
"""simple docstring"""






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    BioGptForCausalLM,
					    BioGptForSequenceClassification,
					    BioGptForTokenClassification,
					    BioGptModel,
					    BioGptTokenizer,
					)
					from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Any		, lowerCamelCase_    :str		, lowerCamelCase_    :Dict=13		, lowerCamelCase_    :Dict=7		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :List[str]=True		, lowerCamelCase_    :Any=False		, lowerCamelCase_    :List[str]=True		, lowerCamelCase_    :Optional[Any]=99		, lowerCamelCase_    :List[str]=32		, lowerCamelCase_    :Union[str, Any]=5		, lowerCamelCase_    :Any=4		, lowerCamelCase_    :Dict=37		, lowerCamelCase_    :List[str]="gelu"		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :str=16		, lowerCamelCase_    :Dict=2		, lowerCamelCase_    :Tuple=0.0_2		, lowerCamelCase_    :Optional[int]=3		, lowerCamelCase_    :Any=4		, lowerCamelCase_    :Union[str, Any]=None		, )							->			Optional[int]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size					)

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

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

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

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_config()

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





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			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=lowerCamelCase_		, initializer_range=self.initializer_range		, )





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :str		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int					)							->			str:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, )							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= BioGptForCausalLM(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     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   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]		, *lowerCamelCase_    :Union[str, Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= BioGptModel(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

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

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

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

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

			# append to next input_ids and attn_mask
			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.cat([input_ids, next_tokens]		, dim=-1					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= 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       :     int				= model(lowerCamelCase_		, attention_mask=lowerCamelCase_					)['''last_hidden_state''']
			SCREAMING_SNAKE_CASE       :     Dict				= model(lowerCamelCase_		, past_key_values=lowerCamelCase_		, attention_mask=lowerCamelCase_					)['''last_hidden_state''']

			# select random slice
			SCREAMING_SNAKE_CASE       :     List[str]				= ids_tensor((1,)		, output_from_past.shape[-1]					).item()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= output_from_no_past[:, -1, random_slice_idx].detach()
			SCREAMING_SNAKE_CASE       :     List[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   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :str		, *lowerCamelCase_    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= BioGptModel(config=lowerCamelCase_					).to(lowerCamelCase_					).eval()

			SCREAMING_SNAKE_CASE       :     str				= torch.ones(input_ids.shape		, dtype=torch.long		, device=lowerCamelCase_					)

			# first forward pass
			SCREAMING_SNAKE_CASE       :     Any				= model(lowerCamelCase_		, attention_mask=lowerCamelCase_		, use_cache=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= outputs.to_tuple()

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

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

			SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_		, attention_mask=lowerCamelCase_					)['''last_hidden_state''']
			SCREAMING_SNAKE_CASE       :     str				= model(lowerCamelCase_		, attention_mask=lowerCamelCase_		, past_key_values=lowerCamelCase_					)[
			    '''last_hidden_state'''
			]

			# select random slice
			SCREAMING_SNAKE_CASE       :     Optional[int]				= ids_tensor((1,)		, output_from_past.shape[-1]					).item()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= output_from_no_past[:, -3:, random_slice_idx].detach()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= 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   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[Any]		, *lowerCamelCase_    :Tuple		, lowerCamelCase_    :List[Any]=False					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= BioGptForCausalLM(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			if gradient_checkpointing:
					model.gradient_checkpointing_enable()

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :int		, *lowerCamelCase_    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= BioGptModel(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[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_0_1					)
							self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]					) - 0.0					)		, 0.0_1					)





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :List[str]		, *lowerCamelCase_    :Tuple					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.num_labels
			SCREAMING_SNAKE_CASE       :     Any				= BioGptForTokenClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[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   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= 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[Any]				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     str				= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
			return config, inputs_dict





@require_torch
class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (BioGptForCausalLM,) if is_torch_available() else ()
	UpperCamelCase				=  (
	    {
	        """feature-extraction""": BioGptModel,
	        """text-classification""": BioGptForSequenceClassification,
	        """text-generation""": BioGptForCausalLM,
	        """token-classification""": BioGptForTokenClassification,
	        """zero-shot""": BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :str					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= BioGptModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ConfigTester(self		, config_class=lowerCamelCase_		, hidden_size=37					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			self.config_tester.run_common_tests()





	def   __lowerCAmelCase (						self    :Dict					)							->			List[str]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Tuple					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE       :     str				= type
					self.model_tester.create_and_check_model(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase_		, gradient_checkpointing=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :int					)							->			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(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase_					)





