



import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	lowerCamelCase__ (		_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= GPTNeoXJapaneseTokenizer
						_lowerCAmelCase								= False
						_lowerCAmelCase								= {'''do_clean_text''': False, '''add_prefix_space''': False}


						def   _lowerCamelCase			( self						:  Tuple			):
													super().setUp()

													a__:       int											=[
													    "こん",
													    "こんに",
													    "にちは",
													    "ばんは",
													    "世界,㔺界",
													    "、",
													    "。",
													    "<BR>",
													    "<SP>",
													    "<TAB>",
													    "<URL>",
													    "<EMAIL>",
													    "<TEL>",
													    "<DATE>",
													    "<PRICE>",
													    "<BLOCK>",
													    "<KIGOU>",
													    "<U2000U2BFF>",
													    "<|emoji1|>",
													    "<unk>",
													    "<|startoftext|>",
													    "<|endoftext|>",
													]
													a__:       Optional[int]											={"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
													a__:       List[Any]											={"unk_token": "<unk>"}

													a__:       int											=os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["vocab_file"]			)
													a__:       Union[str, Any]											=os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["emoji_file"]			)
													with open(self.vocab_file      ,       "w"      ,       encoding="utf-8"			) as vocab_writer:
																				vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)
													with open(self.emoji_file      ,       "w"			) as emoji_writer:
																				emoji_writer.write(json.dumps(_a			)			)


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       **_a						:  int			):
													kwargs.update(self.special_tokens_map			)
													return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,       **_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Union[str, Any]			):
													a__:       Tuple											="こんにちは、世界。 \nこんばんは、㔺界。😀"
													a__:       str											="こんにちは、世界。 \nこんばんは、世界。😀"
													return input_text, output_text


						def   _lowerCamelCase			( self						:  int      ,       _a						:  Any			):
													a__							,			a__:       List[Any]											=self.get_input_output_texts(_a			)
													a__:       Any											=tokenizer.encode(_a      ,       add_special_tokens=_a			)
													a__:       Dict											=tokenizer.decode(_a      ,       clean_up_tokenization_spaces=_a			)
													return text, ids


						def   _lowerCamelCase			( self						:  Any			):
													pass  # TODO add if relevant


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													pass  # TODO add if relevant


						def   _lowerCamelCase			( self						:  List[Any]			):
													pass  # TODO add if relevant


						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       int											=self.get_tokenizer()

													# Testing tokenization
													a__:       str											="こんにちは、世界。　こんばんは、㔺界。"
													a__:       Optional[Any]											=["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
													a__:       Tuple											=tokenizer.tokenize(_a			)
													self.assertListEqual(_a      ,       _a			)

													# Testing conversion to ids without special tokens
													a__:       Optional[Any]											=[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
													a__:       Union[str, Any]											=tokenizer.convert_tokens_to_ids(_a			)
													self.assertListEqual(_a      ,       _a			)

													# Testing conversion to ids with special tokens
													a__:       Union[str, Any]											=tokens + [tokenizer.unk_token]
													a__:       Any											=[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
													a__:       str											=tokenizer.convert_tokens_to_ids(_a			)
													self.assertListEqual(_a      ,       _a			)


						@slow
						def   _lowerCamelCase			( self						:  Tuple			):
													a__:       Union[str, Any]											=self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"			)

													a__:       Any											=tokenizer.encode("ありがとう。"      ,       add_special_tokens=_a			)
													a__:       Union[str, Any]											=tokenizer.encode("どういたしまして。"      ,       add_special_tokens=_a			)

													a__:       Union[str, Any]											=tokenizer.build_inputs_with_special_tokens(_a			)
													a__:       Tuple											=tokenizer.build_inputs_with_special_tokens(_a      ,       _a			)

													assert encoded_sentence == ids_a
													assert encoded_pair == ids_a + ids_a


						def   _lowerCamelCase			( self						:  Any			):
													# Intentionally convert some words to accommodate character fluctuations unique to Japanese
													pass







						def   _lowerCamelCase			( self						:  List[str]			):
													# tokenizer has no padding token
													pass










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer


		class 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

														a__:       Optional[int]											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





