



from typing import List, Optional

import numpy as np

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

class        UpperCAmelCase_  (       __lowercase	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  "EncodecFeatureExtractor"
						UpperCamelCase__      :			Union[str, Any]      =  ("T5Tokenizer", "T5TokenizerFast")






						def __init__(   self				,							_A				,							_A		):







													'''simple docstring'''

													super().__init__(snake_case_				,							snake_case_		)
													__SCREAMING_SNAKE_CASE									= self.feature_extractor
													__SCREAMING_SNAKE_CASE									= False






						def 	_A     (   self				,							_A=None				,							_A=None				,							_A=True		):







													'''simple docstring'''

													return self.tokenizer.get_decoder_prompt_ids(task=snake_case_				,							language=snake_case_				,							no_timestamps=snake_case_		)






						def __call__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= kwargs.pop('audio'				,							snake_case_		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('sampling_rate'				,							snake_case_		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('text'				,							snake_case_		)
													if len(snake_case_		) > 0:
																				__SCREAMING_SNAKE_CASE									= args[0]
																				__SCREAMING_SNAKE_CASE									= 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									= self.tokenizer(snake_case_				,							**snake_case_		)

													if audio is not None:
																				__SCREAMING_SNAKE_CASE									= 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:
																				__SCREAMING_SNAKE_CASE									= audio_inputs['''input_values''']
																				if "padding_mask" in audio_inputs:
																											__SCREAMING_SNAKE_CASE									= audio_inputs['''padding_mask''']
																				return inputs






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= kwargs.pop('audio'				,							snake_case_		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('padding_mask'				,							snake_case_		)

													if len(snake_case_		) > 0:
																				__SCREAMING_SNAKE_CASE									= args[0]
																				__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= to_numpy(snake_case_		)
													__SCREAMING_SNAKE_CASE									= audio_values.shape

													if padding_mask is None:
																				return list(snake_case_		)

													__SCREAMING_SNAKE_CASE									= 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)
													__SCREAMING_SNAKE_CASE									= seq_len - padding_mask.shape[-1]
													__SCREAMING_SNAKE_CASE									= 1 - self.feature_extractor.padding_value
													__SCREAMING_SNAKE_CASE									= np.pad(snake_case_				,							((0, 0), (0, difference))				,							'constant'				,							constant_values=snake_case_		)

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

													return audio_values











from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

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






