

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class A						(     __lowerCamelCase					,							unittest.TestCase  ):
      UpperCamelCase__							:						int   =SpeechaTextaTokenizer
      UpperCamelCase__							:						Optional[int]   =False






      def  lowerCamelCase (      self		:  List[Any]				)      ->		List[Any]:
             """simple docstring"""




             super().setUp()

             _lowerCamelCase :	Tuple						='<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '				)
             _lowerCamelCase :	List[str]						=['he re</w> 123', 'here a 1456']
             _lowerCamelCase :	Union[str, Any]						=dict(zip(lowercase_    ,     range(len(lowercase_				)				)				)				)

             _lowerCamelCase :	int						={'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

             _lowerCamelCase :	Any						=tempfile.mkdtemp()
             _lowerCamelCase :	Union[str, Any]						=os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']				)
             _lowerCamelCase :	Optional[Any]						=os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['merges_file']				)

             with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'				) as fp:
                    fp.write(json.dumps(lowercase_				) + '\n'				)

             with open(self.merges_file    ,     'w'				) as fp:
                    fp.write('\n'.join(lowercase_				)				)






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=list(self.get_tokenizer().get_vocab().keys()				)

             self.assertEqual(vocab_keys[0]    ,     '<s>'				)
             self.assertEqual(vocab_keys[1]    ,     '<pad>'				)
             self.assertEqual(vocab_keys[-1]    ,     'vocab'				)
             self.assertEqual(len(lowercase_				)    ,     14				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Optional[int]:
             """simple docstring"""




             self.assertEqual(self.get_tokenizer().vocab_size    ,     14				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname				)

             # make sure @@ is correctly concatenated
             _lowerCamelCase :	Union[str, Any]						=[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
             _lowerCamelCase :	Optional[int]						=tokenizer.decode(lowercase_				)

             self.assertTrue(output_string == 'herecouple words ofthe'				)






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname				)

             with tempfile.TemporaryDirectory() as tmp_dirname:
                    tokenizer.save_pretrained(lowercase_				)
                    os.remove(os.path.join(lowercase_    ,     'merges.txt'				)				)

                    # load tokenizer without merges file should not throw an error
                    _lowerCamelCase :	List[str]						=SpeechaTextaTokenizer.from_pretrained(lowercase_				)

             with tempfile.TemporaryDirectory() as tmp_dirname:
                    # save tokenizer and load again
                    tokenizer.save_pretrained(lowercase_				)
                    _lowerCamelCase :	int						=SpeechaTextaTokenizer.from_pretrained(lowercase_				)

             self.assertIsNotNone(lowercase_				)






      def  lowerCamelCase (      self		:  str				)      ->		Any:
             """simple docstring"""




             if not self.test_slow_tokenizer:
                    return

             _lowerCamelCase :	Optional[int]						=inspect.signature(self.tokenizer_class.__init__				)
             _lowerCamelCase :	Optional[Any]						=self.get_tokenizer()

             for parameter_name, parameter in signature.parameters.items():
                    if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                           self.assertIn(lowercase_    ,     tokenizer.init_kwargs				)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   ='AutoFeatureExtractor'
      UpperCamelCase__							:						List[str]   ='Speech2Text2Tokenizer'






      def __init__(      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             super().__init__(lowercase_    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=self.feature_extractor
             _lowerCamelCase :	Tuple						=False






      def __call__(      self		:  Tuple    ,     *lowercase_		:  str    ,     **lowercase_		:  List[Any]				)      ->		List[str]:
             """simple docstring"""




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

             if "raw_speech" in kwargs:
                    warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
                    _lowerCamelCase :	List[str]						=kwargs.pop('raw_speech'				)
             else:
                    _lowerCamelCase :	str						=kwargs.pop('audio'    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=kwargs.pop('sampling_rate'    ,     lowercase_				)
             _lowerCamelCase :	List[str]						=kwargs.pop('text'    ,     lowercase_				)
             if len(lowercase_				) > 0:
                    _lowerCamelCase :	Dict						=args[0]
                    _lowerCamelCase :	Optional[Any]						=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:
                    _lowerCamelCase :	List[Any]						=self.feature_extractor(lowercase_    ,     *lowercase_    ,     sampling_rate=lowercase_    ,     **lowercase_				)
             if text is not None:
                    _lowerCamelCase :	Tuple						=self.tokenizer(lowercase_    ,     **lowercase_				)

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






      def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  Optional[int]    ,     *lowercase_		:  Dict    ,     **lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




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






      @contextmanager
      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """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.'				)
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	List[Any]						=self.tokenizer
             yield
             _lowerCamelCase :	Optional[int]						=self.feature_extractor
             _lowerCamelCase :	List[str]						=False


