




"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment





def 						UpperCAmelCase				(			)  ->     str:
			__lowerCamelCase					:	int	       =				torch.nn.Linear(2							,				4     )
			__lowerCamelCase					:	int	       =				torch.optim.AdamW(model.parameters()							,				lr=1.0     )
			__lowerCamelCase					:	str	       =				torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE_							,				max_lr=0.01							,				steps_per_epoch=2							,				epochs=1     )
			__lowerCamelCase					:	Optional[int]	       =				DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
			__lowerCamelCase					:	Tuple	       =				DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

			return model, optimizer, scheduler, train_dl, valid_dl





def 						UpperCAmelCase				(			A__:    str     )  ->     List[Any]:
			return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def 						UpperCAmelCase				(			A__:    List[Any]     )  ->     List[Any]:
			__lowerCamelCase					:	Optional[int]	       =				torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
			model.load_state_dict(SCREAMING_SNAKE_CASE_     )






class __lowercase(      lowercase__				):




					'''simple docstring'''


					@require_cuda
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				Accelerator()
								assert PartialState._shared_state["_cpu"] is False
								assert PartialState._shared_state["device"].type == "cuda"
								with self.assertRaises(__a     ):
											__lowerCamelCase					:	Optional[Any]	       =				Accelerator(cpu=__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				Accelerator()
								__lowerCamelCase					:	List[Any]	       =				GradientState()
								assert state.num_steps == 1
								__lowerCamelCase					:	Union[str, Any]	       =				4
								assert state.num_steps == 4

								assert state.sync_gradients is True
								__lowerCamelCase					:	Optional[Any]	       =				False
								assert state.sync_gradients is False
								GradientState._reset_state()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				Accelerator()
								__lowerCamelCase					:	Union[str, Any]	       =				create_components()

								(
								    __lowerCamelCase       
								)					:	Any	       =				accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )

								self.assertTrue(prepared_model in accelerator._models     )
								self.assertTrue(prepared_optimizer in accelerator._optimizers     )
								self.assertTrue(prepared_scheduler in accelerator._schedulers     )
								self.assertTrue(prepared_train_dl in accelerator._dataloaders     )
								self.assertTrue(prepared_valid_dl in accelerator._dataloaders     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				Accelerator()
								__lowerCamelCase					:	Dict	       =				create_components()
								accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )
								accelerator.free_memory()

								self.assertTrue(len(accelerator._models     ) == 0     )
								self.assertTrue(len(accelerator._optimizers     ) == 0     )
								self.assertTrue(len(accelerator._schedulers     ) == 0     )
								self.assertTrue(len(accelerator._dataloaders     ) == 0     )
					def     snake_case_				(    self     ):
								PartialState._reset_state()

								# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
								def noop(*__a						,	**__a     ):
											pass

								with patch('torch.cuda.set_device'						,	__a     ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'     ):
											__lowerCamelCase					:	Tuple	       =				Accelerator()
											self.assertEqual(str(accelerator.state.device     )						,	'cuda:64'     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				Accelerator()
								__lowerCamelCase					:	List[str]	       =				create_components()
								accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )

								__lowerCamelCase					:	List[Any]	       =				get_signature(__a     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(__a     )

											# make sure random weights don't match
											load_random_weights(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) > 1E-3     )

											# make sure loaded weights match
											accelerator.load_state(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) < 1E-3     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				Accelerator()
								__lowerCamelCase					:	List[str]	       =				create_components()
								accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )

								__lowerCamelCase					:	Tuple	       =				get_signature(__a     )

								# saving hook
								def save_config(__a						,	__a						,	__a     ):
											__lowerCamelCase					:	List[Any]	       =				{'class_name': models[0].__class__.__name__}

											with open(os.path.join(__a						,	'data.json'     )						,	'w'     ) as f:
														json.dump(__a						,	__a     )

        # loading hook
								def load_config(__a						,	__a     ):
											with open(os.path.join(__a						,	'data.json'     )						,	'r'     ) as f:
														__lowerCamelCase					:	Optional[int]	       =				json.load(__a     )

											__lowerCamelCase					:	List[Any]	       =				config['class_name']

								__lowerCamelCase					:	Union[str, Any]	       =				accelerator.register_save_state_pre_hook(__a     )
								__lowerCamelCase					:	str	       =				accelerator.register_load_state_pre_hook(__a     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(__a     )

											# make sure random weights don't match with hooks
											load_random_weights(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) > 1E-3     )

											# random class name to verify correct one is loaded
											__lowerCamelCase					:	Union[str, Any]	       =				'random'

											# make sure loaded weights match with hooks
											accelerator.load_state(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) < 1E-3     )

											# mode.class_name is loaded from config
											self.assertTrue(model.class_name == model.__class__.__name__     )

								# remove hooks
								save_hook.remove()
								load_hook.remove()

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(__a     )

											# make sure random weights don't match with hooks removed
											load_random_weights(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) > 1E-3     )

											# random class name to verify correct one is loaded
											__lowerCamelCase					:	Optional[int]	       =				'random'

											# make sure loaded weights match with hooks removed
											accelerator.load_state(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) < 1E-3     )

