


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_					:







							def __init__(     self,    __a,    __a=13,    __a=7,    __a=True,    __a=True,    __a=False,    __a=True,    __a=99,    __a=32,    __a=5,    __a=4,    __a=37,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=16,    __a=2,    __a=0.02,    __a=3,    __a=4,    __a=None,    ):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					ids_tensor([self.batch_size, self.seq_length],    self.vocab_size)

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

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

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

														_lowerCAmelCase  :							str				     =					self.get_config()

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







							def      snake_case__   (     self):

														'''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      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    __a,    __a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    __a,    __a,    __a,    __a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					BioGptForCausalLM(config=_SCREAMING_SNAKE_CASE)
														model.to(_SCREAMING_SNAKE_CASE)
														model.eval()
														_lowerCAmelCase  :							Optional[Any]				     =					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      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    *__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					BioGptModel(config=_SCREAMING_SNAKE_CASE)
														model.to(_SCREAMING_SNAKE_CASE)
														model.eval()

														# create attention mask
														_lowerCAmelCase  :							int				     =					torch.ones(input_ids.shape,    dtype=torch.long,    device=_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							int				     =					self.seq_length // 2
														_lowerCAmelCase  :							int				     =					0

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

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

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

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

														# get two different outputs
														_lowerCAmelCase  :							Tuple				     =					model(_SCREAMING_SNAKE_CASE,    attention_mask=_SCREAMING_SNAKE_CASE)["last_hidden_state"]
														_lowerCAmelCase  :							Any				     =					model(_SCREAMING_SNAKE_CASE,    past_key_values=_SCREAMING_SNAKE_CASE,    attention_mask=_SCREAMING_SNAKE_CASE)["last_hidden_state"]

														# select random slice
														_lowerCAmelCase  :							Tuple				     =					ids_tensor((1,),    output_from_past.shape[-1]).item()
														_lowerCAmelCase  :							int				     =					output_from_no_past[:, -1, random_slice_idx].detach()
														_lowerCAmelCase  :							Dict				     =					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      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    *__a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					BioGptModel(config=_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE).eval()

														_lowerCAmelCase  :							Union[str, Any]				     =					torch.ones(input_ids.shape,    dtype=torch.long,    device=_SCREAMING_SNAKE_CASE)

														# first forward pass
														_lowerCAmelCase  :							Tuple				     =					model(_SCREAMING_SNAKE_CASE,    attention_mask=_SCREAMING_SNAKE_CASE,    use_cache=_SCREAMING_SNAKE_CASE)

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					outputs.to_tuple()

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

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

														_lowerCAmelCase  :							Dict				     =					model(_SCREAMING_SNAKE_CASE,    attention_mask=_SCREAMING_SNAKE_CASE)["last_hidden_state"]
														_lowerCAmelCase  :							Tuple				     =					model(_SCREAMING_SNAKE_CASE,    attention_mask=_SCREAMING_SNAKE_CASE,    past_key_values=_SCREAMING_SNAKE_CASE)[
														    "last_hidden_state"
														]

														# select random slice
														_lowerCAmelCase  :							List[str]				     =					ids_tensor((1,),    output_from_past.shape[-1]).item()
														_lowerCAmelCase  :							List[str]				     =					output_from_no_past[:, -3:, random_slice_idx].detach()
														_lowerCAmelCase  :							List[Any]				     =					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      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    *__a,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					BioGptForCausalLM(_SCREAMING_SNAKE_CASE)
														model.to(_SCREAMING_SNAKE_CASE)
														if gradient_checkpointing:
																					model.gradient_checkpointing_enable()

														_lowerCAmelCase  :							str				     =					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      snake_case__   (     self,    __a,    *__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					BioGptModel(_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							Union[str, Any]				     =					model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
														for key in model.state_dict().keys():
																					if "c_proj" in key and "weight" in key:
																												self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std),    0.001)
																												self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0),    0.01)







