


"""simple docstring"""


import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case					(     _UpperCamelCase   , unittest.TestCase):
		__UpperCamelCase							=	MPNetTokenizer
		__UpperCamelCase							=	MPNetTokenizerFast
		__UpperCamelCase							=	True
		__UpperCamelCase							=	True







		def        a_       (    self					:				Optional[Any]						)				->  Any:





						'''simple docstring'''







						super().setUp()

						_A         =	[
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "want",
						    "##want",
						    "##ed",
						    "wa",
						    "un",
						    "runn",
						    "##ing",
						    ",",
						    "low",
						    "lowest",
						]
						_A         =	os.path.join(self.tmpdirname			,      VOCAB_FILES_NAMES["vocab_file"]						)
						with open(self.vocab_file			,      "w"			,      encoding="utf-8"						) as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)







		def        a_       (    self					:				List[str]			,      a__					:				Any						)				->  int:





						'''simple docstring'''







						_A         =	"UNwant\u00E9d,running"
						_A         =	"unwanted, running"
						return input_text, output_text







		def        a_       (    self					:				Tuple						)				->  Dict:





						'''simple docstring'''







						_A         =	self.tokenizer_class(self.vocab_file						)

						_A         =	tokenizer.tokenize("UNwant\u00E9d,running"						)
						self.assertListEqual(a__			,      ["un", "##want", "##ed", ",", "runn", "##ing"]						)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)			,      [9, 6, 7, 12, 10, 11]						)







		@slow
		def        a_       (    self					:				Tuple						)				->  Any:





						'''simple docstring'''







						_A         =	self.tokenizer_class.from_pretrained("microsoft/mpnet-base"						)

						_A         =	tokenizer.encode("sequence builders"			,      add_special_tokens=a__						)
						_A         =	tokenizer.encode("multi-sequence build"			,      add_special_tokens=a__						)

						_A         =	tokenizer.build_inputs_with_special_tokens(a__						)
						_A         =	tokenizer.build_inputs_with_special_tokens(a__			,      a__						)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]



"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	'AutoFeatureExtractor'
		__UpperCamelCase							=	'Speech2Text2Tokenizer'







		def __init__(    self					:				int			,      a__					:				Tuple			,      a__					:				Optional[Any]						)				->  str:





						'''simple docstring'''







						super().__init__(a__			,      a__						)
						_A         =	self.feature_extractor
						_A         =	False







		def __call__(    self					:				Optional[int]			,      *a__					:				List[str]			,      **a__					:				Any						)				->  List[Any]:





						'''simple docstring'''







						if self._in_target_context_manager:
										return self.current_processor(*a__			,      **a__						)

						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"			,      a__						)
						_A         =	kwargs.pop("sampling_rate"			,      a__						)
						_A         =	kwargs.pop("text"			,      a__						)
						if len(a__						) > 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(a__			,      *a__			,      sampling_rate=a__			,      **a__						)
						if text is not None:
										_A         =	self.tokenizer(a__			,      **a__						)

						if text is None:
										return inputs
						elif audio is None:
										return encodings
						else:
										_A         =	encodings["input_ids"]
										return inputs







		def        a_       (    self					:				List[Any]			,      *a__					:				Tuple			,      **a__					:				List[Any]						)				->  List[Any]:





						'''simple docstring'''







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







		def        a_       (    self					:				Any			,      *a__					:				Dict			,      **a__					:				Tuple						)				->  Union[str, Any]:





						'''simple docstring'''







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







		@contextmanager
		def        a_       (    self					:				Optional[int]						)				->  Optional[int]:





						'''simple docstring'''







						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