											# mode.class_name is NOT loaded from config
											self.assertTrue(model.class_name != model.__class__.__name__     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				Accelerator()
								__lowerCamelCase					:	Union[str, Any]	       =				create_components()
								__lowerCamelCase					:	Dict	       =				None

								# This should work
								__lowerCamelCase					:	Dict	       =				accelerator.prepare(
								    __a						,	__a						,	__a						,	__a						,	__a						,	__a     )
								self.assertTrue(dummy_obj is None     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				Accelerator()
								__lowerCamelCase					:	Optional[Any]	       =				create_components()
								__lowerCamelCase					:	Optional[Any]	       =				[1, 2, 3]

								# This should work
								__lowerCamelCase					:	Optional[int]	       =				accelerator.prepare(
								    __a						,	__a						,	__a						,	__a						,	__a						,	__a     )
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Dummy object should have `_is_accelerate_prepared` set to `True`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Model is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'						,	)
					@slow
					@require_bnb
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								__lowerCamelCase					:	Tuple	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	load_in_abit=__a						,	device_map={'': 0}						,	)
								__lowerCamelCase					:	Union[str, Any]	       =				Accelerator()

								# This should work
								__lowerCamelCase					:	Union[str, Any]	       =				accelerator.prepare(__a     )
					@slow
					@require_bnb
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								__lowerCamelCase					:	Any	       =				Accelerator()

								with init_empty_weights():
											__lowerCamelCase					:	List[Any]	       =				AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,	)
											model.tie_weights()
											__lowerCamelCase					:	Any	       =				infer_auto_device_map(__a     )
											__lowerCamelCase					:	Optional[int]	       =				'cpu'

								__lowerCamelCase					:	int	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	device_map=__a						,	load_in_abit=__a						,	llm_inta_enable_fpaa_cpu_offload=__a     )

								# This should not work and get value error
								with self.assertRaises(__a     ):
											__lowerCamelCase					:	Any	       =				accelerator.prepare(__a     )
					@slow
					@require_bnb
					@require_multi_gpu
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								__lowerCamelCase					:	Tuple	       =				{'distributed_type': DistributedType.MULTI_GPU}

								with init_empty_weights():
											__lowerCamelCase					:	Tuple	       =				AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,	)
											model.tie_weights()
											__lowerCamelCase					:	Union[str, Any]	       =				infer_auto_device_map(__a     )
											__lowerCamelCase					:	Optional[int]	       =				1

								__lowerCamelCase					:	Union[str, Any]	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	load_in_abit=__a						,	device_map=__a						,	)
								__lowerCamelCase					:	Any	       =				Accelerator()

								# This should not work and get value error
								with self.assertRaises(__a     ):
											__lowerCamelCase					:	Dict	       =				accelerator.prepare(__a     )

								PartialState._reset_state()
					@slow
					@require_bnb
					@require_multi_gpu
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								with init_empty_weights():
											__lowerCamelCase					:	Tuple	       =				AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,	)
											__lowerCamelCase					:	Optional[Any]	       =				infer_auto_device_map(__a     )
											__lowerCamelCase					:	Optional[int]	       =				1

								__lowerCamelCase					:	List[Any]	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	load_in_abit=__a						,	device_map=__a						,	)
								__lowerCamelCase					:	List[str]	       =				Accelerator()

								# This should work
								__lowerCamelCase					:	Any	       =				accelerator.prepare(__a     )





					@require_cuda
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				torch.nn.Linear(10						,	10     )
								__lowerCamelCase					:	Tuple	       =				torch.optim.SGD(model.parameters()						,	lr=0.01     )
								__lowerCamelCase					:	List[str]	       =				Accelerator(cpu=__a     )
								__lowerCamelCase					:	List[Any]	       =				accelerator.prepare(__a     )









"""simple docstring"""
import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=True						,	__a=True						,	__a=True						,	__a=99						,	__a=32						,	__a=5						,	__a=4						,	__a=4						,	__a="gelu"						,	__a=0.0						,	__a=0.1						,	__a=True						,	__a=512						,	__a=16						,	__a=2						,	__a=0.02						,	__a=3						,	__a=4						,	__a=None						,	):
								__lowerCamelCase					:	List[str]	       =				parent
								__lowerCamelCase					:	List[str]	       =				batch_size
								__lowerCamelCase					:	Optional[Any]	       =				seq_length
								__lowerCamelCase					:	List[Any]	       =				is_training
								__lowerCamelCase					:	str	       =				use_input_mask
								__lowerCamelCase					:	List[str]	       =				use_token_type_ids
								__lowerCamelCase					:	str	       =				use_labels
								__lowerCamelCase					:	Tuple	       =				vocab_size
								__lowerCamelCase					:	Any	       =				hidden_size
								__lowerCamelCase					:	List[str]	       =				num_hidden_layers
								__lowerCamelCase					:	Tuple	       =				num_attention_heads
								__lowerCamelCase					:	str	       =				intermediate_multiple_size
								__lowerCamelCase					:	Tuple	       =				hidden_act
								__lowerCamelCase					:	str	       =				hidden_dropout
								__lowerCamelCase					:	List[str]	       =				attention_dropout
								__lowerCamelCase					:	Tuple	       =				weight_tying
								__lowerCamelCase					:	int	       =				max_position_embeddings
								__lowerCamelCase					:	Tuple	       =				type_vocab_size
								__lowerCamelCase					:	Union[str, Any]	       =				type_sequence_label_size
								__lowerCamelCase					:	Optional[int]	       =				initializer_range
								__lowerCamelCase					:	Optional[int]	       =				num_labels
								__lowerCamelCase					:	str	       =				num_choices
								__lowerCamelCase					:	Tuple	       =				scope
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

