
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_UpperCamelCase:					List[Any]     ='src/transformers'

# Matches is_xxx_available()
_UpperCamelCase:					Tuple     =re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
_UpperCamelCase:					Tuple     =re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_UpperCamelCase:					List[str]     =re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


_UpperCamelCase:					Dict     ='\n{0} = None\n'

_UpperCamelCase:					int     ='\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_UpperCamelCase:					Dict     ='\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]    ):






  """simple docstring"""




  if _re_test_backend.search(__SCREAMING_SNAKE_CASE    ) is None:
    return None
  _lowerCAmelCase						=		[b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE    )]
  backends.sort()
  return "_and_".join(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  with open(os.path.join(__SCREAMING_SNAKE_CASE		,				'__init__.py'    )		,				'r'		,				encoding='utf-8'		,				newline='\n'    ) as f:
    _lowerCAmelCase						=		f.readlines()

  # Get to the point we do the actual imports for type checking
  _lowerCAmelCase						=		0
  while not lines[line_index].startswith('if TYPE_CHECKING'    ):
    line_index += 1

  _lowerCAmelCase						=		{}
  # Go through the end of the file
  while line_index < len(__SCREAMING_SNAKE_CASE    ):
    # If the line is an if is_backend_available, we grab all objects associated.
    _lowerCAmelCase						=		find_backend(lines[line_index]    )
    if backend is not None:
      while not lines[line_index].startswith('    else:'    ):
        line_index += 1
      line_index += 1

      _lowerCAmelCase						=		[]
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(' ' * 8    ):
        _lowerCAmelCase						=		lines[line_index]
        _lowerCAmelCase						=		_re_single_line_import.search(__SCREAMING_SNAKE_CASE    )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(', '    )    )
        elif line.startswith(' ' * 12    ):
          objects.append(line[12:-2]    )
        line_index += 1

      _lowerCAmelCase						=		objects
    else:
      line_index += 1

  return backend_specific_objects



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Dict    ):






  """simple docstring"""




  if name.isupper():
    return DUMMY_CONSTANT.format(__SCREAMING_SNAKE_CASE    )
  elif name.islower():
    return DUMMY_FUNCTION.format(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  else:
    return DUMMY_CLASS.format(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple=None    ):






  """simple docstring"""




  if backend_specific_objects is None:
    _lowerCAmelCase						=		read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  _lowerCAmelCase						=		{}

  for backend, objects in backend_specific_objects.items():
    _lowerCAmelCase						=		'[' + ', '.join(f'''"{b}"''' for b in backend.split('_and_'    )    ) + ']'
    _lowerCAmelCase						=		'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ) for o in objects]    )
    _lowerCAmelCase						=		dummy_file

  return dummy_files



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]=False    ):






  """simple docstring"""




  _lowerCAmelCase						=		create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  _lowerCAmelCase						=		{'torch': 'pt'}

  # Locate actual dummy modules and read their content.
  _lowerCAmelCase						=		os.path.join(__SCREAMING_SNAKE_CASE		,				'utils'    )
  _lowerCAmelCase						=		{
      backend: os.path.join(__SCREAMING_SNAKE_CASE		,				f'''dummy_{short_names.get(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )}_objects.py'''    )
      for backend in dummy_files.keys()
  }

  _lowerCAmelCase						=		{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(__SCREAMING_SNAKE_CASE    ):
      with open(__SCREAMING_SNAKE_CASE		,				'r'		,				encoding='utf-8'		,				newline='\n'    ) as f:
        _lowerCAmelCase						=		f.read()
    else:
      _lowerCAmelCase						=		''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            f'''Updating transformers.utils.dummy_{short_names.get(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )}_objects.py as the main '''
            '__init__ has new objects.'    )
        with open(dummy_file_paths[backend]		,				'w'		,				encoding='utf-8'		,				newline='\n'    ) as f:
          f.write(dummy_files[backend]    )
      else:
        raise ValueError(
            'The main __init__ has objects that are not present in '
            f'''transformers.utils.dummy_{short_names.get(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )}_objects.py. Run `make fix-copies` '''
            'to fix this.'    )


if __name__ == "__main__":
       _UpperCamelCase:					Tuple     =argparse.ArgumentParser()
       parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
       _UpperCamelCase:					List[Any]     =parser.parse_args()

       check_dummies(args.fix_and_overwrite)


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

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

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

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

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )
