


from __future__ import annotations

import math

lowerCAmelCase							=						"""2020.9.26"""
lowerCAmelCase							=						"""xcodz-dot, cclaus, dhruvmanila"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)		->    tuple[float, float]:




						'''simple docstring'''







						if not all(isinstance(lowercase_			,					(float, int)			) for val in locals().values()			):
												__UpperCAmelCase :       Tuple  						=  f"Input values must either be float or int: {list(locals().values()			)}"
												raise TypeError(lowercase_			)
						__UpperCAmelCase :       List[Any]  						=  ((x * distance) / (z + distance)) * scale
						__UpperCAmelCase :       Tuple  						=  ((y * distance) / (z + distance)) * scale
						return projected_x, projected_y




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)		->    tuple[float, float, float]:




						'''simple docstring'''







						if not isinstance(lowercase_			,					lowercase_			):
												raise TypeError('''Axis must be a str'''			)
						__UpperCAmelCase :       Any  						=  locals()
						del input_variables["axis"]
						if not all(isinstance(lowercase_			,					(float, int)			) for val in input_variables.values()			):
												__UpperCAmelCase :       Union[str, Any]  						=  (
												    '''Input values except axis must either be float or int: '''
												    f"{list(input_variables.values()			)}"
												)
												raise TypeError(lowercase_			)
						__UpperCAmelCase :       Tuple  						=  (angle % 360) / 450 * 180 / math.pi
						if axis == "z":
												__UpperCAmelCase :       Any  						=  x * math.cos(lowercase_			) - y * math.sin(lowercase_			)
												__UpperCAmelCase :       Tuple  						=  y * math.cos(lowercase_			) + x * math.sin(lowercase_			)
												__UpperCAmelCase :       List[Any]  						=  z
						elif axis == "x":
												__UpperCAmelCase :       Union[str, Any]  						=  y * math.cos(lowercase_			) - z * math.sin(lowercase_			)
												__UpperCAmelCase :       str  						=  z * math.cos(lowercase_			) + y * math.sin(lowercase_			)
												__UpperCAmelCase :       Any  						=  x
						elif axis == "y":
												__UpperCAmelCase :       str  						=  x * math.cos(lowercase_			) - z * math.sin(lowercase_			)
												__UpperCAmelCase :       Any  						=  z * math.cos(lowercase_			) + x * math.sin(lowercase_			)
												__UpperCAmelCase :       str  						=  y
						else:
												raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\''''			)

						return new_x, new_y, new_z


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F'{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }')
				print(F'{rotate(1.0, 2.0, 3.0, "y", 90.0) = }')





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        lowerCamelCase      :


	def __init__(						self							,    lowercase__							,    lowercase__=1_3							,    lowercase__=7							,    lowercase__=True							,    lowercase__=True							,    lowercase__=False							,    lowercase__=True							,    lowercase__=9_9							,    lowercase__=3_2							,    lowercase__=5							,    lowercase__=4							,    lowercase__=3_7							,    lowercase__="gelu"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=5_1_2							,    lowercase__=1_6							,    lowercase__=2							,    lowercase__=0.0_2							,    lowercase__=3							,    lowercase__=4							,    lowercase__=None							,    ):
							__UpperCAmelCase :       Tuple  						=  parent
							__UpperCAmelCase :       List[Any]  						=  batch_size
							__UpperCAmelCase :       Optional[Any]  						=  seq_length
							__UpperCAmelCase :       Tuple  						=  is_training
							__UpperCAmelCase :       List[Any]  						=  use_input_mask
							__UpperCAmelCase :       List[str]  						=  use_token_type_ids
							__UpperCAmelCase :       Union[str, Any]  						=  use_labels
							__UpperCAmelCase :       Union[str, Any]  						=  vocab_size
							__UpperCAmelCase :       Optional[int]  						=  hidden_size
							__UpperCAmelCase :       Any  						=  num_hidden_layers
							__UpperCAmelCase :       Optional[Any]  						=  num_attention_heads
							__UpperCAmelCase :       str  						=  intermediate_size
							__UpperCAmelCase :       Dict  						=  hidden_act
							__UpperCAmelCase :       str  						=  hidden_dropout_prob
							__UpperCAmelCase :       Optional[Any]  						=  attention_probs_dropout_prob
							__UpperCAmelCase :       List[str]  						=  max_position_embeddings
							__UpperCAmelCase :       Tuple  						=  type_vocab_size
							__UpperCAmelCase :       int  						=  type_sequence_label_size
							__UpperCAmelCase :       List[Any]  						=  initializer_range
							__UpperCAmelCase :       List[str]  						=  num_labels
							__UpperCAmelCase :       Dict  						=  num_choices
							__UpperCAmelCase :       Union[str, Any]  						=  scope


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size)

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

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

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

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

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


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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Union[str, Any]  						=  BioGptModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       int  						=  model(lowercase__							,    attention_mask=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  model(lowercase__)
							self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
							__UpperCAmelCase :       Optional[Any]  						=  BioGptForCausalLM(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       List[Any]  						=  model(lowercase__							,    attention_mask=lowercase__							,    token_type_ids=lowercase__							,    labels=lowercase__)
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size))


