

'''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 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		Dict		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		str=13		,				UpperCAmelCase__    :		List[Any]=7		,				UpperCAmelCase__    :		Tuple=True		,				UpperCAmelCase__    :		Any=True		,				UpperCAmelCase__    :		Tuple=False		,				UpperCAmelCase__    :		List[str]=True		,				UpperCAmelCase__    :		List[str]=99		,				UpperCAmelCase__    :		List[Any]=32		,				UpperCAmelCase__    :		List[Any]=5		,				UpperCAmelCase__    :		Optional[Any]=4		,				UpperCAmelCase__    :		Dict=37		,				UpperCAmelCase__    :		Optional[Any]="gelu"		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		Union[str, Any]=0.1		,				UpperCAmelCase__    :		int=512		,				UpperCAmelCase__    :		List[str]=16		,				UpperCAmelCase__    :		Tuple=2		,				UpperCAmelCase__    :		Tuple=0.02		,				UpperCAmelCase__    :		int=3		,				UpperCAmelCase__    :		int=4		,				UpperCAmelCase__    :		Dict=None		,				):
										'''simple docstring'''
										lowercase   :       Any								=parent
										lowercase   :       int								=batch_size
										lowercase   :       Any								=seq_length
										lowercase   :       int								=is_training
										lowercase   :       List[str]								=use_input_mask
										lowercase   :       Union[str, Any]								=use_token_type_ids
										lowercase   :       List[str]								=use_labels
										lowercase   :       Dict								=vocab_size
										lowercase   :       Optional[Any]								=hidden_size
										lowercase   :       str								=num_hidden_layers
										lowercase   :       Optional[Any]								=num_attention_heads
										lowercase   :       Tuple								=intermediate_size
										lowercase   :       List[str]								=hidden_act
										lowercase   :       Optional[Any]								=hidden_dropout_prob
										lowercase   :       List[str]								=attention_probs_dropout_prob
										lowercase   :       int								=max_position_embeddings
										lowercase   :       Optional[Any]								=type_vocab_size
										lowercase   :       int								=type_sequence_label_size
										lowercase   :       List[str]								=initializer_range
										lowercase   :       Dict								=num_labels
										lowercase   :       Dict								=num_choices
										lowercase   :       Any								=scope







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )

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

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

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

										lowercase   :       str								=self.get_config()

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







				def    lowerCamelCase_  (					self    :		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=_SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				)







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								=BioGptModel(config=_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										model.eval()
										lowercase   :       Dict								=model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE      )
										lowercase   :       List[Any]								=model(_SCREAMING_SNAKE_CASE      )
										self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)      )







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[int]		,				):
										'''simple docstring'''
										lowercase   :       Tuple								=BioGptForCausalLM(config=_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										model.eval()
										lowercase   :       List[str]								=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    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		Optional[Any]		,				*UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptModel(config=_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										model.eval()

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

										# first forward pass
										lowercase		,  lowercase   :       int								=model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE      ).to_tuple()

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

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

										# append to next input_ids and attn_mask
										lowercase   :       Tuple								=torch.cat([input_ids, next_tokens]		,				dim=-1      )
										lowercase   :       Optional[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
										lowercase   :       Optional[int]								=model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE      )['''last_hidden_state''']
										lowercase   :       int								=model(_SCREAMING_SNAKE_CASE		,				past_key_values=_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE      )['''last_hidden_state''']

										# select random slice
										lowercase   :       Union[str, Any]								=ids_tensor((1,)		,				output_from_past.shape[-1]      ).item()
										lowercase   :       Dict								=output_from_no_past[:, -1, random_slice_idx].detach()
										lowercase   :       List[str]								=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    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		List[Any]		,				*UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=BioGptModel(config=_SCREAMING_SNAKE_CASE      ).to(_SCREAMING_SNAKE_CASE      ).eval()

										lowercase   :       Tuple								=torch.ones(input_ids.shape		,				dtype=torch.long		,				device=_SCREAMING_SNAKE_CASE      )

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

										lowercase		,  lowercase   :       Tuple								=outputs.to_tuple()

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

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

										lowercase   :       Any								=model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE      )['''last_hidden_state''']
										lowercase   :       List[str]								=model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				past_key_values=_SCREAMING_SNAKE_CASE      )[
										    '''last_hidden_state'''
										]

										# select random slice
										lowercase   :       str								=ids_tensor((1,)		,				output_from_past.shape[-1]      ).item()
										lowercase   :       Optional[int]								=output_from_no_past[:, -3:, random_slice_idx].detach()
										lowercase   :       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    lowerCamelCase_  (					self    :		int		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		List[str]		,				*UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		List[Any]=False      ):
										'''simple docstring'''
										lowercase   :       Tuple								=BioGptForCausalLM(_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										if gradient_checkpointing:
																model.gradient_checkpointing_enable()

										lowercase   :       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    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		str		,				*UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       int								=BioGptModel(_SCREAMING_SNAKE_CASE      )
										lowercase   :       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    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		int		,				*UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.num_labels
										lowercase   :       Tuple								=BioGptForTokenClassification(_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										model.eval()
										lowercase   :       List[Any]								=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    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.prepare_config_and_inputs()
										(
										    (
										    lowercase
										)		,  (
										    lowercase
										)		,  (
										    lowercase
										)		,  (
										    lowercase
										)		,  (
										    lowercase
										)		,  (
										    lowercase
										)		,  (
										    lowercase
										)		,  
										)   :       Any								=config_and_inputs
										lowercase   :       Union[str, Any]								={'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





