




"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a_				:    Tuple										=     logging.getLogger(__name__)
a_				:    List[Any]										=     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_				:    Dict										=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class __lowercase:




					'''simple docstring'''


					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
					        )
					    }	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowercase__				)}	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'Override some existing default config settings when a model is trained from scratch. Example: '
					            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
					        )
					    }	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,	)
					__a						:						str     		= field(
					    default='main'	,	metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }	,	)
					def     snake_case_				(    self     ):
								if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
											raise ValueError(
											    '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'     )



@dataclass
class __lowercase:




					'''simple docstring'''


					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
					__a						:						Optional[str]     		= field(default=lowercase__	,	metadata={'help': 'The input training data file (a text file).'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={'help': 'Overwrite the cached training and evaluation sets'}				)
					__a						:						Optional[int]     		= field(
					    default=5	,	metadata={
					        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
					    }	,	)
					__a						:						Optional[int]     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'The maximum total input sequence length after tokenization. Sequences longer '
					            'than this will be truncated. Default to the max input length of the model.'
					        )
					    }	,	)
					__a						:						Optional[int]     		= field(
					    default=lowercase__	,	metadata={'help': 'The number of processes to use for the preprocessing.'}	,	)
					__a						:						float     		= field(
					    default=0.1_5	,	metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}				)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'Whether to pad all samples to `max_seq_length`. '
					            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
					        )
					    }	,	)
					def     snake_case_				(    self     ):
								if self.train_file is not None:
											__lowerCamelCase					:	Tuple	       =				self.train_file.split('.'     )[-1]
											assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
								if self.validation_file is not None:
											__lowerCamelCase					:	List[Any]	       =				self.validation_file.split('.'     )[-1]
											assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 						UpperCAmelCase				(			A__:    List[str]							,				A__:    Any     )  ->     Optional[int]:
			with open(A__							,				'r'							,				encoding='utf-8'     ) as f:
						__lowerCamelCase					:	Dict	       =				[json.loads(A__     ) for line in f.read().splitlines() if (len(A__     ) > 0 and not line.isspace())]
			assert len(A__     ) == len(A__     )

			__lowerCamelCase					:	List[Any]	       =				{c: dataset[c] for c in dataset.column_names}
			__lowerCamelCase					:	Any	       =				refs
			return Dataset.from_dict(A__     )





def 						UpperCAmelCase				(			)  ->     List[Any]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__lowerCamelCase					:	str	       =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
			if len(sys.argv     ) == 2 and sys.argv[1].endswith('.json'     ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__lowerCamelCase					:	Tuple	       =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						__lowerCamelCase					:	int	       =				parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			__lowerCamelCase					:	Optional[Any]	       =				None
			if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowerCamelCase					:	int	       =				get_last_checkpoint(training_args.output_dir     )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
									raise ValueError(
									    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    'Use --overwrite_output_dir to overcome.'     )
						elif last_checkpoint is not None:
									logger.info(
									    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'     )

    # Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,				datefmt='%m/%d/%Y %H:%M:%S'							,				handlers=[logging.StreamHandler(sys.stdout     )]							,				)
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

			# Log on each process the small summary:
			logger.warning(
			    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + f'''distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'''     )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank     ):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('Training/evaluation parameters %s'							,				A__     )

			# Set seed before initializing model.
			set_seed(training_args.seed     )

			# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
			# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
			# (the dataset will be downloaded automatically from the datasets Hub).
			#
			# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
			# 'text' is found. You can easily tweak this behavior (see below).
			#
			# In distributed training, the load_dataset function guarantee that only one local process can concurrently
			# download the dataset.
			if data_args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						__lowerCamelCase					:	Union[str, Any]	       =				load_dataset(data_args.dataset_name							,				data_args.dataset_config_name     )
						if "validation" not in datasets.keys():
									__lowerCamelCase					:	int	       =				load_dataset(
									    data_args.dataset_name							,				data_args.dataset_config_name							,				split=f'''train[:{data_args.validation_split_percentage}%]'''							,				)
									__lowerCamelCase					:	List[str]	       =				load_dataset(
									    data_args.dataset_name							,				data_args.dataset_config_name							,				split=f'''train[{data_args.validation_split_percentage}%:]'''							,				)
			else:
						__lowerCamelCase					:	List[str]	       =				{}
						if data_args.train_file is not None:
									__lowerCamelCase					:	int	       =				data_args.train_file
						if data_args.validation_file is not None:
									__lowerCamelCase					:	List[Any]	       =				data_args.validation_file
						__lowerCamelCase					:	Optional[Any]	       =				data_args.train_file.split('.'     )[-1]
						if extension == "txt":
									__lowerCamelCase					:	Dict	       =				'text'
						__lowerCamelCase					:	Tuple	       =				load_dataset(A__							,				data_files=A__     )
			# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
			# https://huggingface.co/docs/datasets/loading_datasets.html.

