


from typing import List, Optional

import numpy as np

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

class 						__a (							UpperCAmelCase     ):
		_a      :    int           =      'EncodecFeatureExtractor'
		_a      :    Optional[Any]           =      ('T5Tokenizer', 'T5TokenizerFast')
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     self.feature_extractor
									_UpperCAmelCase											=     False
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=True		)					->			Tuple:




									"""simple docstring"""





									return self.tokenizer.get_decoder_prompt_ids(task=_SCREAMING_SNAKE_CASE     ,       language=_SCREAMING_SNAKE_CASE     ,       no_timestamps=_SCREAMING_SNAKE_CASE		)
		def __call__(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





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

									_UpperCAmelCase											=     kwargs.pop('audio'     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     kwargs.pop('sampling_rate'     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     kwargs.pop('text'     ,       _SCREAMING_SNAKE_CASE		)
									if len(_SCREAMING_SNAKE_CASE		) > 0:
																_UpperCAmelCase											=     args[0]
																_UpperCAmelCase											=     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:
																_UpperCAmelCase											=     self.tokenizer(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									if audio is not None:
																_UpperCAmelCase											=     self.feature_extractor(_SCREAMING_SNAKE_CASE     ,       *_SCREAMING_SNAKE_CASE     ,       sampling_rate=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									if audio is None:
																return inputs

									elif text is None:
																return audio_inputs

									else:
																_UpperCAmelCase											=     audio_inputs['input_values']
																if "padding_mask" in audio_inputs:
																							_UpperCAmelCase											=     audio_inputs['padding_mask']
																return inputs
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     kwargs.pop('audio'     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     kwargs.pop('padding_mask'     ,       _SCREAMING_SNAKE_CASE		)

									if len(_SCREAMING_SNAKE_CASE		) > 0:
																_UpperCAmelCase											=     args[0]
																_UpperCAmelCase											=     args[1:]

									if audio_values is not None:
																return self._decode_audio(_SCREAMING_SNAKE_CASE     ,       padding_mask=_SCREAMING_SNAKE_CASE		)
									else:
																return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			Optional[int]:




									"""simple docstring"""





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






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[np.ndarray]:




									"""simple docstring"""





									_UpperCAmelCase											=     to_numpy(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     audio_values.shape

									if padding_mask is None:
																return list(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     to_numpy(_SCREAMING_SNAKE_CASE		)

									# 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)
									_UpperCAmelCase											=     seq_len - padding_mask.shape[-1]
									_UpperCAmelCase											=     1 - self.feature_extractor.padding_value
									_UpperCAmelCase											=     np.pad(_SCREAMING_SNAKE_CASE     ,       ((0, 0), (0, difference))     ,       'constant'     ,       constant_values=_SCREAMING_SNAKE_CASE		)

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

									return audio_values




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__    :Tuple       =				{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[Any]       =				['''DeiTFeatureExtractor''']
		lowerCAmelCase__    :Union[str, Any]       =				['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Tuple       =				[
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Union[str, Any]       =				[
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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