






"""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     A_				(   snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					GPTNeoXJapaneseTokenizer
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					{'''do_clean_text''': False, '''add_prefix_space''': False}







				def      _snake_case       (				self				:    Tuple						)					->       Optional[Any]:
										super().setUp()

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

										__magic_name__      =  os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES["vocab_file"]						)
										__magic_name__      =  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(__lowerCamelCase						)						)







				def      _snake_case       (				self				:    int    ,     **__lowerCamelCase				:    Union[str, Any]						)					->       Union[str, Any]:
										kwargs.update(self.special_tokens_map						)
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Any						)					->       Optional[Any]:
										__magic_name__      =  "こんにちは、世界。 \nこんばんは、㔺界。😀"
										__magic_name__      =  "こんにちは、世界。 \nこんばんは、世界。😀"
										return input_text, output_text







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    Dict						)					->       Tuple:
										__magic_name__   ,     __magic_name__      =  self.get_input_output_texts(__lowerCamelCase						)
										__magic_name__      =  tokenizer.encode(__lowerCamelCase    ,     add_special_tokens=__lowerCamelCase						)
										__magic_name__      =  tokenizer.decode(__lowerCamelCase    ,     clean_up_tokenization_spaces=__lowerCamelCase						)
										return text, ids







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[Any]:
										pass  # TODO add if relevant







				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										pass  # TODO add if relevant







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







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.get_tokenizer()

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

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

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







				@slow
				def      _snake_case       (				self				:    Any						)					->       List[str]:
										__magic_name__      =  self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"						)

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

										__magic_name__      =  tokenizer.build_inputs_with_special_tokens(__lowerCamelCase						)
										__magic_name__      =  tokenizer.build_inputs_with_special_tokens(__lowerCamelCase    ,     __lowerCamelCase						)

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







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







				def      _snake_case       (				self				:    Tuple						)					->       List[str]:
										# tokenizer has no padding token
										pass











"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


lowercase           =							logging.get_logger(__name__)

class     A_				(   snake_case_	):







				def __init__(				self				:    str    ,     *__lowerCamelCase				:    List[Any]    ,     **__lowerCamelCase				:    List[str]						)					->       None:
										warnings.warn(
										    "The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConditionalDetrImageProcessor instead."    ,     __lowerCamelCase    ,     )
										super().__init__(*__lowerCamelCase    ,     **__lowerCamelCase						)



