






"""simple docstring"""



from typing import List, Optional

import numpy as np

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





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "EncodecFeatureExtractor"
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   ("T5Tokenizer", "T5TokenizerFast")




					def __init__(				self     :						Any    ,						A     :						Union[str, Any]    ,						A     :						Tuple   )  ->  int:
							super().__init__(A    ,						A   )
							lowercase_ : int							       =   self.feature_extractor
							lowercase_ : Union[str, Any]							       =   False




					def   A  (				self     :						Dict    ,						A     :						Optional[Any]=None    ,						A     :						Optional[Any]=None    ,						A     :						List[Any]=True   )  ->  Dict:
							return self.tokenizer.get_decoder_prompt_ids(task=A    ,						language=A    ,						no_timestamps=A   )




					def __call__(				self     :						Optional[int]    ,						*A     :						str    ,						**A     :						Any   )  ->  int:
							# For backward compatibility
							if self._in_target_context_manager:
									return self.current_processor(*A    ,						**A   )

							lowercase_ : Any							       =   kwargs.pop('''audio'''    ,						A   )
							lowercase_ : Dict							       =   kwargs.pop('''sampling_rate'''    ,						A   )
							lowercase_ : Dict							       =   kwargs.pop('''text'''    ,						A   )
							if len(A   ) > 0:
									lowercase_ : Optional[int]							       =   args[0]
									lowercase_ : Dict							       =   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:
									lowercase_ : Tuple							       =   self.tokenizer(A    ,						**A   )

							if audio is not None:
									lowercase_ : List[str]							       =   self.feature_extractor(A    ,						*A    ,						sampling_rate=A    ,						**A   )

							if audio is None:
									return inputs

							elif text is None:
									return audio_inputs

							else:
									lowercase_ : Optional[Any]							       =   audio_inputs['''input_values''']
									if "padding_mask" in audio_inputs:
											lowercase_ : List[str]							       =   audio_inputs['''padding_mask''']
									return inputs




					def   A  (				self     :						str    ,						*A     :						Optional[int]    ,						**A     :						List[str]   )  ->  str:
							lowercase_ : List[str]							       =   kwargs.pop('''audio'''    ,						A   )
							lowercase_ : List[str]							       =   kwargs.pop('''padding_mask'''    ,						A   )

							if len(A   ) > 0:
									lowercase_ : Union[str, Any]							       =   args[0]
									lowercase_ : str							       =   args[1:]

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




					def   A  (				self     :						Optional[int]    ,						*A     :						Tuple    ,						**A     :						List[Any]   )  ->  Optional[Any]:
							return self.tokenizer.decode(*A    ,						**A   )






					def   A  (				self     :						Any    ,						A     :						Dict    ,						A     :						Optional = None   )  ->  List[np.ndarray]:
							lowercase_ : Optional[Any]							       =   to_numpy(A   )
							lowercase_       ,					lowercase_       ,					lowercase_ : str							       =   audio_values.shape

							if padding_mask is None:
									return list(A   )

							lowercase_ : List[Any]							       =   to_numpy(A   )

							# 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)
							lowercase_ : int							       =   seq_len - padding_mask.shape[-1]
							lowercase_ : List[str]							       =   1 - self.feature_extractor.padding_value
							lowercase_ : str							       =   np.pad(A    ,						((0, 0), (0, difference))    ,						'''constant'''    ,						constant_values=A   )

							lowercase_ : Union[str, Any]							       =   audio_values.tolist()
							for i in range(A   ):
									lowercase_ : List[Any]							       =   np.asarray(audio_values[i]   )[
									    padding_mask[i][None, :] != self.feature_extractor.padding_value
									]
									lowercase_ : str							       =   sliced_audio.reshape(A    ,						-1   )

							return audio_values











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A		:       Union[str, Any]  				=		{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''NllbTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''NllbTokenizerFast''']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_nllb import NllbTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_nllb_fast import NllbTokenizerFast

else:
						import sys

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