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    *__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.num_labels
														_lowerCAmelCase  :							Optional[int]				     =					BioGptForTokenClassification(_SCREAMING_SNAKE_CASE)
														model.to(_SCREAMING_SNAKE_CASE)
														model.eval()
														_lowerCAmelCase  :							Dict				     =					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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					self.prepare_config_and_inputs()
														(
														    (
														    _lowerCAmelCase
														)     ,					(
														    _lowerCAmelCase
														)     ,					(
														    _lowerCAmelCase
														)     ,					(
														    _lowerCAmelCase
														)     ,					(
														    _lowerCAmelCase
														)     ,					(
														    _lowerCAmelCase
														)     ,					(
														    _lowerCAmelCase
														)     ,					
														)  :							Union[str, Any]				     =					config_and_inputs
														_lowerCAmelCase  :							Any				     =					{"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict



@require_torch
class 						UpperCAmelCase_					( a     ,      a     ,      a     ,      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      snake_case__   (     self):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					_lowerCAmelCase  :							Tuple				     =					type
																					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_SCREAMING_SNAKE_CASE)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_SCREAMING_SNAKE_CASE)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_weight_initialization(*_SCREAMING_SNAKE_CASE)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_for_token_classification(*_SCREAMING_SNAKE_CASE)







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					BioGptForCausalLM.from_pretrained("microsoft/biogpt")
														model.to(_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							str				     =					BioGptTokenizer.from_pretrained("microsoft/biogpt")

														_lowerCAmelCase  :							str				     =					"left"

														# Define PAD Token = EOS Token = 50256
														_lowerCAmelCase  :							Tuple				     =					tokenizer.eos_token
														_lowerCAmelCase  :							Optional[int]				     =					model.config.eos_token_id

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

														_lowerCAmelCase  :							int				     =					tokenizer(_SCREAMING_SNAKE_CASE,    return_tensors="pt",    padding=_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							Optional[int]				     =					inputs["input_ids"].to(_SCREAMING_SNAKE_CASE)

														_lowerCAmelCase  :							int				     =					model.generate(
														    input_ids=_SCREAMING_SNAKE_CASE,    attention_mask=inputs["attention_mask"].to(_SCREAMING_SNAKE_CASE),    )

														_lowerCAmelCase  :							List[str]				     =					tokenizer(sentences[0],    return_tensors="pt").input_ids.to(_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							str				     =					model.generate(input_ids=_SCREAMING_SNAKE_CASE)

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

														_lowerCAmelCase  :							int				     =					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE,    skip_special_tokens=_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.decode(output_non_padded[0],    skip_special_tokens=_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							Tuple				     =					tokenizer.decode(output_padded[0],    skip_special_tokens=_SCREAMING_SNAKE_CASE)

														_lowerCAmelCase  :							Tuple				     =					[
														    "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      snake_case__   (     self):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[int]				     =					self.model_tester.prepare_config_and_inputs_for_common()
														_lowerCAmelCase  :							Any				     =					3
														_lowerCAmelCase  :							List[str]				     =					input_dict["input_ids"]
														_lowerCAmelCase  :							int				     =					input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							Any				     =					ids_tensor([self.model_tester.batch_size],    self.model_tester.type_sequence_label_size)
														_lowerCAmelCase  :							Dict				     =					BioGptForSequenceClassification(_SCREAMING_SNAKE_CASE)
														model.to(_SCREAMING_SNAKE_CASE)
														model.eval()
														_lowerCAmelCase  :							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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					self.model_tester.prepare_config_and_inputs_for_common()
														_lowerCAmelCase  :							List[Any]				     =					3
														_lowerCAmelCase  :							Tuple				     =					"multi_label_classification"
														_lowerCAmelCase  :							List[Any]				     =					input_dict["input_ids"]
														_lowerCAmelCase  :							str				     =					input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							List[str]				     =					ids_tensor(
														    [self.model_tester.batch_size, config.num_labels],    self.model_tester.type_sequence_label_size).to(torch.float)
														_lowerCAmelCase  :							Dict				     =					BioGptForSequenceClassification(_SCREAMING_SNAKE_CASE)
														model.to(_SCREAMING_SNAKE_CASE)
														model.eval()
														_lowerCAmelCase  :							List[Any]				     =					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 						UpperCAmelCase_					( unittest.TestCase):







