
from typing import List, Optional

import numpy as np

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





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "EncodecFeatureExtractor"
	snake_case_					    =     ("T5Tokenizer", "T5TokenizerFast")






	def __init__(       self					: Tuple							,A					: Any							,A					: List[str] ):
					super().__init__(A							,A )
					__A   			= self.feature_extractor
					__A   			= False






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: Tuple=None							,A					: List[str]=None							,A					: Union[str, Any]=True ):
					return self.tokenizer.get_decoder_prompt_ids(task=A							,language=A							,no_timestamps=A )






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

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

					if audio is not None:
									__A   			= self.feature_extractor(A							,*A							,sampling_rate=A							,**A )

					if audio is None:
									return inputs

					elif text is None:
									return audio_inputs

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






	def 				UpperCamelCase_  (       self					: List[Any]							,*A					: Optional[Any]							,**A					: Optional[int] ):
					__A   			= kwargs.pop("audio"							,A )
					__A   			= kwargs.pop("padding_mask"							,A )

					if len(A ) > 0:
									__A   			= args[0]
									__A   			= 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 				UpperCamelCase_  (       self					: List[Any]							,*A					: Dict							,**A					: Tuple ):
					return self.tokenizer.decode(*A							,**A )







	def 				UpperCamelCase_  (       self					: int							,A					: Union[str, Any]							,A					: Optional = None ):
					__A   			= to_numpy(A )
					__A			,							__A			,							__A   			= audio_values.shape

					if padding_mask is None:
									return list(A )

					__A   			= 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)
					__A   			= seq_len - padding_mask.shape[-1]
					__A   			= 1 - self.feature_extractor.padding_value
					__A   			= np.pad(A							,((0, 0), (0, difference))							,"constant"							,constant_values=A )

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

					return audio_values


from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)




