





'''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      UpperCamelCase__					:




					"""simple docstring"""


					def __init__(						self				:			List[str]      ,						lowerCamelCase_				:			Union[str, Any]      ,						lowerCamelCase_				:			Any=13      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			int=True      ,						lowerCamelCase_				:			Dict=False      ,						lowerCamelCase_				:			Tuple=True      ,						lowerCamelCase_				:			str=99      ,						lowerCamelCase_				:			int=32      ,						lowerCamelCase_				:			Union[str, Any]=5      ,						lowerCamelCase_				:			str=4      ,						lowerCamelCase_				:			str=37      ,						lowerCamelCase_				:			Optional[Any]="gelu"      ,						lowerCamelCase_				:			Tuple=0.1      ,						lowerCamelCase_				:			Optional[int]=0.1      ,						lowerCamelCase_				:			Any=5_12      ,						lowerCamelCase_				:			List[Any]=16      ,						lowerCamelCase_				:			List[Any]=2      ,						lowerCamelCase_				:			Any=0.02      ,						lowerCamelCase_				:			Any=3      ,						lowerCamelCase_				:			Optional[Any]=4      ,						lowerCamelCase_				:			Dict=None      ,						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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

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

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

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

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

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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Optional[int]      ,						lowerCamelCase_				:			Any      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			Optional[int]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]      ,						lowerCamelCase_				:			Optional[int]      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			Optional[Any]      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			BioGptForCausalLM(config=lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.eval()
							SCREAMING_SNAKE_CASE :				Any		=			model(lowerCamelCase_      ,						attention_mask=lowerCamelCase_      ,						token_type_ids=lowerCamelCase_      ,						labels=lowerCamelCase_						)
							self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)						)


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			List[Any]      ,						*lowerCamelCase_				:			int						):

							'''simple docstring'''



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

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

							# first forward pass
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						attention_mask=lowerCamelCase_						).to_tuple()

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

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

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

							# get two different outputs
							SCREAMING_SNAKE_CASE :				Optional[int]		=			model(lowerCamelCase_      ,						attention_mask=lowerCamelCase_						)["""last_hidden_state"""]
							SCREAMING_SNAKE_CASE :				Dict		=			model(lowerCamelCase_      ,						past_key_values=lowerCamelCase_      ,						attention_mask=lowerCamelCase_						)["""last_hidden_state"""]

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

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


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			Optional[int]      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Optional[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			int      ,						*lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



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

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

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

							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			outputs.to_tuple()

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

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

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			model(lowerCamelCase_      ,						attention_mask=lowerCamelCase_						)["""last_hidden_state"""]
							SCREAMING_SNAKE_CASE :				List[Any]		=			model(lowerCamelCase_      ,						attention_mask=lowerCamelCase_      ,						past_key_values=lowerCamelCase_						)[
							    """last_hidden_state"""
							]

							# select random slice
							SCREAMING_SNAKE_CASE :				Tuple		=			ids_tensor((1,)      ,						output_from_past.shape[-1]						).item()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			output_from_no_past[:, -3:, random_slice_idx].detach()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

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


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Optional[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			Tuple      ,						lowerCamelCase_				:			Optional[Any]      ,						lowerCamelCase_				:			List[str]      ,						*lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Optional[int]=False						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			BioGptForCausalLM(lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							if gradient_checkpointing:
									model.gradient_checkpointing_enable()

							SCREAMING_SNAKE_CASE :				Dict		=			model(lowerCamelCase_      ,						labels=lowerCamelCase_						)
							self.parent.assertEqual(result.loss.shape      ,						()						)
							self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)						)
							result.loss.backward()


					def lowerCamelCase_			(						self				:			List[Any]      ,						lowerCamelCase_				:			Dict      ,						*lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			BioGptModel(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			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 lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Optional[Any]      ,						lowerCamelCase_				:			Any      ,						lowerCamelCase_				:			Optional[int]      ,						lowerCamelCase_				:			Union[str, Any]      ,						*lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.num_labels
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			BioGptForTokenClassification(lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.eval()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			model(lowerCamelCase_      ,						attention_mask=lowerCamelCase_      ,						token_type_ids=lowerCamelCase_						)
							self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.num_labels)						)



					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



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







@require_torch
class      UpperCamelCase__					(					lowercase_						,					lowercase_						,					lowercase_						,					unittest.TestCase			):




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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.config_tester.run_common_tests()


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase_      ,						gradient_checkpointing=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase_						)


					@slow
					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
							model.to(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)

							SCREAMING_SNAKE_CASE :				int		=			"""left"""

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

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

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

							SCREAMING_SNAKE_CASE :				Optional[int]		=			model.generate(
							    input_ids=lowerCamelCase_      ,						attention_mask=inputs["""attention_mask"""].to(lowerCamelCase_						)      ,						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer(sentences[0]      ,						return_tensors="""pt"""						).input_ids.to(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			model.generate(input_ids=lowerCamelCase_						)

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

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

							SCREAMING_SNAKE_CASE :				List[str]		=			[
							    """Hello, my dog is a little bit bigger than a little bit.""",
							    """Today, I have a good idea of how to use the information""",
							]
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						[non_padded_sentence, padded_sentence]						)


					@slow
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



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



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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







@require_torch
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					@slow
					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			4_23_84

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

							SCREAMING_SNAKE_CASE :				int		=			torch.tensor(
							    [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]						)

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



					@slow
					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)
							SCREAMING_SNAKE_CASE :				str		=			BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
							model.to(lowerCamelCase_						)

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

							SCREAMING_SNAKE_CASE :				Optional[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(lowerCamelCase_      ,						lowerCamelCase_						)










'''simple docstring'''




def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			1  # To kept the Calculated Value
		# Since C(n, k) = C(n, n-k)
		if k > (n - k):
				SCREAMING_SNAKE_CASE :				Dict		=			n - k
		# Calculate C(n,k)
		for i in range(lowerCamelCase_	):
				result *= n - i
				result //= i + 1
		return result



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return binomial_coefficient(2 * node_count			,			lowerCamelCase_	) // (node_count + 1)



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if n < 0:
				raise ValueError("""factorial() not defined for negative values"""	)
		SCREAMING_SNAKE_CASE :				Dict		=			1
		for i in range(1			,			n + 1	):
				result *= i
		return result



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return catalan_number(lowerCamelCase_	) * factorial(lowerCamelCase_	)


if __name__ == "__main__":
							__UpperCAmelCase            =       int(input("""Enter the number of nodes: """).strip() or 0)
							if node_count <= 0:
														raise ValueError("""We need some nodes to work with.""")
							print(
							    f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
							    f'''binary trees and {catalan_number(node_count)} binary search trees.'''
							)



