import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_    :		Any							=	get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")


class 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Optional[Any]        =      BartphoTokenizer
			A__    :       List[Any]        =      False
			A__    :       Any        =      True



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								super().setUp()

								__a		 =       ['▁This', '▁is', '▁a', '▁t', 'est']
								__a		 =       dict(zip(UpperCAmelCase				,						range(len(UpperCAmelCase   )   )   )   )
								__a		 =       {'unk_token': '<unk>'}

								__a		 =       os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['monolingual_vocab_file']   )
								with open(self.monolingual_vocab_file				,						'w'				,						encoding='utf-8'   ) as fp:
													for token in vocab_tokens:
																		fp.write(f'''{token} {vocab_tokens[token]}\n'''   )

								__a		 =       BartphoTokenizer(UpperCAmelCase				,						self.monolingual_vocab_file				,						**self.special_tokens_map   )
								tokenizer.save_pretrained(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Union[str, Any]:
								kwargs.update(self.special_tokens_map   )
								return BartphoTokenizer.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								__a		 =       'This is a là test'
								__a		 =       'This is a<unk><unk> test'
								return input_text, output_text







			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       BartphoTokenizer(UpperCAmelCase				,						self.monolingual_vocab_file				,						**self.special_tokens_map   )
								__a		 =       'This is a là test'
								__a		 =       '▁This ▁is ▁a ▁l à ▁t est'.split()
								__a		 =       tokenizer.tokenize(UpperCAmelCase   )
								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       tokens + [tokenizer.unk_token]
								__a		 =       [4, 5, 6, 3, 3, 7, 8, 3]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase   )				,						UpperCAmelCase   )





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								try:
													return super().from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								except OSError:
													warnings.warn(
													    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
													    ' include a `tokenizer_class` attribute is deprecated and will be '
													    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
													    ' attribute to either your `config.json` or `tokenizer_config.json` '
													    'file to suppress this warning: '				,						UpperCAmelCase				,						)

													__a		 =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
													__a		 =       WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )

													return cls(feature_extractor=UpperCAmelCase				,						tokenizer=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*UpperCAmelCase				,						**UpperCAmelCase   )

								if "raw_speech" in kwargs:
													warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
													__a		 =       kwargs.pop('raw_speech'   )
								else:
													__a		 =       kwargs.pop('audio'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('sampling_rate'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('text'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

								if audio is not None:
													__a		 =       self.feature_extractor(UpperCAmelCase				,						*UpperCAmelCase				,						sampling_rate=UpperCAmelCase				,						**UpperCAmelCase   )
								if text is not None:
													__a		 =       self.tokenizer(UpperCAmelCase				,						**UpperCAmelCase   )

								if text is None:
													return inputs
								elif audio is None:
													return encodings
								else:
													__a		 =       encodings['input_ids']
													return inputs



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor.pad(*UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       kwargs.pop('input_features'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('labels'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if input_features is not None:
													__a		 =       self.feature_extractor.pad(UpperCAmelCase				,						*UpperCAmelCase				,						**UpperCAmelCase   )
								if labels is not None:
													__a		 =       self.tokenizer.pad(UpperCAmelCase				,						**UpperCAmelCase   )

								if labels is None:
													return input_features
								elif input_features is None:
													return labels
								else:
													__a		 =       labels['input_ids']
													return input_features



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.tokenizer.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return self.tokenizer.decode(*UpperCAmelCase				,						**UpperCAmelCase   )







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'   )
								__a		 =       True
								__a		 =       self.tokenizer
								yield
								__a		 =       self.feature_extractor
								__a		 =       False




