'''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


SCREAMING_SNAKE_CASE_:					Any  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  ["""input_values""", """padding_mask"""]



    def __init__(self   :   Tuple       ,      __a   :   int = 1       ,      __a   :   int = 24000       ,      __a   :   float = 0.0       ,      __a   :   float = None       ,      __a   :   float = None       ,      **__a   :   str       ,      ):
         super().__init__(feature_size=__a       ,      sampling_rate=__a       ,      padding_value=__a       ,      **__a			)
         UpperCAmelCase_	   =       chunk_length_s
         UpperCAmelCase_	   =       overlap



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



    @property
    def  _lowercase						(self   :   List[Any]			):
         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       ,      __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       ,      ):
         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_	   =       True

         UpperCAmelCase_	   =       bool(
             isinstance(__a       ,      (list, tuple)			) and (isinstance(raw_audio[0]       ,      (np.ndarray, tuple, list)			))			)

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

         # always return batch
         if not is_batched:
              UpperCAmelCase_	   =       [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_	   =       None
         UpperCAmelCase_	   =       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_	   =       min(array.shape[0] for array in raw_audio			)
                   UpperCAmelCase_	   =       int(np.floor(max_length / self.chunk_stride			)			)
                   UpperCAmelCase_	   =       (nb_step - 1) * self.chunk_stride + self.chunk_length
              elif padding:
                   UpperCAmelCase_	   =       max(array.shape[0] for array in raw_audio			)
                   UpperCAmelCase_	   =       int(np.ceil(max_length / self.chunk_stride			)			)
                   UpperCAmelCase_	   =       (nb_step - 1) * self.chunk_stride + self.chunk_length
                   UpperCAmelCase_	   =       "max_length"
              else:
                   UpperCAmelCase_	   =       input_values

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

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

         UpperCAmelCase_	   =       input_values
         if return_tensors is not None:
              UpperCAmelCase_	   =       padded_inputs.convert_to_tensors(__a			)

         return padded_inputs





'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




