
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 (	snake_case__ ,				unittest.TestCase	):
				_lowercase      		=GPTNeoXJapaneseTokenizer
				_lowercase      		=False
				_lowercase      		={'''do_clean_text''': False, '''add_prefix_space''': False}




				def 					__a						( self					)    ->				int:
							super().setUp()

							lowerCAmelCase_      =     [
							    '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|>',
							]
							lowerCAmelCase_      =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
							lowerCAmelCase_      =     {'unk_token': '<unk>'}

							lowerCAmelCase_      =     os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES["vocab_file"]					)
							lowerCAmelCase_      =     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 					__a						( self    ,    **_UpperCamelCase					)    ->				str:
							kwargs.update(self.special_tokens_map					)
							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname    ,    **_A					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							lowerCAmelCase_      =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
							lowerCAmelCase_      =     'こんにちは、世界。 \nこんばんは、世界。😀'
							return input_text, output_text




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     self.get_input_output_texts(_A					)
							lowerCAmelCase_      =     tokenizer.encode(_A    ,    add_special_tokens=_A					)
							lowerCAmelCase_      =     tokenizer.decode(_A    ,    clean_up_tokenization_spaces=_A					)
							return text, ids




				def 					__a						( self					)    ->				Any:
							pass  # TODO add if relevant




				def 					__a						( self					)    ->				int:
							pass  # TODO add if relevant




				def 					__a						( self					)    ->				str:
							pass  # TODO add if relevant




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     self.get_tokenizer()

							# Testing tokenization
							lowerCAmelCase_      =     'こんにちは、世界。　こんばんは、㔺界。'
							lowerCAmelCase_      =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
							lowerCAmelCase_      =     tokenizer.tokenize(_A					)
							self.assertListEqual(_A    ,    _A					)

							# Testing conversion to ids without special tokens
							lowerCAmelCase_      =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							lowerCAmelCase_      =     tokenizer.convert_tokens_to_ids(_A					)
							self.assertListEqual(_A    ,    _A					)

							# Testing conversion to ids with special tokens
							lowerCAmelCase_      =     tokens + [tokenizer.unk_token]
							lowerCAmelCase_      =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
							lowerCAmelCase_      =     tokenizer.convert_tokens_to_ids(_A					)
							self.assertListEqual(_A    ,    _A					)




				@slow
				def 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"					)

							lowerCAmelCase_      =     tokenizer.encode("ありがとう。"    ,    add_special_tokens=_A					)
							lowerCAmelCase_      =     tokenizer.encode("どういたしまして。"    ,    add_special_tokens=_A					)

							lowerCAmelCase_      =     tokenizer.build_inputs_with_special_tokens(_A					)
							lowerCAmelCase_      =     tokenizer.build_inputs_with_special_tokens(_A    ,    _A					)

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




				def 					__a						( self					)    ->				Dict:
							# Intentionally convert some words to accommodate character fluctuations unique to Japanese
							pass




				def 					__a						( self					)    ->				Optional[Any]:
							# tokenizer has no padding token
							pass





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