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

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

								__lowerCamelCase					:	int	       =				self.get_config()

								return config, input_ids, input_mask, token_labels
					def     snake_case_				(    self     ):
								return GPTNeoXJapaneseConfig(
								    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_multiple_size=self.intermediate_multiple_size						,	hidden_act=self.hidden_act						,	hidden_dropout=self.hidden_dropout						,	attention_dropout=self.attention_dropout						,	weight_tying=self.weight_tying						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	is_decoder=__a						,	initializer_range=self.initializer_range						,	)
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				self.prepare_config_and_inputs()

								__lowerCamelCase					:	Tuple	       =				True

								return config, input_ids, input_mask, token_labels
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Any	       =				GPTNeoXJapaneseModel(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	int	       =				model(__a						,	attention_mask=__a     )
								__lowerCamelCase					:	Optional[Any]	       =				model(__a     )
								self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	List[str]	       =				True
								__lowerCamelCase					:	Tuple	       =				GPTNeoXJapaneseModel(__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Optional[Any]	       =				model(__a						,	attention_mask=__a     )
								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     ):
								__lowerCamelCase					:	Optional[Any]	       =				GPTNeoXJapaneseForCausalLM(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	List[str]	       =				model(__a						,	attention_mask=__a						,	labels=__a     )
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	List[Any]	       =				True
								__lowerCamelCase					:	str	       =				GPTNeoXJapaneseForCausalLM(config=__a     )
								model.to(__a     )
								model.eval()

								# first forward pass
								__lowerCamelCase					:	List[Any]	       =				model(__a						,	attention_mask=__a						,	use_cache=__a     )
								__lowerCamelCase					:	Any	       =				outputs.past_key_values

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

								# append to next input_ids and
								__lowerCamelCase					:	Any	       =				torch.cat([input_ids, next_tokens]						,	dim=-1     )
								__lowerCamelCase					:	int	       =				torch.cat([input_mask, next_mask]						,	dim=-1     )

								__lowerCamelCase					:	int	       =				model(__a						,	attention_mask=__a						,	output_hidden_states=__a     )
								__lowerCamelCase					:	Dict	       =				output_from_no_past['hidden_states'][0]
								__lowerCamelCase					:	Any	       =				model(
								    __a						,	attention_mask=__a						,	past_key_values=__a						,	output_hidden_states=__a						,	)['hidden_states'][0]

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





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



@require_torch
class __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Dict     		= (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
					__a						:						str     		= (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
					__a						:						Optional[int]     		= (
					    {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
					    if is_torch_available()
					    else {}
					)
					__a						:						Union[str, Any]     		= False
					__a						:						Optional[Any]     		= False
					__a						:						List[Any]     		= False
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				GPTNeoXJapaneseModelTester(self     )
								__lowerCamelCase					:	List[Any]	       =				ConfigTester(self						,	config_class=__a						,	hidden_size=37     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__a						,	__a						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(__a						,	__a						,	__a     )
					def     snake_case_				(    self     ):
								# This regression test was failing with PyTorch < 1.3
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs_for_decoder()

								__lowerCamelCase					:	Tuple	       =				None

								self.model_tester.create_and_check_model_as_decoder(__a						,	__a						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(__a						,	__a						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*__a     )





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				'abeja/gpt-neox-japanese-2.7b'

								__lowerCamelCase					:	Any	       =				['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

								__lowerCamelCase					:	Union[str, Any]	       =				[
								    'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
								    '100年後に必要とされる会社は、「人」が中心の会社です。',
								    'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
								    '国境の長いトンネルを抜けると、そこは雪国だった。',
								    '美味しい日本食といえば、やっぱりお寿司ですよね。',
								]

								__lowerCamelCase					:	Dict	       =				GPTNeoXJapaneseTokenizer.from_pretrained(__a     )
								__lowerCamelCase					:	str	       =				GPTNeoXJapaneseForCausalLM.from_pretrained(__a     )

								__lowerCamelCase					:	List[str]	       =				[]
								for prompt in prompts:
											__lowerCamelCase					:	List[Any]	       =				tokenizer(__a						,	return_tensors='pt'     ).input_ids
											__lowerCamelCase					:	List[Any]	       =				model.generate(__a						,	max_length=50     )
											__lowerCamelCase					:	str	       =				tokenizer.batch_decode(__a						,	skip_special_tokens=__a     )
											predicted_outputs += generated_string
								self.assertListEqual(__a						,	__a     )



