



import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				GPTSanJapaneseTokenizer
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				{"""do_clean_text""": False, """add_prefix_space""": False}



       def  A_   (     self):

             """simple docstring"""





             super().setUp()

             # fmt: off
             UpperCAmelCase_  :	Optional[Any]				      =		["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>", "<|bagoftoken|>", "<|endoftext|>"]
             # fmt: on
             UpperCAmelCase_  :	Tuple				      =		{"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
             UpperCAmelCase_  :	Tuple				      =		{"unk_token": "<unk>"}

             UpperCAmelCase_  :	Union[str, Any]				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"])
             UpperCAmelCase_  :	Tuple				      =		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(lowercase))



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		self.get_input_output_texts(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.decode(lowercase     ,clean_up_tokenization_spaces=lowercase)
             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_  :	Dict				      =		self.get_tokenizer()

             # Testing tokenization
             UpperCAmelCase_  :	int				      =		"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_  :	Optional[Any]				      =		tokenizer.tokenize(lowercase)
             self.assertListEqual(lowercase     ,lowercase)

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

             # Testing conversion to ids with special tokens
             UpperCAmelCase_  :	Optional[Any]				      =		tokens + [tokenizer.unk_token]
             UpperCAmelCase_  :	int				      =		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
             UpperCAmelCase_  :	str				      =		tokenizer.convert_tokens_to_ids(lowercase)
             self.assertListEqual(lowercase     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.get_tokenizer()

             # Testing tokenization
             UpperCAmelCase_  :	Optional[Any]				      =		"こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
             UpperCAmelCase_  :	List[Any]				      =		"こんにちは、、、、世界。こんばんは、、、、世界。"
             UpperCAmelCase_  :	List[str]				      =		tokenizer.encode(lowercase)
             UpperCAmelCase_  :	List[str]				      =		tokenizer.decode(lowercase)
             self.assertEqual(lowercase     ,lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

             # Testing tokenization
             UpperCAmelCase_  :	Optional[int]				      =		"こんにちは、世界。"
             UpperCAmelCase_  :	Any				      =		"こんばんは、㔺界。😀"
             UpperCAmelCase_  :	str				      =		"こんにちは、世界。こんばんは、世界。😀"
             UpperCAmelCase_  :	Tuple				      =		tokenizer.encode(prefix_text + input_text)
             UpperCAmelCase_  :	Any				      =		tokenizer.encode(""     ,prefix_text=prefix_text + input_text)
             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer.encode(lowercase     ,prefix_text=lowercase)
             UpperCAmelCase_  :	Tuple				      =		tokenizer.decode(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		tokenizer.decode(lowercase)
             UpperCAmelCase_  :	Tuple				      =		tokenizer.decode(lowercase)
             self.assertEqual(lowercase     ,lowercase)
             self.assertEqual(lowercase     ,lowercase)
             self.assertEqual(lowercase     ,lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

             # Testing tokenization
             UpperCAmelCase_  :	Optional[int]				      =		"こんにちは、世界。"
             UpperCAmelCase_  :	List[Any]				      =		"こんばんは、㔺界。😀"

             UpperCAmelCase_  :	int				      =		len(tokenizer.encode(lowercase)) - 2
             UpperCAmelCase_  :	str				      =		len(tokenizer.encode(lowercase)) - 2

             UpperCAmelCase_  :	Any				      =		[1] + [0] * (len_prefix + len_text + 1)
             UpperCAmelCase_  :	Union[str, Any]				      =		[1] * (len_prefix + len_text + 1) + [0]
             UpperCAmelCase_  :	List[Any]				      =		[1] + [1] * (len_prefix) + [0] * (len_text + 1)

             UpperCAmelCase_  :	Any				      =		tokenizer(prefix_text + input_text).token_type_ids
             UpperCAmelCase_  :	Optional[Any]				      =		tokenizer(""     ,prefix_text=prefix_text + input_text).token_type_ids
             UpperCAmelCase_  :	Dict				      =		tokenizer(lowercase     ,prefix_text=lowercase).token_type_ids
             self.assertListEqual(lowercase     ,lowercase)
             self.assertListEqual(lowercase     ,lowercase)
             self.assertListEqual(lowercase     ,lowercase)



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

             UpperCAmelCase_  :	int				      =		tokenizer.encode("あンいワ")
             UpperCAmelCase_  :	int				      =		tokenizer.encode(""     ,prefix_text="あンいワ")
             UpperCAmelCase_  :	List[str]				      =		tokenizer.encode("いワ"     ,prefix_text="あン")

             self.assertEqual(tokenizer.decode(lowercase)     ,tokenizer.decode(lowercase))
             self.assertEqual(tokenizer.decode(lowercase)     ,tokenizer.decode(lowercase))
             self.assertNotEqual(lowercase     ,lowercase)
             self.assertNotEqual(lowercase     ,lowercase)
             self.assertEqual(x_token_a[1]     ,x_token_a[-1])  # SEG token
             self.assertEqual(x_token_a[1]     ,x_token_a[3])  # SEG token



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")

             UpperCAmelCase_  :	Union[str, Any]				      =		[["武田信玄", "は、"], ["織田信長", "の配下の、"]]
             UpperCAmelCase_  :	Any				      =		tokenizer(lowercase     ,padding=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer.batch_encode_plus(lowercase     ,padding=lowercase)

             # fmt: off
             UpperCAmelCase_  :	Tuple				      =		[[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
             UpperCAmelCase_  :	str				      =		[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
             UpperCAmelCase_  :	Optional[int]				      =		[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
             # fmt: on
             self.assertListEqual(x_token.input_ids     ,lowercase)
             self.assertListEqual(x_token.token_type_ids     ,lowercase)
             self.assertListEqual(x_token.attention_mask     ,lowercase)
             self.assertListEqual(x_token_a.input_ids     ,lowercase)
             self.assertListEqual(x_token_a.token_type_ids     ,lowercase)
             self.assertListEqual(x_token_a.attention_mask     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             pass





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



       def __call__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("sampling_rate"     ,lowercase)

             if text is None and audios is None:
                   raise ValueError("You have to specify either text or audios. Both cannot be none.")

             if text is not None:
                   UpperCAmelCase_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.batch_decode(*lowercase     ,**lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.decode(*lowercase     ,**lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
