

"""simple docstring"""





from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCamelCase  :      str							      =		logging.get_logger(__name__)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["input_values", "padding_mask"]







					def __init__(       self			:				int   ,lowercase_			:				int = 1   ,lowercase_			:				int = 2_4_0_0_0   ,lowercase_			:				float = 0.0   ,lowercase_			:				float = None   ,lowercase_			:				float = None   ,**lowercase_			:				Tuple   ,):
						super().__init__(feature_size=lowercase_   ,sampling_rate=lowercase_   ,padding_value=lowercase_   ,**lowercase_		)
						lowerCAmelCase__	:						Optional[Any]  				=	chunk_length_s
						lowerCAmelCase__	:						List[Any]  				=	overlap







					@property
					def        __lowerCAmelCase (       self			:				Tuple		):
						if self.chunk_length_s is None:
							return None
						else:
							return int(self.chunk_length_s * self.sampling_rate		)







					@property
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.chunk_length_s is None or self.overlap is None:
							return None
						else:
							return max(1   ,int((1.0 - self.overlap) * self.chunk_length		)		)







					def __call__(       self			:				str   ,lowercase_			:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,lowercase_			:				Optional[Union[bool, str, PaddingStrategy]] = None   ,lowercase_			:				Optional[bool] = False   ,lowercase_			:				Optional[int] = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				Optional[int] = None   ,):
						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
								    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
								    F' {self.sampling_rate} and not {sampling_rate}.'		)
						else:
							logger.warning(
							    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
							    '''Failing to do so can result in silent errors that might be hard to debug.'''		)

						if padding and truncation:
							raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''		)
						elif padding is None:
							# by default let's pad the inputs
							lowerCAmelCase__	:						Optional[int]  				=	True

						lowerCAmelCase__	:						int  				=	bool(
						    isinstance(lowercase_   ,(list, tuple)		) and (isinstance(raw_audio[0]   ,(np.ndarray, tuple, list)		))		)

						if is_batched:
							lowerCAmelCase__	:						str  				=	[np.asarray(lowercase_   ,dtype=np.floataa		).T for audio in raw_audio]
						elif not is_batched and not isinstance(lowercase_   ,np.ndarray		):
							lowerCAmelCase__	:						List[Any]  				=	np.asarray(lowercase_   ,dtype=np.floataa		)
						elif isinstance(lowercase_   ,np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
							lowerCAmelCase__	:						Tuple  				=	raw_audio.astype(np.floataa		)

						# always return batch
						if not is_batched:
							lowerCAmelCase__	:						Dict  				=	[np.asarray(lowercase_		).T]

						# verify inputs are valid
						for idx, example in enumerate(lowercase_		):
							if example.ndim > 2:
								raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'		)
							if self.feature_size == 1 and example.ndim != 1:
								raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'		)
							if self.feature_size == 2 and example.shape[-1] != 2:
								raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'		)

						lowerCAmelCase__	:						Union[str, Any]  				=	None
						lowerCAmelCase__	:						int  				=	BatchFeature({'''input_values''': raw_audio}		)
						if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
							if truncation:
								lowerCAmelCase__	:						Optional[int]  				=	min(array.shape[0] for array in raw_audio		)
								lowerCAmelCase__	:						Any  				=	int(np.floor(max_length / self.chunk_stride		)		)
								lowerCAmelCase__	:						List[str]  				=	(nb_step - 1) * self.chunk_stride + self.chunk_length
							elif padding:
								lowerCAmelCase__	:						Union[str, Any]  				=	max(array.shape[0] for array in raw_audio		)
								lowerCAmelCase__	:						Dict  				=	int(np.ceil(max_length / self.chunk_stride		)		)
								lowerCAmelCase__	:						Dict  				=	(nb_step - 1) * self.chunk_stride + self.chunk_length
								lowerCAmelCase__	:						str  				=	'''max_length'''
							else:
								lowerCAmelCase__	:						Tuple  				=	input_values

        # normal padding on batch
						if padded_inputs is None:
							lowerCAmelCase__	:						List[str]  				=	self.pad(
							    lowercase_   ,max_length=lowercase_   ,truncation=lowercase_   ,padding=lowercase_   ,return_attention_mask=lowercase_   ,)
							if padding:
								lowerCAmelCase__	:						Optional[int]  				=	padded_inputs.pop('''attention_mask'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	[]
						for example in padded_inputs.pop('''input_values'''		):
							if self.feature_size == 1:
								lowerCAmelCase__	:						Optional[int]  				=	example[..., None]
							input_values.append(example.T		)

						lowerCAmelCase__	:						Optional[int]  				=	input_values
						if return_tensors is not None:
							lowerCAmelCase__	:						int  				=	padded_inputs.convert_to_tensors(lowercase_		)

						return padded_inputs







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




