



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    _lowercase (a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    GPTNeoXJapaneseTokenizer
				lowercase__     =    False
				lowercase__     =    {"""do_clean_text""": False, """add_prefix_space""": False}

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									super().setUp()

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

									UpperCamelCase_			= os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES["vocab_file"]     )
									UpperCamelCase_			= 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(snake_case__     )     )

				def    _lowerCamelCase       (			self  ,			**snake_case__     ):

									'''simple docstring'''


									kwargs.update(self.special_tokens_map     )
									return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,			**snake_case__     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= "こんにちは、世界。 \nこんばんは、㔺界。😀"
									UpperCamelCase_			= "こんにちは、世界。 \nこんばんは、世界。😀"
									return input_text, output_text

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.get_input_output_texts(snake_case__     )
									UpperCamelCase_			= tokenizer.encode(snake_case__  ,			add_special_tokens=snake_case__     )
									UpperCamelCase_			= tokenizer.decode(snake_case__  ,			clean_up_tokenization_spaces=snake_case__     )
									return text, ids

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass  # TODO add if relevant

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass  # TODO add if relevant

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass  # TODO add if relevant

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.get_tokenizer()

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

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

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

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"     )

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

									UpperCamelCase_			= tokenizer.build_inputs_with_special_tokens(snake_case__     )
									UpperCamelCase_			= tokenizer.build_inputs_with_special_tokens(snake_case__  ,			snake_case__     )

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