	@slow
	def   __lowerCAmelCase (						self    :Dict					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
			model.to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= BioGptTokenizer.from_pretrained('''microsoft/biogpt'''					)

			SCREAMING_SNAKE_CASE       :     Any				= '''left'''

			# Define PAD Token = EOS Token = 50256
			SCREAMING_SNAKE_CASE       :     Any				= tokenizer.eos_token
			SCREAMING_SNAKE_CASE       :     List[str]				= model.config.eos_token_id

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

			SCREAMING_SNAKE_CASE       :     Tuple				= tokenizer(lowerCamelCase_		, return_tensors='''pt'''		, padding=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= inputs['''input_ids'''].to(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= model.generate(
			    input_ids=lowerCamelCase_		, attention_mask=inputs['''attention_mask'''].to(lowerCamelCase_					)		, )

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer(sentences[0]		, return_tensors='''pt'''					).input_ids.to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Tuple				= model.generate(input_ids=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= 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(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model.generate(input_ids=lowerCamelCase_		, max_length=model.config.max_length - num_paddings					)

			SCREAMING_SNAKE_CASE       :     Any				= tokenizer.batch_decode(lowerCamelCase_		, skip_special_tokens=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer.decode(output_non_padded[0]		, skip_special_tokens=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer.decode(output_padded[0]		, skip_special_tokens=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     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   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Any:







			'''simple docstring'''




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







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     Dict				= 3
			SCREAMING_SNAKE_CASE       :     int				= '''multi_label_classification'''
			SCREAMING_SNAKE_CASE       :     Any				= input_dict['''input_ids''']
			SCREAMING_SNAKE_CASE       :     int				= input_ids.ne(1					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= ids_tensor(
			    [self.model_tester.batch_size, config.num_labels]		, self.model_tester.type_sequence_label_size					).to(torch.float					)
			SCREAMING_SNAKE_CASE       :     Any				= BioGptForSequenceClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@slow
	def   __lowerCAmelCase (						self    :Tuple					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor([[2, 48_05, 9, 6_56, 21]]					)
			SCREAMING_SNAKE_CASE       :     str				= model(lowerCamelCase_					)[0]

			SCREAMING_SNAKE_CASE       :     str				= 4_23_84

			SCREAMING_SNAKE_CASE       :     List[str]				= torch.Size((1, 5, vocab_size)					)
			self.assertEqual(output.shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= 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   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= BioGptTokenizer.from_pretrained('''microsoft/biogpt'''					)
			SCREAMING_SNAKE_CASE       :     str				= BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''					)
			model.to(lowerCamelCase_					)

			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer('''COVID-19 is'''		, return_tensors='''pt'''					).to(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= model.generate(
			    **lowerCamelCase_		, min_length=1_00		, max_length=10_24		, num_beams=5		, early_stopping=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     str				= tokenizer.decode(output_ids[0]		, skip_special_tokens=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= (
			    '''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_					)







"""simple docstring"""






from __future__ import annotations

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





def __A						(   a_ :							float		,		a_ :							float		,		a_ :							bool = False     )->							list[float]:




		'''simple docstring'''






		if radian_mode:
				return [magnitude * cos(a_     ), magnitude * sin(a_     )]
		return [magnitude * cos(radians(a_     )     ), magnitude * sin(radians(a_     )     )]





def __A						(   a_ :							NDArray[floataa]		,		a_ :							NDArray[floataa]		,		a_ :							float = 10**-1     )->							bool:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     NDArray[floataa]				= cross(a_		,		a_     )
		SCREAMING_SNAKE_CASE       :     float				= sum(a_     )
		return abs(a_     ) < eps


if __name__ == "__main__":
					# Test to check if it works
					lowerCamelCase__ :       Optional[Any]						     =			array(
					    [
					        polar_force(7_1_8.4, 180 - 30),
					        polar_force(8_7_9.5_4, 45),
					        polar_force(100, -90),
					    ]
					)

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

					assert in_static_equilibrium(forces, location)

					# Problem 1 in image_data/2D_problems.jpg
					lowerCamelCase__ :       Union[str, Any]						     =			array(
					    [
					        polar_force(30 * 9.8_1, 15),
					        polar_force(215, 180 - 45),
					        polar_force(264, 90 - 30),
					    ]
					)

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

					assert in_static_equilibrium(forces, location)

					# Problem in image_data/2D_problems_1.jpg
					lowerCamelCase__ :       Union[str, Any]						     =			array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

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

					assert in_static_equilibrium(forces, location)

					import doctest

					doctest.testmod()





