






"""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


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''input_values''', '''padding_mask''']





	def __init__( self   :					Dict      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					int = 24_000      ,      lowerCAmelCase_   :					float = 0.0      ,      lowerCAmelCase_   :					float = None      ,      lowerCAmelCase_   :					float = None      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     Optional[int]:
				super().__init__(feature_size=lowerCAmelCase_      ,      sampling_rate=lowerCAmelCase_      ,      padding_value=lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   chunk_length_s
				UpperCAmelCase_	:					Union[str, Any]     	=   overlap





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				if self.chunk_length_s is None:
							return None
				else:
							return int(self.chunk_length_s * self.sampling_rate		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     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   :					int      ,      lowerCAmelCase_   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,      lowerCAmelCase_   :					Optional[Union[bool, str, PaddingStrategy]] = None      ,      lowerCAmelCase_   :					Optional[bool] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      )		->     BatchFeature:
				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
							UpperCAmelCase_	:					Tuple     	=   True

				UpperCAmelCase_	:					Union[str, Any]     	=   bool(
				    isinstance(lowerCAmelCase_      ,      (list, tuple)		) and (isinstance(raw_audio[0]      ,      (np.ndarray, tuple, list)		))		)

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

				# always return batch
				if not is_batched:
							UpperCAmelCase_	:					List[str]     	=   [np.asarray(lowerCAmelCase_		).T]

				# verify inputs are valid
				for idx, example in enumerate(lowerCAmelCase_		):
							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"""		)

				UpperCAmelCase_	:					Optional[Any]     	=   None
				UpperCAmelCase_	:					List[str]     	=   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:
										UpperCAmelCase_	:					Optional[int]     	=   min(array.shape[0] for array in raw_audio		)
										UpperCAmelCase_	:					Optional[Any]     	=   int(np.floor(max_length / self.chunk_stride		)		)
										UpperCAmelCase_	:					Optional[int]     	=   (nb_step - 1) * self.chunk_stride + self.chunk_length
							elif padding:
										UpperCAmelCase_	:					int     	=   max(array.shape[0] for array in raw_audio		)
										UpperCAmelCase_	:					Dict     	=   int(np.ceil(max_length / self.chunk_stride		)		)
										UpperCAmelCase_	:					Optional[Any]     	=   (nb_step - 1) * self.chunk_stride + self.chunk_length
										UpperCAmelCase_	:					Tuple     	=   "max_length"
							else:
										UpperCAmelCase_	:					Optional[int]     	=   input_values

        # normal padding on batch
				if padded_inputs is None:
							UpperCAmelCase_	:					List[Any]     	=   self.pad(
							    lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      )
							if padding:
										UpperCAmelCase_	:					Any     	=   padded_inputs.pop("attention_mask"		)

				UpperCAmelCase_	:					List[Any]     	=   []
				for example in padded_inputs.pop("input_values"		):
							if self.feature_size == 1:
										UpperCAmelCase_	:					List[str]     	=   example[..., None]
							input_values.append(example.T		)

				UpperCAmelCase_	:					Dict     	=   input_values
				if return_tensors is not None:
							UpperCAmelCase_	:					Optional[int]     	=   padded_inputs.convert_to_tensors(lowerCAmelCase_		)

				return padded_inputs







'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


