
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 			UpperCamelCase_			(						A__	,     unittest.TestCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							GPTNeoXJapaneseTokenizer
							UpperCamelCase_					   =							False
							UpperCamelCase_					   =							{'''do_clean_text''': False, '''add_prefix_space''': False}
							def        lowerCAmelCase__ (				self) ->	List[str]:
										super().setUp()

										UpperCamelCase__    :		str     =				[
										    "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__    :		int     =				{"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
										UpperCamelCase__    :		Dict     =				{"unk_token": "<unk>"}

										UpperCamelCase__    :		Dict     =				os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES['vocab_file'])
										UpperCamelCase__    :		List[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(UpperCamelCase))
							def        lowerCAmelCase__ (				self     ,						**UpperCamelCase) ->	Optional[int]:
										kwargs.update(self.special_tokens_map)
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[Any]:
										UpperCamelCase__    :		List[str]     =				"こんにちは、世界。 \nこんばんは、㔺界。😀"
										UpperCamelCase__    :		Tuple     =				"こんにちは、世界。 \nこんばんは、世界。😀"
										return input_text, output_text
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		str     =				self.get_input_output_texts(UpperCamelCase)
										UpperCamelCase__    :		Any     =				tokenizer.encode(UpperCamelCase     ,						add_special_tokens=UpperCamelCase)
										UpperCamelCase__    :		str     =				tokenizer.decode(UpperCamelCase     ,						clean_up_tokenization_spaces=UpperCamelCase)
										return text, ids
							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										pass  # TODO add if relevant
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										pass  # TODO add if relevant
							def        lowerCAmelCase__ (				self) ->	Dict:
										pass  # TODO add if relevant
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		Dict     =				self.get_tokenizer()

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

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

										# Testing conversion to ids with special tokens
										UpperCamelCase__    :		Any     =				tokens + [tokenizer.unk_token]
										UpperCamelCase__    :		Union[str, Any]     =				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
										UpperCamelCase__    :		List[str]     =				tokenizer.convert_tokens_to_ids(UpperCamelCase)
										self.assertListEqual(UpperCamelCase     ,						UpperCamelCase)
							@slow
							def        lowerCAmelCase__ (				self) ->	Dict:
										UpperCamelCase__    :		List[Any]     =				self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b')

										UpperCamelCase__    :		Dict     =				tokenizer.encode('ありがとう。'     ,						add_special_tokens=UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				tokenizer.encode('どういたしまして。'     ,						add_special_tokens=UpperCamelCase)

										UpperCamelCase__    :		int     =				tokenizer.build_inputs_with_special_tokens(UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				tokenizer.build_inputs_with_special_tokens(UpperCamelCase     ,						UpperCamelCase)

										assert encoded_sentence == ids_a
										assert encoded_pair == ids_a + ids_a
							def        lowerCAmelCase__ (				self) ->	int:
										pass






							def        lowerCAmelCase__ (				self) ->	Any:
										pass





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__				:  str       					=				{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__				:  List[Any]       					=				[
							    '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MobileViTV2ForImageClassification''',
							    '''MobileViTV2ForSemanticSegmentation''',
							    '''MobileViTV2Model''',
							    '''MobileViTV2PreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileViTVaConfig,
							    MobileViTVaOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilevitva import (
														    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileViTVaForImageClassification,
														    MobileViTVaForSemanticSegmentation,
														    MobileViTVaModel,
														    MobileViTVaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__				:  List[str]       					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



