




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: List[Any]										="""EncodecFeatureExtractor"""
				__a							: int										=("""T5Tokenizer""", """T5TokenizerFast""")



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											super().__init__(UpperCAmelCase_       , UpperCAmelCase_						)
											lowerCAmelCase   =		self.feature_extractor
											lowerCAmelCase   =		False



				def       __snake_case		( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_=True						):
											return self.tokenizer.get_decoder_prompt_ids(task=UpperCAmelCase_       , language=UpperCAmelCase_       , no_timestamps=UpperCAmelCase_						)



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

											lowerCAmelCase   =		kwargs.pop('''audio'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		kwargs.pop('''sampling_rate'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		kwargs.pop('''text'''       , UpperCAmelCase_						)
											if len(UpperCAmelCase_						) > 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 text is not None:
																		lowerCAmelCase   =		self.tokenizer(UpperCAmelCase_       , **UpperCAmelCase_						)

											if audio is not None:
																		lowerCAmelCase   =		self.feature_extractor(UpperCAmelCase_       , *UpperCAmelCase_       , sampling_rate=UpperCAmelCase_       , **UpperCAmelCase_						)

											if audio is None:
																		return inputs

											elif text is None:
																		return audio_inputs

											else:
																		lowerCAmelCase   =		audio_inputs['''input_values''']
																		if "padding_mask" in audio_inputs:
																									lowerCAmelCase   =		audio_inputs['''padding_mask''']
																		return inputs



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											lowerCAmelCase   =		kwargs.pop('''audio'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		kwargs.pop('''padding_mask'''       , UpperCAmelCase_						)

											if len(UpperCAmelCase_						) > 0:
																		lowerCAmelCase   =		args[0]
																		lowerCAmelCase   =		args[1:]

											if audio_values is not None:
																		return self._decode_audio(UpperCAmelCase_       , padding_mask=UpperCAmelCase_						)
											else:
																		return self.tokenizer.batch_decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.decode(*UpperCAmelCase_       , **UpperCAmelCase_						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		to_numpy(UpperCAmelCase_						)
											lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		audio_values.shape

											if padding_mask is None:
																		return list(UpperCAmelCase_						)

											lowerCAmelCase   =		to_numpy(UpperCAmelCase_						)

											# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
											# token (so that the generated audio values are **not** treated as padded tokens)
											lowerCAmelCase   =		seq_len - padding_mask.shape[-1]
											lowerCAmelCase   =		1 - self.feature_extractor.padding_value
											lowerCAmelCase   =		np.pad(UpperCAmelCase_       , ((0, 0), (0, difference))       , '''constant'''       , constant_values=UpperCAmelCase_						)

											lowerCAmelCase   =		audio_values.tolist()
											for i in range(UpperCAmelCase_						):
																		lowerCAmelCase   =		np.asarray(audio_values[i]						)[
																		    padding_mask[i][None, :] != self.feature_extractor.padding_value
																		]
																		lowerCAmelCase   =		sliced_audio.reshape(UpperCAmelCase_       , -1						)

											return audio_values









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						={
    """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_						=[
		    """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_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



