



"""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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     List[str]		=		'Wav2Vec2FeatureExtractor'
   __lowerCAmelCase						:     str		=		'AutoTokenizer'

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			self.feature_extractor
    UpperCAmelCase			:      List[str]			=			False

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    try:
     return super().from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
    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: """   ,  _SCREAMING_SNAKE_CASE   ,  )

     UpperCAmelCase			:      Any			=			WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[str]			=			WavaVecaCTCTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

     return cls(feature_extractor=_SCREAMING_SNAKE_CASE   ,  tokenizer=_SCREAMING_SNAKE_CASE		)

   def __call__(     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




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

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

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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    if self._in_target_context_manager:
     return self.current_processor.pad(*_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			kwargs.pop("""input_features"""   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			kwargs.pop("""labels"""   ,  _SCREAMING_SNAKE_CASE		)
    if len(_SCREAMING_SNAKE_CASE		) > 0:
     UpperCAmelCase			:      Any			=			args[0]
     UpperCAmelCase			:      Optional[int]			=			args[1:]

    if input_features is not None:
     UpperCAmelCase			:      str			=			self.feature_extractor.pad(_SCREAMING_SNAKE_CASE   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
    if labels is not None:
     UpperCAmelCase			:      Union[str, Any]			=			self.tokenizer.pad(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    if labels is None:
     return input_features
    elif input_features is None:
     return labels
    else:
     UpperCAmelCase			:      List[str]			=			labels["""input_ids"""]
     return input_features

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




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




   @contextmanager
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   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."""		)
    UpperCAmelCase			:      Any			=			True
    UpperCAmelCase			:      int			=			self.tokenizer
    yield
    UpperCAmelCase			:      Union[str, Any]			=			self.feature_extractor
    UpperCAmelCase			:      Union[str, Any]			=			False





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,	[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]		):
 UpperCAmelCase			:      List[Any]			=			tmp_path_factory.mktemp("""dset_infos_dir"""		)
 if "full:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""		)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write(""""""		)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / """dataset_infos.json"""    ,	"""w"""		) as f:
   f.write("""{\"default\": {\"dataset_size\": 42}}"""		)
 UpperCAmelCase			:      str			=			DatasetInfosDict.from_directory(UpperCamelCase		)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,	[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	DatasetInfo		):
 UpperCAmelCase			:      List[Any]			=			str(UpperCamelCase		)
 dataset_info.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			DatasetInfo.from_directory(UpperCamelCase		)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(UpperCamelCase    ,	"""dataset_info.json"""		)		)

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo(
     description="""foo"""    ,	citation="""bar"""    ,	homepage="""https://foo.bar"""    ,	license="""CC0"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	post_processed={}    ,	supervised_keys=()    ,	task_templates=[]    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train""", """num_examples""": 42}]    ,	download_checksums={}    ,	download_size=1337    ,	post_processing_size=442    ,	dataset_size=1234    ,	size_in_bytes=1337 + 442 + 1234    ,	)
 UpperCAmelCase			:      str			=			dataset_info._to_yaml_dict()
 assert sorted(UpperCamelCase		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]    ,	(list, dict, int, str)		)
 UpperCAmelCase			:      Union[str, Any]			=			yaml.safe_dump(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			yaml.safe_load(UpperCamelCase		)
 assert dataset_info_yaml_dict == reloaded

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo()
 UpperCAmelCase			:      Union[str, Any]			=			dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,	[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}		),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}		),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	)
            }		),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42		),
                """v2""": DatasetInfo(dataset_size=1337		),
            }		),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	DatasetInfosDict		):
 UpperCAmelCase			:      List[str]			=			str(UpperCamelCase		)
 dataset_infos_dict.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			DatasetInfosDict.from_directory(UpperCamelCase		)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  UpperCAmelCase			:      Union[str, Any]			=			config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  UpperCAmelCase			:      Optional[Any]			=			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(UpperCamelCase    ,	"""README.md"""		)		)
