




import warnings
from contextlib import contextmanager

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

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''Wav2Vec2FeatureExtractor'''
							UpperCamelCase__   	=    '''AutoTokenizer'''
							def __init__(		self		:List[Any]						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								super().__init__(__magic_name__						, __magic_name__							)
								a									=				self.feature_extractor
								a									=				False
							@classmethod
							def  lowerCamelCase__				(		cls		:str						, __magic_name__		:List[Any]						, **__magic_name__		:Tuple							):







								'''simple docstring'''







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

									a									=				WavaVecaFeatureExtractor.from_pretrained(__magic_name__						, **__magic_name__							)
									a									=				WavaVecaCTCTokenizer.from_pretrained(__magic_name__						, **__magic_name__							)

									return cls(feature_extractor=__magic_name__						, tokenizer=__magic_name__							)
							def __call__(		self		:List[str]						, *__magic_name__		:Optional[Any]						, **__magic_name__		:Any							):







								'''simple docstring'''







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

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

								if text is None:
									return inputs
								elif audio is None:
									return encodings
								else:
									a									=				encodings["""input_ids"""]
									return inputs
							def  lowerCamelCase__				(		self		:str						, *__magic_name__		:List[Any]						, **__magic_name__		:int							):







								'''simple docstring'''







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

								a									=				kwargs.pop("""input_features"""						, __magic_name__							)
								a									=				kwargs.pop("""labels"""						, __magic_name__							)
								if len(__magic_name__							) > 0:
									a									=				args[0]
									a									=				args[1:]

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

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									a									=				labels["""input_ids"""]
									return input_features
							def  lowerCamelCase__				(		self		:Dict						, *__magic_name__		:List[Any]						, **__magic_name__		:List[str]							):







								'''simple docstring'''







								return self.tokenizer.batch_decode(*__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:Tuple						, *__magic_name__		:List[Any]						, **__magic_name__		:str							):







								'''simple docstring'''







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





							@contextmanager
							def  lowerCamelCase__				(		self		:List[str]							):







								'''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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

