

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

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

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model(_A     ,						attention_mask=_A )['''last_hidden_state''']
       __SCREAMING_SNAKE_CASE      :	str					=		model(_A     ,						attention_mask=_A     ,						past_key_values=_A )[
           '''last_hidden_state'''
       ]

       # select random slice
       __SCREAMING_SNAKE_CASE      :	str					=		ids_tensor((1,)     ,						output_from_past.shape[-1] ).item()
       __SCREAMING_SNAKE_CASE      :	Dict					=		output_from_no_past[:, -3:, random_slice_idx].detach()
       __SCREAMING_SNAKE_CASE      :	Dict					=		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(_A     ,						_A     ,						atol=1e-3 ) )



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							List[str]     ,						_A							:							str     ,						_A							:							Dict     ,						_A							:							List[str]     ,						_A							:							int     ,						*_A							:							Optional[Any]     ,						_A							:							str=False ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		BioGptForCausalLM(_A )
       model.to(_A )
       if gradient_checkpointing:
         model.gradient_checkpointing_enable()

       __SCREAMING_SNAKE_CASE      :	Dict					=		model(_A     ,						labels=_A )
       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							:							List[Any]     ,						_A							:							str     ,						*_A							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		BioGptModel(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		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_01 )
           self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )     ,						0.01 )



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							Union[str, Any]     ,						_A							:							str     ,						_A							:							Dict     ,						_A							:							List[str]     ,						_A							:							Dict     ,						*_A							:							List[Any] ):







       """simple docstring"""




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




     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		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
       ),		
       )      :	List[str]					=		config_and_inputs
       __SCREAMING_SNAKE_CASE      :	Any					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class     __UpperCamelCase (							lowerCAmelCase__ ,						lowerCAmelCase__ ,						lowerCAmelCase__ ,						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							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		BioGptModelTester(self )
       __SCREAMING_SNAKE_CASE      :	int					=		ConfigTester(self     ,						config_class=_A     ,						hidden_size=37 )



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       self.config_tester.run_common_tests()



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A )



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         __SCREAMING_SNAKE_CASE      :	Any					=		type
         self.model_tester.create_and_check_model(*_A )



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_A )



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*_A     ,						gradient_checkpointing=_A )



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_A )



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*_A )



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*_A )



     @slow
     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
       model.to(_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		'''left'''

       # Define PAD Token = EOS Token = 50256
       __SCREAMING_SNAKE_CASE      :	Dict					=		tokenizer.eos_token
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model.config.eos_token_id

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

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tokenizer(_A     ,						return_tensors='''pt'''     ,						padding=_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		inputs['''input_ids'''].to(_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model.generate(
           input_ids=_A     ,						attention_mask=inputs['''attention_mask'''].to(_A )     ,						)

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

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

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		tokenizer.batch_decode(_A     ,						skip_special_tokens=_A )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		tokenizer.decode(output_non_padded[0]     ,						skip_special_tokens=_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tokenizer.decode(output_padded[0]     ,						skip_special_tokens=_A )

       __SCREAMING_SNAKE_CASE      :	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(_A     ,						_A )
       self.assertListEqual(_A     ,						[non_padded_sentence, padded_sentence] )



     @slow
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




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




     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		3
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		'''multi_label_classification'''
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		input_dict['''input_ids''']
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		input_ids.ne(1 ).to(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		ids_tensor(
           [self.model_tester.batch_size, config.num_labels]     ,						self.model_tester.type_sequence_label_size ).to(torch.float )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		BioGptForSequenceClassification(_A )
       model.to(_A )
       model.eval()
       __SCREAMING_SNAKE_CASE      :	List[str]					=		model(_A     ,						attention_mask=_A     ,						labels=_A )
       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  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		torch.tensor([[2, 4805, 9, 656, 21]] )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		model(_A )[0]

       __SCREAMING_SNAKE_CASE      :	int					=		4_2384

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		torch.Size((1, 5, vocab_size) )
       self.assertEqual(output.shape     ,						_A )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		torch.tensor(
           [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]     ,						_A     ,						atol=1e-4 ) )




     @slow
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
       __SCREAMING_SNAKE_CASE      :	str					=		BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
       model.to(_A )

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		tokenizer('''COVID-19 is'''     ,						return_tensors='''pt''' ).to(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model.generate(
           **_A     ,						min_length=100     ,						max_length=1024     ,						num_beams=5     ,						early_stopping=_A     ,						)
       __SCREAMING_SNAKE_CASE      :	int					=		tokenizer.decode(output_ids[0]     ,						skip_special_tokens=_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, 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(_A     ,						_A )






import numpy as np
import qiskit


def 		lowerCamelCase__       (						_A = 8	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	np.random.default_rng(seed=_A				)

	# Roughly 25% of the qubits will contribute to the key.
	# So we take more than we need.
	snake_case_						       =	6 * key_len
	# Measurement basis for Alice's qubits.
	snake_case_						       =	rng.integers(2	,      size=_A				)
	# The set of states Alice will prepare.
	snake_case_						       =	rng.integers(2	,      size=_A				)
	# Measurement basis for Bob's qubits.
	snake_case_						       =	rng.integers(2	,      size=_A				)

	# Quantum Circuit to simulate BB84
	snake_case_						       =	qiskit.QuantumCircuit(_A	,      name="BB84"				)

	# Alice prepares her qubits according to rules above.
	for index, _ in enumerate(_A				):
		if alice_state[index] == 1:
			bbaa_circ.x(_A				)
		if alice_basis[index] == 1:
			bbaa_circ.h(_A				)
	bbaa_circ.barrier()

	# Bob measures the received qubits according to rules above.
	for index, _ in enumerate(_A				):
		if bob_basis[index] == 1:
			bbaa_circ.h(_A				)

	bbaa_circ.barrier()
	bbaa_circ.measure_all()

	# Simulate the quantum circuit.
	snake_case_						       =	qiskit.Aer.get_backend("aer_simulator"				)
	# We only need to run one shot because the key is unique.
	# Multiple shots will produce the same key.
	snake_case_						       =	qiskit.execute(_A	,      _A	,      shots=1	,      seed_simulator=_A				)
	# Returns the result of measurement.
	snake_case_						       =	job.result().get_counts(_A				).most_frequent()

	# Extracting the generated key from the simulation results.
	# Only keep measurement results where Alice and Bob chose the same basis.
	snake_case_						       =	"".join(
	    [
	        result_bit
	        for alice_basis_bit, bob_basis_bit, result_bit in zip(
	            _A	,      _A	,      _A				)
	        if alice_basis_bit == bob_basis_bit
	    ]				)

	# Get final key. Pad with 0 if too short, otherwise truncate.
	snake_case_						       =	gen_key[:key_len] if len(_A				) >= key_len else gen_key.ljust(_A	,      "0"				)
	return key


if __name__ == "__main__":
		print(f'''The generated key is : {bbaa(8, seed=0)}''')
		from doctest import testmod

		testmod()
