
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


lowercase						:      Any									= logging.get_logger(__name__)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ['input_values', 'padding_mask']







	def __init__(      self	:Union[str, Any]  ,     a	:int = 1  ,     a	:int = 2_4_0_0_0  ,     a	:float = 0.0  ,     a	:float = None  ,     a	:float = None  ,     **a	:List[str]  ,     )	->				Optional[Any]:
								super().__init__(feature_size=a  ,     sampling_rate=a  ,     padding_value=a  ,     **a		)
								__UpperCamelCase			:     Any         =      chunk_length_s
								__UpperCamelCase			:     Optional[int]         =      overlap







	@property
	def      _lowerCamelCase       (      self	:List[Any]		)	->				int:







								if self.chunk_length_s is None:
															return None
								else:
															return int(self.chunk_length_s * self.sampling_rate		)







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				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	:Dict  ,     a	:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,     a	:Optional[Union[bool, str, PaddingStrategy]] = None  ,     a	:Optional[bool] = False  ,     a	:Optional[int] = None  ,     a	:Optional[Union[str, TensorType]] = None  ,     a	:Optional[int] = None  ,     )	->				Optional[int]:







								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			:     Any         =      True

								__UpperCamelCase			:     List[Any]         =      bool(
								    isinstance(a  ,     (list, tuple)		) and (isinstance(raw_audio[0]  ,     (np.ndarray, tuple, list)		))		)

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

								# always return batch
								if not is_batched:
															__UpperCamelCase			:     str         =      [np.asarray(a		).T]

								# verify inputs are valid
								for idx, example in enumerate(a		):
															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			:     Union[str, Any]         =      None
								__UpperCamelCase			:     Any         =      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			:     Any         =      min(array.shape[0] for array in raw_audio		)
																						__UpperCamelCase			:     int         =      int(np.floor(max_length / self.chunk_stride		)		)
																						__UpperCamelCase			:     Dict         =      (nb_step - 1) * self.chunk_stride + self.chunk_length
															elif padding:
																						__UpperCamelCase			:     str         =      max(array.shape[0] for array in raw_audio		)
																						__UpperCamelCase			:     Optional[int]         =      int(np.ceil(max_length / self.chunk_stride		)		)
																						__UpperCamelCase			:     Dict         =      (nb_step - 1) * self.chunk_stride + self.chunk_length
																						__UpperCamelCase			:     Any         =      'max_length'
															else:
																						__UpperCamelCase			:     List[str]         =      input_values

        # normal padding on batch
								if padded_inputs is None:
															__UpperCamelCase			:     Any         =      self.pad(
															    a  ,     max_length=a  ,     truncation=a  ,     padding=a  ,     return_attention_mask=a  ,     )
															if padding:
																						__UpperCamelCase			:     str         =      padded_inputs.pop("attention_mask"		)

								__UpperCamelCase			:     Optional[int]         =      []
								for example in padded_inputs.pop("input_values"		):
															if self.feature_size == 1:
																						__UpperCamelCase			:     Any         =      example[..., None]
															input_values.append(example.T		)

								__UpperCamelCase			:     List[Any]         =      input_values
								if return_tensors is not None:
															__UpperCamelCase			:     List[str]         =      padded_inputs.convert_to_tensors(a		)

								return padded_inputs

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase						:      str									= {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      str									= ['VisionEncoderDecoderModel']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      List[Any]									= ['TFVisionEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      int									= ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)