"""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  _snake_case     :






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[int]=13							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=True							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			List[Any]=False							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			List[str]=99							,					UpperCAmelCase      :			str=32							,					UpperCAmelCase      :			Dict=5							,					UpperCAmelCase      :			Optional[int]=4							,					UpperCAmelCase      :			str=37							,					UpperCAmelCase      :			List[str]="gelu"							,					UpperCAmelCase      :			int=0.1							,					UpperCAmelCase      :			Optional[int]=0.1							,					UpperCAmelCase      :			Union[str, Any]=512							,					UpperCAmelCase      :			Dict=16							,					UpperCAmelCase      :			Any=2							,					UpperCAmelCase      :			Any=0.0_2							,					UpperCAmelCase      :			List[Any]=3							,					UpperCAmelCase      :			Dict=4							,					UpperCAmelCase      :			int=None							,					):
													__lowerCamelCase					:		Optional[int]		   =     parent
													__lowerCamelCase					:		Optional[int]		   =     batch_size
													__lowerCamelCase					:		Optional[Any]		   =     seq_length
													__lowerCamelCase					:		Union[str, Any]		   =     is_training
													__lowerCamelCase					:		Tuple		   =     use_input_mask
													__lowerCamelCase					:		List[Any]		   =     use_token_type_ids
													__lowerCamelCase					:		List[str]		   =     use_labels
													__lowerCamelCase					:		Union[str, Any]		   =     vocab_size
													__lowerCamelCase					:		Any		   =     hidden_size
													__lowerCamelCase					:		List[str]		   =     num_hidden_layers
													__lowerCamelCase					:		Tuple		   =     num_attention_heads
													__lowerCamelCase					:		int		   =     intermediate_size
													__lowerCamelCase					:		Any		   =     hidden_act
													__lowerCamelCase					:		Union[str, Any]		   =     hidden_dropout_prob
													__lowerCamelCase					:		int		   =     attention_probs_dropout_prob
													__lowerCamelCase					:		Union[str, Any]		   =     max_position_embeddings
													__lowerCamelCase					:		Optional[int]		   =     type_vocab_size
													__lowerCamelCase					:		Any		   =     type_sequence_label_size
													__lowerCamelCase					:		Optional[int]		   =     initializer_range
													__lowerCamelCase					:		Union[str, Any]		   =     num_labels
													__lowerCamelCase					:		int		   =     num_choices
													__lowerCamelCase					:		Any		   =     scope






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		List[Any]		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

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

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

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

													__lowerCamelCase					:		Union[str, Any]		   =     self.get_config()

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






						def       lowerCamelCase__     (       self      :			Tuple    ):
													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=UpperCAmelCase							,					initializer_range=self.initializer_range							,					)






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		Tuple		   =     BioGptModel(config=UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Tuple		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int							,					):
													__lowerCamelCase					:		Optional[int]		   =     BioGptForCausalLM(config=UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Tuple		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					token_type_ids=UpperCAmelCase							,					labels=UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			str							,					*UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[int]		   =     BioGptModel(config=UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()

													# create attention mask
													__lowerCamelCase					:		Tuple		   =     torch.ones(input_ids.shape							,					dtype=torch.long							,					device=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.seq_length // 2
													__lowerCamelCase					:		str		   =     0

													# first forward pass
													__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    ).to_tuple()

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

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

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

													# get two different outputs
													__lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )["last_hidden_state"]
													__lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase							,					past_key_values=UpperCAmelCase							,					attention_mask=UpperCAmelCase    )["last_hidden_state"]

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

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






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Any							,					*UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		int		   =     BioGptModel(config=UpperCAmelCase    ).to(UpperCAmelCase    ).eval()

													__lowerCamelCase					:		List[Any]		   =     torch.ones(input_ids.shape							,					dtype=torch.long							,					device=UpperCAmelCase    )

													# first forward pass
													__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					use_cache=UpperCAmelCase    )

													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     outputs.to_tuple()

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

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

													__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )["last_hidden_state"]
													__lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					past_key_values=UpperCAmelCase    )[
													    "last_hidden_state"
													]