@require_torch
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__	,       lowercase__	,       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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptModelTester(self      )
										lowercase   :       Optional[int]								=ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				hidden_size=37      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       int								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       str								=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																lowercase   :       str								=type
																self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_SCREAMING_SNAKE_CASE      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       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    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_SCREAMING_SNAKE_CASE      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*_SCREAMING_SNAKE_CASE      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*_SCREAMING_SNAKE_CASE      )







				@slow
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       int								=BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''      )
										model.to(_SCREAMING_SNAKE_CASE      )
										lowercase   :       Optional[Any]								=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''      )

										lowercase   :       List[Any]								='''left'''

										# Define PAD Token = EOS Token = 50256
										lowercase   :       Dict								=tokenizer.eos_token
										lowercase   :       int								=model.config.eos_token_id

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

										lowercase   :       int								=tokenizer(_SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''		,				padding=_SCREAMING_SNAKE_CASE      )
										lowercase   :       int								=inputs['''input_ids'''].to(_SCREAMING_SNAKE_CASE      )

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

										lowercase   :       List[Any]								=tokenizer(sentences[0]		,				return_tensors='''pt'''      ).input_ids.to(_SCREAMING_SNAKE_CASE      )
										lowercase   :       Optional[Any]								=model.generate(input_ids=_SCREAMING_SNAKE_CASE      )

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

										lowercase   :       Tuple								=tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		,				skip_special_tokens=_SCREAMING_SNAKE_CASE      )
										lowercase   :       Dict								=tokenizer.decode(output_non_padded[0]		,				skip_special_tokens=_SCREAMING_SNAKE_CASE      )
										lowercase   :       Optional[Any]								=tokenizer.decode(output_padded[0]		,				skip_special_tokens=_SCREAMING_SNAKE_CASE      )

										lowercase   :       Optional[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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''






										for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase   :       Optional[Any]								=BioGptModel.from_pretrained(_SCREAMING_SNAKE_CASE      )
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       str								=self.model_tester.prepare_config_and_inputs_for_common()
										lowercase   :       str								=3
										lowercase   :       Tuple								=input_dict['''input_ids''']
										lowercase   :       Any								=input_ids.ne(1      ).to(_SCREAMING_SNAKE_CASE      )
										lowercase   :       Optional[int]								=ids_tensor([self.model_tester.batch_size]		,				self.model_tester.type_sequence_label_size      )
										lowercase   :       Tuple								=BioGptForSequenceClassification(_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										model.eval()
										lowercase   :       List[str]								=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    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       List[str]								=self.model_tester.prepare_config_and_inputs_for_common()
										lowercase   :       Union[str, Any]								=3
										lowercase   :       Union[str, Any]								='''multi_label_classification'''
										lowercase   :       Optional[Any]								=input_dict['''input_ids''']
										lowercase   :       Optional[int]								=input_ids.ne(1      ).to(_SCREAMING_SNAKE_CASE      )
										lowercase   :       str								=ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]		,				self.model_tester.type_sequence_label_size      ).to(torch.float      )
										lowercase   :       List[Any]								=BioGptForSequenceClassification(_SCREAMING_SNAKE_CASE      )
										model.to(_SCREAMING_SNAKE_CASE      )
										model.eval()
										lowercase   :       Dict								=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    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''      )
										lowercase   :       Tuple								=torch.tensor([[2, 4805, 9, 656, 21]]      )
										lowercase   :       Dict								=model(_SCREAMING_SNAKE_CASE      )[0]

										lowercase   :       Dict								=42384

										lowercase   :       Tuple								=torch.Size((1, 5, vocab_size)      )
										self.assertEqual(output.shape		,				_SCREAMING_SNAKE_CASE      )

										lowercase   :       List[str]								=torch.tensor(
										    [[[-9.52_36, -9.89_18, 10.4557], [-11.0469, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]      )

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







				@slow
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''      )
										lowercase   :       List[str]								=BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''      )
										model.to(_SCREAMING_SNAKE_CASE      )

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

										lowercase   :       str								=(
										    '''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      )








'''simple docstring'''





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						BioGptTokenizer
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase   :       List[str]								=[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''w</w>''',
										    '''r</w>''',
										    '''t</w>''',
										    '''lo''',
										    '''low''',
										    '''er</w>''',
										    '''low</w>''',
										    '''lowest</w>''',
										    '''newer</w>''',
										    '''wider</w>''',
										    '''<unk>''',
										]
										lowercase   :       Any								=dict(zip(UpperCAmelCase__		,				range(len(UpperCAmelCase__      )      )      )      )
										lowercase   :       Union[str, Any]								=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

										lowercase   :       Optional[int]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file''']      )
										lowercase   :       Union[str, Any]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''merges_file''']      )
										with open(self.vocab_file		,				'''w'''      ) as fp:
																fp.write(json.dumps(UpperCAmelCase__      )      )
										with open(self.merges_file		,				'''w'''      ) as fp:
																fp.write('''\n'''.join(UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								='''lower newer'''
										lowercase   :       str								='''lower newer'''
										return input_text, output_text







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptTokenizer(self.vocab_file		,				self.merges_file      )

										lowercase   :       Any								='''lower'''
										lowercase   :       int								=['''low''', '''er</w>''']
										lowercase   :       Optional[Any]								=tokenizer.tokenize(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[int]								=tokens + ['''<unk>''']
										lowercase   :       Any								=[14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )		,				UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''      )

										lowercase   :       List[str]								=tokenizer.encode('''sequence builders'''		,				add_special_tokens=UpperCAmelCase__      )
										lowercase   :       Optional[int]								=tokenizer.encode('''multi-sequence build'''		,				add_special_tokens=UpperCAmelCase__      )

										lowercase   :       str								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__		,				UpperCAmelCase__      )

										self.assertTrue(encoded_sentence == [2] + text      )
										self.assertTrue(encoded_pair == [2] + text + [2] + text_a      )





