






"""simple docstring"""



from typing import List, Optional

import numpy as np

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







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[str]             =			"EncodecFeatureExtractor"
			_UpperCAmelCase		:str             =			("T5Tokenizer", "T5TokenizerFast")


			def __init__(				self					:  Optional[Any]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Tuple ):
										super().__init__(snake_case__       ,	snake_case__ )
										lowerCamelCase_	:							Any									=self.feature_extractor
										lowerCamelCase_	:							Union[str, Any]									=False


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


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

										lowerCamelCase_	:							Optional[int]									=kwargs.pop("audio"       ,	snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									=kwargs.pop("sampling_rate"       ,	snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									=kwargs.pop("text"       ,	snake_case__ )
										if len(snake_case__ ) > 0:
																	lowerCamelCase_	:							Optional[Any]									=args[0]
																	lowerCamelCase_	:							Optional[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:
																	lowerCamelCase_	:							List[Any]									=self.tokenizer(snake_case__       ,	**snake_case__ )

										if audio is not None:
																	lowerCamelCase_	:							Optional[int]									=self.feature_extractor(snake_case__       ,	*snake_case__       ,	sampling_rate=snake_case__       ,	**snake_case__ )

										if audio is None:
																	return inputs

										elif text is None:
																	return audio_inputs

										else:
																	lowerCamelCase_	:							Optional[int]									=audio_inputs["input_values"]
																	if "padding_mask" in audio_inputs:
																								lowerCamelCase_	:							Optional[int]									=audio_inputs["padding_mask"]
																	return inputs


			def      UpperCAmelCase__						(				self					:  List[str]       ,	*snake_case__					:  List[Any]       ,	**snake_case__					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=kwargs.pop("audio"       ,	snake_case__ )
										lowerCamelCase_	:							Dict									=kwargs.pop("padding_mask"       ,	snake_case__ )

										if len(snake_case__ ) > 0:
																	lowerCamelCase_	:							Optional[Any]									=args[0]
																	lowerCamelCase_	:							List[Any]									=args[1:]

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


			def      UpperCAmelCase__						(				self					:  str       ,	*snake_case__					:  List[Any]       ,	**snake_case__					:  Optional[Any] ):
										return self.tokenizer.decode(*snake_case__       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional = None ):
										lowerCamelCase_	:							str									=to_numpy(snake_case__ )
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							str									=audio_values.shape

										if padding_mask is None:
																	return list(snake_case__ )

										lowerCamelCase_	:							Optional[int]									=to_numpy(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)
										lowerCamelCase_	:							Tuple									=seq_len - padding_mask.shape[-1]
										lowerCamelCase_	:							Tuple									=1 - self.feature_extractor.padding_value
										lowerCamelCase_	:							int									=np.pad(snake_case__       ,	((0, 0), (0, difference))       ,	"constant"       ,	constant_values=snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=audio_values.tolist()
										for i in range(snake_case__ ):
																	lowerCamelCase_	:							int									=np.asarray(audio_values[i] )[
																	    padding_mask[i][None, :] != self.feature_extractor.padding_value
																	]
																	lowerCamelCase_	:							List[Any]									=sliced_audio.reshape(snake_case__       ,	-1 )

										return audio_values











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


A__	:    List[str]			 = {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

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