													# select random slice
													__lowerCamelCase					:		List[Any]		   =     ids_tensor((1,)							,					output_from_past.shape[-1]    ).item()
													__lowerCamelCase					:		Tuple		   =     output_from_no_past[:, -3:, random_slice_idx].detach()
													__lowerCamelCase					:		str		   =     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(UpperCAmelCase							,					UpperCAmelCase							,					atol=1E-3    )    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					*UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int=False    ):
													__lowerCamelCase					:		Union[str, Any]		   =     BioGptForCausalLM(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													__lowerCamelCase					:		str		   =     model(UpperCAmelCase							,					labels=UpperCAmelCase    )
													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[int]							,					UpperCAmelCase      :			Optional[Any]							,					*UpperCAmelCase      :			str    ):
													__lowerCamelCase					:		List[Any]		   =     BioGptModel(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[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    )							,					0.0_0_1    )
																											self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]    ) - 0.0    )							,					0.0_1    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Dict							,					*UpperCAmelCase      :			Tuple    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.num_labels
													__lowerCamelCase					:		Union[str, Any]		   =     BioGptForTokenClassification(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					token_type_ids=UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)    )







						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Any		   =     self.prepare_config_and_inputs()
													(
													    (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  
													)					:		Optional[Any]		   =     config_and_inputs
													__lowerCamelCase					:		Tuple		   =     {"input_ids": input_ids, "attention_mask": input_mask}
													return config, inputs_dict

@require_torch
class  _snake_case     (					a__				, a__				, a__				, unittest.TestCase		):
						snake_case__			  =			(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						snake_case__			  =			(BioGptForCausalLM,) if is_torch_available() else ()
						snake_case__			  =			(
						    {
						        "feature-extraction": BioGptModel,
						        "text-classification": BioGptForSequenceClassification,
						        "text-generation": BioGptForCausalLM,
						        "token-classification": BioGptForTokenClassification,
						        "zero-shot": BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     BioGptModelTester(self    )
													__lowerCamelCase					:		Optional[int]		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					hidden_size=37    )






						def       lowerCamelCase__     (       self      :			Any    ):
													self.config_tester.run_common_tests()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		List[Any]		   =     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(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*UpperCAmelCase							,					gradient_checkpointing=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCAmelCase    )






						@slow
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Any		   =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"    )
													model.to(UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     BioGptTokenizer.from_pretrained("microsoft/biogpt"    )

													__lowerCamelCase					:		int		   =     "left"

													# Define PAD Token = EOS Token = 50256
													__lowerCamelCase					:		Union[str, Any]		   =     tokenizer.eos_token
													__lowerCamelCase					:		Union[str, Any]		   =     model.config.eos_token_id

													# use different length sentences to test batching
													__lowerCamelCase					:		List[str]		   =     [
													    "Hello, my dog is a little",
													    "Today, I",
													]

													__lowerCamelCase					:		str		   =     tokenizer(UpperCAmelCase							,					return_tensors="pt"							,					padding=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     inputs["input_ids"].to(UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     model.generate(
													    input_ids=UpperCAmelCase							,					attention_mask=inputs["attention_mask"].to(UpperCAmelCase    )							,					)

													__lowerCamelCase					:		Optional[Any]		   =     tokenizer(sentences[0]							,					return_tensors="pt"    ).input_ids.to(UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     model.generate(input_ids=UpperCAmelCase    )

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

													__lowerCamelCase					:		Tuple		   =     tokenizer.batch_decode(UpperCAmelCase							,					skip_special_tokens=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.decode(output_non_padded[0]							,					skip_special_tokens=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.decode(output_padded[0]							,					skip_special_tokens=UpperCAmelCase    )

													__lowerCamelCase					:		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(UpperCAmelCase							,					UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					[non_padded_sentence, padded_sentence]    )






						@slow
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowerCamelCase					:		str		   =     BioGptModel.from_pretrained(UpperCAmelCase    )
																				self.assertIsNotNone(UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCamelCase					:		List[Any]		   =     3
													__lowerCamelCase					:		Optional[int]		   =     input_dict["input_ids"]
													__lowerCamelCase					:		int		   =     input_ids.ne(1    ).to(UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size    )
													__lowerCamelCase					:		str		   =     BioGptForSequenceClassification(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
													self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)    )







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

@require_torch
class  _snake_case     (					unittest.TestCase		):






						@slow
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Any		   =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"    )
													__lowerCamelCase					:		Dict		   =     torch.tensor([[2, 4805, 9, 656, 21]]    )
													__lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase    )[0]

													__lowerCamelCase					:		int		   =     42384

													__lowerCamelCase					:		int		   =     torch.Size((1, 5, vocab_size)    )
													self.assertEqual(output.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     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]							,					UpperCAmelCase							,					atol=1E-4    )    )







						@slow
						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		int		   =     BioGptTokenizer.from_pretrained("microsoft/biogpt"    )
													__lowerCamelCase					:		str		   =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"    )
													model.to(UpperCAmelCase    )

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

													__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(UpperCAmelCase							,					UpperCAmelCase    )
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


if __name__ == "__main__":
				print(F"""{solution() = }""")