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       str  						=  BioGptModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()

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

							# first forward pass
							__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  model(lowercase__							,    attention_mask=lowercase__).to_tuple()

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

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

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

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

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

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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       int  						=  BioGptModel(config=lowercase__).to(lowercase__).eval()

							__UpperCAmelCase :       List[str]  						=  torch.ones(input_ids.shape							,    dtype=torch.long							,    device=lowercase__)

							# first forward pass
							__UpperCAmelCase :       Union[str, Any]  						=  model(lowercase__							,    attention_mask=lowercase__							,    use_cache=lowercase__)

							__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  outputs.to_tuple()

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

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

							__UpperCAmelCase :       List[Any]  						=  model(lowercase__							,    attention_mask=lowercase__)['''last_hidden_state''']
							__UpperCAmelCase :       int  						=  model(lowercase__							,    attention_mask=lowercase__							,    past_key_values=lowercase__)[
							    '''last_hidden_state'''
							]

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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__							,    lowercase__=False):
							__UpperCAmelCase :       int  						=  BioGptForCausalLM(lowercase__)
							model.to(lowercase__)
							if gradient_checkpointing:
													model.gradient_checkpointing_enable()

							__UpperCAmelCase :       Tuple  						=  model(lowercase__							,    labels=lowercase__)
							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    A(						self							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       Optional[int]  						=  BioGptModel(lowercase__)
							__UpperCAmelCase :       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    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    *lowercase__):
							__UpperCAmelCase :       Optional[Any]  						=  self.num_labels
							__UpperCAmelCase :       List[str]  						=  BioGptForTokenClassification(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       List[str]  						=  model(lowercase__							,    attention_mask=lowercase__							,    token_type_ids=lowercase__)
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels))


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.prepare_config_and_inputs()
							(
							    (
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			(
							    __UpperCAmelCase
							)						,			
							) :       int  						=  config_and_inputs
							__UpperCAmelCase :       List[Any]  						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class        lowerCamelCase      (     _UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		str        =	(
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	_lowerCAmelCase    :		int        =	(BioGptForCausalLM,) if is_torch_available() else ()
	_lowerCAmelCase    :		Union[str, Any]        =	(
	    {
	        '''feature-extraction''': BioGptModel,
	        '''text-classification''': BioGptForSequenceClassification,
	        '''text-generation''': BioGptForCausalLM,
	        '''token-classification''': BioGptForTokenClassification,
	        '''zero-shot''': BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCAmelCase    :		List[Any]        =	False


	def    A(						self):
							__UpperCAmelCase :       int  						=  BioGptModelTester(self)
							__UpperCAmelCase :       int  						=  ConfigTester(self							,    config_class=lowercase__							,    hidden_size=3_7)


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


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													__UpperCAmelCase :       Dict  						=  type
													self.model_tester.create_and_check_model(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*lowercase__							,    gradient_checkpointing=lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       Any  						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(lowercase__)
							__UpperCAmelCase :       Dict  						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

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

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

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

							__UpperCAmelCase :       int  						=  tokenizer(lowercase__							,    return_tensors='''pt'''							,    padding=lowercase__)
							__UpperCAmelCase :       Union[str, Any]  						=  inputs['''input_ids'''].to(lowercase__)

							__UpperCAmelCase :       int  						=  model.generate(
							    input_ids=lowercase__							,    attention_mask=inputs['''attention_mask'''].to(lowercase__)							,    )

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

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

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

							__UpperCAmelCase :       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(lowercase__							,    lowercase__)
							self.assertListEqual(lowercase__							,    [non_padded_sentence, padded_sentence])


	@slow
	def    A(						self):
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__UpperCAmelCase :       Union[str, Any]  						=  BioGptModel.from_pretrained(lowercase__)
													self.assertIsNotNone(lowercase__)


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


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase :       List[Any]  						=  3
							__UpperCAmelCase :       Union[str, Any]  						=  '''multi_label_classification'''
							__UpperCAmelCase :       List[Any]  						=  input_dict['''input_ids''']
							__UpperCAmelCase :       Tuple  						=  input_ids.ne(1).to(lowercase__)
							__UpperCAmelCase :       List[str]  						=  ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]							,    self.model_tester.type_sequence_label_size).to(torch.float)
							__UpperCAmelCase :       List[Any]  						=  BioGptForSequenceClassification(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase :       Optional[Any]  						=  model(lowercase__							,    attention_mask=lowercase__							,    labels=lowercase__)
							self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels))






@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@slow
	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							__UpperCAmelCase :       Optional[Any]  						=  torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]])
							__UpperCAmelCase :       int  						=  model(lowercase__)[0]

							__UpperCAmelCase :       Any  						=  4_2_3_8_4

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

							__UpperCAmelCase :       Dict  						=  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]							,    lowercase__							,    atol=1e-4))


	@slow
	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')
							__UpperCAmelCase :       int  						=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(lowercase__)

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

							__UpperCAmelCase :       int  						=  (
							    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
							    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
							    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
							    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
							    ''' more than 800,000 deaths.'''
							)
							self.assertEqual(lowercase__							,    lowercase__)