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			__lowerCamelCase					:	Union[str, Any]	       =				{
			    'cache_dir': model_args.cache_dir,
			    'revision': model_args.model_revision,
			    'use_auth_token': True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						__lowerCamelCase					:	Optional[Any]	       =				AutoConfig.from_pretrained(model_args.config_name							,				**A__     )
			elif model_args.model_name_or_path:
						__lowerCamelCase					:	Optional[int]	       =				AutoConfig.from_pretrained(model_args.model_name_or_path							,				**A__     )
			else:
						__lowerCamelCase					:	Optional[Any]	       =				CONFIG_MAPPING[model_args.model_type]()
						logger.warning('You are instantiating a new config instance from scratch.'     )
						if model_args.config_overrides is not None:
									logger.info(f'''Overriding config: {model_args.config_overrides}'''     )
									config.update_from_string(model_args.config_overrides     )
									logger.info(f'''New config: {config}'''     )

			__lowerCamelCase					:	str	       =				{
			    'cache_dir': model_args.cache_dir,
			    'use_fast': model_args.use_fast_tokenizer,
			    'revision': model_args.model_revision,
			    'use_auth_token': True if model_args.use_auth_token else None,
			}
			if model_args.tokenizer_name:
						__lowerCamelCase					:	Union[str, Any]	       =				AutoTokenizer.from_pretrained(model_args.tokenizer_name							,				**A__     )
			elif model_args.model_name_or_path:
						__lowerCamelCase					:	Tuple	       =				AutoTokenizer.from_pretrained(model_args.model_name_or_path							,				**A__     )
			else:
						raise ValueError(
						    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
						    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'     )

			if model_args.model_name_or_path:
						__lowerCamelCase					:	str	       =				AutoModelForMaskedLM.from_pretrained(
						    model_args.model_name_or_path							,				from_tf=bool('.ckpt' in model_args.model_name_or_path     )							,				config=A__							,				cache_dir=model_args.cache_dir							,				revision=model_args.model_revision							,				use_auth_token=True if model_args.use_auth_token else None							,				)
			else:
						logger.info('Training new model from scratch'     )
						__lowerCamelCase					:	int	       =				AutoModelForMaskedLM.from_config(A__     )

			model.resize_token_embeddings(len(A__     )     )

			# Preprocessing the datasets.
			# First we tokenize all the texts.
			if training_args.do_train:
						__lowerCamelCase					:	List[str]	       =				datasets['train'].column_names
			else:
						__lowerCamelCase					:	List[Any]	       =				datasets['validation'].column_names
			__lowerCamelCase					:	Optional[int]	       =				'text' if 'text' in column_names else column_names[0]

			__lowerCamelCase					:	Dict	       =				'max_length' if data_args.pad_to_max_length else False

			def tokenize_function(A__:    List[Any]     ):
						# Remove empty lines
						__lowerCamelCase					:	Any	       =				[line for line in examples['text'] if len(A__     ) > 0 and not line.isspace()]
						return tokenizer(examples['text']							,				padding=A__							,				truncation=A__							,				max_length=data_args.max_seq_length     )

			__lowerCamelCase					:	str	       =				datasets.map(
			    A__							,				batched=A__							,				num_proc=data_args.preprocessing_num_workers							,				remove_columns=[text_column_name]							,				load_from_cache_file=not data_args.overwrite_cache							,				)

			# Add the chinese references if provided
			if data_args.train_ref_file is not None:
						__lowerCamelCase					:	Optional[Any]	       =				add_chinese_references(tokenized_datasets['train']							,				data_args.train_ref_file     )
			if data_args.validation_ref_file is not None:
						__lowerCamelCase					:	Any	       =				add_chinese_references(
						    tokenized_datasets['validation']							,				data_args.validation_ref_file     )
			# If we have ref files, need to avoid it removed by trainer
			__lowerCamelCase					:	Union[str, Any]	       =				data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						__lowerCamelCase					:	Tuple	       =				False

			# Data collator
			# This one will take care of randomly masking the tokens.
			__lowerCamelCase					:	Tuple	       =				DataCollatorForWholeWordMask(tokenizer=A__							,				mlm_probability=data_args.mlm_probability     )

			# Initialize our Trainer
			__lowerCamelCase					:	Dict	       =				Trainer(
			    model=A__							,				args=A__							,				train_dataset=tokenized_datasets['train'] if training_args.do_train else None							,				eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None							,				tokenizer=A__							,				data_collator=A__							,				)

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									__lowerCamelCase					:	List[Any]	       =				last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     ):
									__lowerCamelCase					:	List[str]	       =				model_args.model_name_or_path
						else:
									__lowerCamelCase					:	Optional[int]	       =				None
						__lowerCamelCase					:	Tuple	       =				trainer.train(resume_from_checkpoint=A__     )
						trainer.save_model()  # Saves the tokenizer too for easy upload

						__lowerCamelCase					:	str	       =				os.path.join(training_args.output_dir							,				'train_results.txt'     )
						if trainer.is_world_process_zero():
									with open(A__							,				'w'     ) as writer:
												logger.info('***** Train results *****'     )
												for key, value in sorted(train_result.metrics.items()     ):
															logger.info(f'''  {key} = {value}'''     )
															writer.write(f'''{key} = {value}\n'''     )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir							,				'trainer_state.json'     )     )

    # Evaluation
			__lowerCamelCase					:	Optional[int]	       =				{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'     )

						__lowerCamelCase					:	int	       =				trainer.evaluate()

						__lowerCamelCase					:	List[Any]	       =				math.exp(eval_output['eval_loss']     )
						__lowerCamelCase					:	Any	       =				perplexity

						__lowerCamelCase					:	Union[str, Any]	       =				os.path.join(training_args.output_dir							,				'eval_results_mlm_wwm.txt'     )
						if trainer.is_world_process_zero():
									with open(A__							,				'w'     ) as writer:
												logger.info('***** Eval results *****'     )
												for key, value in sorted(results.items()     ):
															logger.info(f'''  {key} = {value}'''     )
															writer.write(f'''{key} = {value}\n'''     )

			return results





def 						UpperCAmelCase				(			A__:    List[Any]     )  ->     Tuple:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()









"""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     )



