





"""simple docstring"""


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__ (      __SCREAMING_SNAKE_CASE				,    unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							List[str]								=     GPTNeoXJapaneseTokenizer
			__lowerCAmelCase    :							Union[str, Any]								=     False
			__lowerCAmelCase    :							Tuple								=     {"""do_clean_text""": False, """add_prefix_space""": False}







			def      _a			(			self						):






									'''simple docstring'''







									super().setUp()

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

									UpperCamelCase							:		str							      = os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["""vocab_file"""]						)
									UpperCamelCase							:		List[str]							      = 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	,      **_A						):






									'''simple docstring'''







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







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = """こんにちは、世界。 \nこんばんは、㔺界。😀"""
									UpperCamelCase							:		int							      = """こんにちは、世界。 \nこんばんは、世界。😀"""
									return input_text, output_text







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		str							      = self.get_input_output_texts(_A						)
									UpperCamelCase							:		str							      = tokenizer.encode(_A	,      add_special_tokens=_A						)
									UpperCamelCase							:		Dict							      = tokenizer.decode(_A	,      clean_up_tokenization_spaces=_A						)
									return text, ids







			def      _a			(			self						):






									'''simple docstring'''







									pass  # TODO add if relevant







			def      _a			(			self						):






									'''simple docstring'''







									pass  # TODO add if relevant







			def      _a			(			self						):






									'''simple docstring'''







									pass  # TODO add if relevant







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = self.get_tokenizer()

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

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

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







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[Any]							      = self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""						)

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

									UpperCamelCase							:		Any							      = tokenizer.build_inputs_with_special_tokens(_A						)
									UpperCamelCase							:		Dict							      = tokenizer.build_inputs_with_special_tokens(_A	,      _A						)

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







			def      _a			(			self						):






									'''simple docstring'''







									pass







			def      _a			(			self						):






									'''simple docstring'''







									pass










"""simple docstring"""


from typing import TYPE_CHECKING

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


__magic_name__					:				str	= {"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				Optional[int]	= ["""LevitFeatureExtractor"""]
		__magic_name__					:				Any	= ["""LevitImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				List[Any]	= [
		    """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LevitForImageClassification""",
		    """LevitForImageClassificationWithTeacher""",
		    """LevitModel""",
		    """LevitPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_levit import LevitFeatureExtractor
				from .image_processing_levit import LevitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_levit import (
				    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LevitForImageClassification,
				    LevitForImageClassificationWithTeacher,
				    LevitModel,
				    LevitPreTrainedModel,
				)
else:
		import sys

		__magic_name__					:				Tuple	= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



