



"""simple docstring"""
import warnings
from contextlib import contextmanager

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


class 				lowerCamelCase_( lowerCAmelCase_     ):





					'''simple docstring'''



					lowercase__ :							int              =			'Wav2Vec2FeatureExtractor'
					lowercase__ :							Any              =			'AutoTokenizer'

					def __init__(     self					,							lowerCamelCase__					,							lowerCamelCase__ ):
									super().__init__(__lowerCAmelCase					,							__lowerCAmelCase )
									_lowerCamelCase   	=					self.feature_extractor
									_lowerCamelCase   	=					False

					@classmethod
					def       snake_case__   (     cls					,							lowerCamelCase__					,							**lowerCamelCase__ ):
									try:
													return super().from_pretrained(__lowerCAmelCase					,							**__lowerCAmelCase )
									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: '''					,							__lowerCAmelCase					,							)

													_lowerCamelCase   	=					WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase					,							**__lowerCAmelCase )
													_lowerCamelCase   	=					WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase					,							**__lowerCAmelCase )

													return cls(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase )

					def __call__(     self					,							*lowerCamelCase__					,							**lowerCamelCase__ ):
									# For backward compatibility
									if self._in_target_context_manager:
													return self.current_processor(*__lowerCAmelCase					,							**__lowerCAmelCase )

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

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

					def       snake_case__   (     self					,							*lowerCamelCase__					,							**lowerCamelCase__ ):
									# For backward compatibility
									if self._in_target_context_manager:
													return self.current_processor.pad(*__lowerCAmelCase					,							**__lowerCAmelCase )

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

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

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

					def       snake_case__   (     self					,							*lowerCamelCase__					,							**lowerCamelCase__ ):
									return self.tokenizer.batch_decode(*__lowerCAmelCase					,							**__lowerCAmelCase )

					def       snake_case__   (     self					,							*lowerCamelCase__					,							**lowerCamelCase__ ):
									return self.tokenizer.decode(*__lowerCAmelCase					,							**__lowerCAmelCase )





					@contextmanager
					def       snake_case__   (     self ):
									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   	=					True
									_lowerCamelCase   	=					self.tokenizer
									yield
									_lowerCamelCase   	=					self.feature_extractor
									_lowerCamelCase   	=					False

"""simple docstring"""


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Dict  				=				MgpstrTokenizer
    _snake_case  :	Any  				=				False
    _snake_case  :	str  				=				{}
    _snake_case  :	Dict  				=				False


    def   lowerCAmelCase_	(       self						:	int       ):
      super().setUp()

      # fmt: off
      _UpperCAmelCase    			=   ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      _UpperCAmelCase    			=   dict(zip(__lowerCAmelCase ,					range(len(__lowerCAmelCase       )       )       )       )

      _UpperCAmelCase    			=   os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]       )
      with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""       ) as fp:
        fp.write(json.dumps(__lowerCAmelCase       ) + """\n"""       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					**__lowerCAmelCase						:	Dict       ):
      return MgpstrTokenizer.from_pretrained(self.tmpdirname ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   """tester"""
      _UpperCAmelCase    			=   """tester"""
      return input_text, output_text


    @unittest.skip("""MGP-STR always lower cases letters."""       )
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.get_tokenizers(do_lower_case=__lowerCAmelCase       )
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase    			=   """[SPECIAL_TOKEN]"""

          tokenizer.add_special_tokens({"""cls_token""": special_token}       )
          _UpperCAmelCase    			=   tokenizer.encode([special_token] ,					add_special_tokens=__lowerCAmelCase       )
          self.assertEqual(len(__lowerCAmelCase       ) ,					1       )

          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
          self.assertTrue(special_token not in decoded       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase			,					_UpperCAmelCase    			=   self.get_input_output_texts(__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.tokenize(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.convert_tokens_to_ids(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )
          self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.convert_ids_to_tokens(__lowerCAmelCase       )
          self.assertNotEqual(len(__lowerCAmelCase       ) ,					0       )
          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase       )
          self.assertIsInstance(__lowerCAmelCase ,					__lowerCAmelCase       )

          self.assertEqual(text_a.replace(""" """ ,					""""""       ) ,					__lowerCAmelCase       )


    @unittest.skip("""MGP-STR tokenizer only handles one sequence."""       )
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""       )
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      pass
