


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 _lowerCamelCase(						a_, unittest.TestCase   ):
       lowercase_ :	List[Any]     =			GPTSanJapaneseTokenizer
       lowercase_ :	str     =			False
       lowercase_ :	Any     =			{"""do_clean_text""": False, """add_prefix_space""": False}
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           super().setUp()

           # fmt: off
           _lowercase       :							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
           _lowercase       :							str      		=		{"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
           _lowercase       :							List[Any]      		=		{"unk_token": "<unk>"}

           _lowercase       :							str      		=		os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['vocab_file'])
           _lowercase       :							Dict      		=		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 			UpperCamelCase     (					self,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           kwargs.update(self.special_tokens_map)
           return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname,      **_a)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase       :							Dict      		=		"こんにちは、世界。 \nこんばんは、㔺界。😀"
           _lowercase       :							str      		=		"こんにちは、世界。 \nこんばんは、世界。😀"
           return input_text, output_text
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		self.get_input_output_texts(_a)
           _lowercase       :							List[str]      		=		tokenizer.encode(_a,      add_special_tokens=_a)
           _lowercase       :							Optional[Any]      		=		tokenizer.decode(_a,      clean_up_tokenization_spaces=_a)
           return text, ids
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           pass  # TODO add if relevant
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           pass  # TODO add if relevant
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.get_tokenizer()

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

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

           # Testing conversion to ids with special tokens
           _lowercase       :							List[Any]      		=		tokens + [tokenizer.unk_token]
           _lowercase       :							str      		=		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
           _lowercase       :							List[Any]      		=		tokenizer.convert_tokens_to_ids(_a)
           self.assertListEqual(_a,      _a)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		self.get_tokenizer()

           # Testing tokenization
           _lowercase       :							Union[str, Any]      		=		"こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
           _lowercase       :							str      		=		"こんにちは、、、、世界。こんばんは、、、、世界。"
           _lowercase       :							List[str]      		=		tokenizer.encode(_a)
           _lowercase       :							List[str]      		=		tokenizer.decode(_a)
           self.assertEqual(_a,      _a)
       @slow
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

           # Testing tokenization
           _lowercase       :							int      		=		"こんにちは、世界。"
           _lowercase       :							List[Any]      		=		"こんばんは、㔺界。😀"
           _lowercase       :							Dict      		=		"こんにちは、世界。こんばんは、世界。😀"
           _lowercase       :							str      		=		tokenizer.encode(prefix_text + input_text)
           _lowercase       :							Optional[Any]      		=		tokenizer.encode('',      prefix_text=prefix_text + input_text)
           _lowercase       :							Dict      		=		tokenizer.encode(_a,      prefix_text=_a)
           _lowercase       :							str      		=		tokenizer.decode(_a)
           _lowercase       :							Union[str, Any]      		=		tokenizer.decode(_a)
           _lowercase       :							int      		=		tokenizer.decode(_a)
           self.assertEqual(_a,      _a)
           self.assertEqual(_a,      _a)
           self.assertEqual(_a,      _a)
       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Dict      		=		self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

           # Testing tokenization
           _lowercase       :							Union[str, Any]      		=		"こんにちは、世界。"
           _lowercase       :							List[Any]      		=		"こんばんは、㔺界。😀"

           _lowercase       :							Tuple      		=		len(tokenizer.encode(_a)) - 2
           _lowercase       :							Dict      		=		len(tokenizer.encode(_a)) - 2

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

           _lowercase       :							Optional[Any]      		=		tokenizer(prefix_text + input_text).token_type_ids
           _lowercase       :							Optional[Any]      		=		tokenizer('',      prefix_text=prefix_text + input_text).token_type_ids
           _lowercase       :							Dict      		=		tokenizer(_a,      prefix_text=_a).token_type_ids
           self.assertListEqual(_a,      _a)
           self.assertListEqual(_a,      _a)
           self.assertListEqual(_a,      _a)
       @slow
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

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

           _lowercase       :							str      		=		tokenizer.encode('あンいワ')
           _lowercase       :							Any      		=		tokenizer.encode('',      prefix_text='あンいワ')
           _lowercase       :							str      		=		tokenizer.encode('いワ',      prefix_text='あン')

           self.assertEqual(tokenizer.decode(_a),      tokenizer.decode(_a))
           self.assertEqual(tokenizer.decode(_a),      tokenizer.decode(_a))
           self.assertNotEqual(_a,      _a)
           self.assertNotEqual(_a,      _a)
           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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

           _lowercase       :							Optional[Any]      		=		[["武田信玄", "は、"], ["織田信長", "の配下の、"]]
           _lowercase       :							Optional[int]      		=		tokenizer(_a,      padding=_a)
           _lowercase       :							List[str]      		=		tokenizer.batch_encode_plus(_a,      padding=_a)

           # fmt: off
           _lowercase       :							Any      		=		[[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
           _lowercase       :							List[str]      		=		[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
           _lowercase       :							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,      _a)
           self.assertListEqual(x_token.token_type_ids,      _a)
           self.assertListEqual(x_token.attention_mask,      _a)
           self.assertListEqual(x_token_a.input_ids,      _a)
           self.assertListEqual(x_token_a.token_type_ids,      _a)
           self.assertListEqual(x_token_a.attention_mask,      _a)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           pass




       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           pass




"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"laion/clap-htsat-unfused"
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
   def 			__magic_name__			(			self	,    **_a				):
          return RobertaTokenizer.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				List[Any]       		=							self.get_feature_extractor()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				int       		=							ClapProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Union[str, Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_feature_extractor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Dict       		=							ClapProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				int       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Dict       		=							floats_list((3, 1_000)				)

          lowercase    :				str       		=							feature_extractor(_a	,    return_tensors="np"				)
          lowercase    :				Dict       		=							processor(audios=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_feature_extractor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Optional[Any]       		=							"This is a test string"

          lowercase    :				Any       		=							processor(text=_a				)

          lowercase    :				List[Any]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_feature_extractor()
          lowercase    :				Any       		=							self.get_tokenizer()

          lowercase    :				Union[str, Any]       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Union[str, Any]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				str       		=							processor.batch_decode(_a				)
          lowercase    :				Optional[int]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Any       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          self.assertListEqual(
              processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )


