



"""simple docstring"""

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= '''EncodecFeatureExtractor'''
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= ('''T5Tokenizer''', '''T5TokenizerFast''')





	def __init__(      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	List[str]:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.feature_extractor
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     False





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       Tuple=True    )						->	List[str]:





					"""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				:       Dict     ,       *_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	str:





					"""simple docstring"""


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

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

					if audio is not None:
									SCREAMING_SNAKE_CASE						:							Tuple							=     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:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     audio_inputs['input_values']
									if "padding_mask" in audio_inputs:
													SCREAMING_SNAKE_CASE						:							List[str]							=     audio_inputs['padding_mask']
									return inputs





	def 			_lowerCAmelCase     (      self				:       str     ,       *_SCREAMING_SNAKE_CASE				:       Tuple     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     kwargs.pop('audio'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     kwargs.pop('padding_mask'     ,       _SCREAMING_SNAKE_CASE    )

					if len(_SCREAMING_SNAKE_CASE    ) > 0:
									SCREAMING_SNAKE_CASE						:							Any							=     args[0]
									SCREAMING_SNAKE_CASE						:							Tuple							=     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 			_lowerCAmelCase     (      self				:       str     ,       *_SCREAMING_SNAKE_CASE				:       Tuple     ,       **_SCREAMING_SNAKE_CASE				:       Any    )						->	Union[str, Any]:





					"""simple docstring"""


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







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     to_numpy(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     audio_values.shape

					if padding_mask is None:
									return list(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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)
					SCREAMING_SNAKE_CASE						:							Tuple							=     seq_len - padding_mask.shape[-1]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     1 - self.feature_extractor.padding_value
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     np.pad(_SCREAMING_SNAKE_CASE     ,       ((0, 0), (0, difference))     ,       'constant'     ,       constant_values=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     audio_values.tolist()
					for i in range(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							str							=     np.asarray(audio_values[i]    )[
									    padding_mask[i][None, :] != self.feature_extractor.padding_value
									]
									SCREAMING_SNAKE_CASE						:							Tuple							=     sliced_audio.reshape(_SCREAMING_SNAKE_CASE     ,       -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_						:    Optional[int]				=      {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

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