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					BioGptForCausalLM.from_pretrained("microsoft/biogpt")
														_lowerCAmelCase  :							Tuple				     =					torch.tensor([[2, 4805, 9, 656, 21]])
														_lowerCAmelCase  :							Dict				     =					model(_SCREAMING_SNAKE_CASE)[0]

														_lowerCAmelCase  :							Optional[Any]				     =					4_2384

														_lowerCAmelCase  :							Optional[Any]				     =					torch.Size((1, 5, vocab_size))
														self.assertEqual(output.shape,    _SCREAMING_SNAKE_CASE)

														_lowerCAmelCase  :							List[str]				     =					torch.tensor(
														    [[[-9.5_236, -9.8_918, 10.4557], [-11.0469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]])

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







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					BioGptTokenizer.from_pretrained("microsoft/biogpt")
														_lowerCAmelCase  :							Tuple				     =					BioGptForCausalLM.from_pretrained("microsoft/biogpt")
														model.to(_SCREAMING_SNAKE_CASE)

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

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





from __future__ import annotations

from collections.abc import MutableSequence



class 						UpperCAmelCase_					:







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														if len(__a) != degree + 1:
																					raise ValueError(
																					    "The number of coefficients should be equal to the degree + 1.")

														_lowerCAmelCase  :							list[float]				     =					list(__a)
														_lowerCAmelCase  :							Any				     =					degree







							def __add__(     self,    __a):

														'''simple docstring'''

														if self.degree > polynomial_a.degree:
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[:]
																					for i in range(polynomial_a.degree + 1):
																												coefficients[i] += polynomial_a.coefficients[i]
																					return Polynomial(self.degree,    __a)
														else:
																					_lowerCAmelCase  :							Union[str, Any]				     =					polynomial_a.coefficients[:]
																					for i in range(self.degree + 1):
																												coefficients[i] += self.coefficients[i]
																					return Polynomial(polynomial_a.degree,    __a)







							def __sub__(     self,    __a):

														'''simple docstring'''

														return self + polynomial_a * Polynomial(0,    [-1])







							def __neg__(     self):

														'''simple docstring'''

														return Polynomial(self.degree,    [-c for c in self.coefficients])







							def __mul__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + polynomial_a.degree + 1)
														for i in range(self.degree + 1):
																					for j in range(polynomial_a.degree + 1):
																												coefficients[i + j] += (
																												    self.coefficients[i] * polynomial_a.coefficients[j]
																												)

														return Polynomial(self.degree + polynomial_a.degree,    __a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							int | float				     =					0
														for i in range(self.degree + 1):
																					result += self.coefficients[i] * (substitution**i)
														return result







							def __str__(     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					""
														for i in range(self.degree,    -1,    -1):
																					if self.coefficients[i] == 0:
																												continue
																					elif self.coefficients[i] > 0:
																												if polynomial:
																																			polynomial += " + "
																					else:
																												polynomial += " - "

																					if i == 0:
																												polynomial += str(abs(self.coefficients[i]))
																					elif i == 1:
																												polynomial += str(abs(self.coefficients[i])) + "x"
																					else:
																												polynomial += str(abs(self.coefficients[i])) + "x^" + str(__a)

														return polynomial







							def __repr__(     self):

														'''simple docstring'''

														return self.__str__()







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * self.degree
														for i in range(self.degree):
																					_lowerCAmelCase  :							List[Any]				     =					self.coefficients[i + 1] * (i + 1)
														return Polynomial(self.degree - 1,    __a)







							def      snake_case__   (     self,    __a = 0):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + 2)
														_lowerCAmelCase  :							Optional[Any]				     =					constant
														for i in range(self.degree + 1):
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[i] / (i + 1)
														return Polynomial(self.degree + 1,    __a)







							def __eq__(     self,    __a):

														'''simple docstring'''

														if not isinstance(__a,    __a):
																					return False

														if self.degree != polynomial_a.degree:
																					return False

														for i in range(self.degree + 1):
																					if self.coefficients[i] != polynomial_a.coefficients[i]:
																												return False

														return True







							def __ne__(     self,    __a):

														'''simple docstring'''

														return not self.__eq__(__a)
