

from typing import List, Optional

import numpy as np

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



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Union[str, Any]      							=					'''EncodecFeatureExtractor'''
			_UpperCAmelCase       :							str      							=					('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					Tuple):
									super().__init__(lowerCAmelCase__       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.feature_extractor
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Dict=None       ,						lowerCAmelCase__  :					List[Any]=None       ,						lowerCAmelCase__  :					List[Any]=True):
									return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__       ,						language=lowerCAmelCase__       ,						no_timestamps=lowerCAmelCase__)



			def __call__(			self  :					int       ,						*lowerCAmelCase__  :					List[str]       ,						**lowerCAmelCase__  :					str):
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	kwargs.pop("audio"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	kwargs.pop("sampling_rate"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	kwargs.pop("text"       ,						lowerCAmelCase__)
									if len(lowerCAmelCase__) > 0:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	args[0]
															SCREAMING_SNAKE_CASE_:			Union[str, 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 text is not None:
															SCREAMING_SNAKE_CASE_:			int  		=	self.tokenizer(lowerCAmelCase__       ,						**lowerCAmelCase__)

									if audio is not None:
															SCREAMING_SNAKE_CASE_:			Dict  		=	self.feature_extractor(lowerCAmelCase__       ,						*lowerCAmelCase__       ,						sampling_rate=lowerCAmelCase__       ,						**lowerCAmelCase__)

									if audio is None:
															return inputs

									elif text is None:
															return audio_inputs

									else:
															SCREAMING_SNAKE_CASE_:			Dict  		=	audio_inputs["input_values"]
															if "padding_mask" in audio_inputs:
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	audio_inputs["padding_mask"]
															return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						*lowerCAmelCase__  :					Tuple       ,						**lowerCAmelCase__  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	kwargs.pop("audio"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	kwargs.pop("padding_mask"       ,						lowerCAmelCase__)

									if len(lowerCAmelCase__) > 0:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	args[0]
															SCREAMING_SNAKE_CASE_:			Dict  		=	args[1:]

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						*lowerCAmelCase__  :					Optional[int]       ,						**lowerCAmelCase__  :					Tuple):
									return self.tokenizer.decode(*lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Optional = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	to_numpy(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	audio_values.shape

									if padding_mask is None:
															return list(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	to_numpy(lowerCAmelCase__)

									# 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)
									SCREAMING_SNAKE_CASE_:			int  		=	seq_len - padding_mask.shape[-1]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	1 - self.feature_extractor.padding_value
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.pad(lowerCAmelCase__       ,						((0, 0), (0, difference))       ,						"constant"       ,						constant_values=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	audio_values.tolist()
									for i in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Dict  		=	np.asarray(audio_values[i])[
															    padding_mask[i][None, :] != self.feature_extractor.padding_value
															]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	sliced_audio.reshape(lowerCAmelCase__       ,						-1)

									return audio_values









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]            =						{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Optional[int]            =						[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						lowerCAmelCase							:  Any            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






