





"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


A			      =	logging.get_logger(__name__)




class 					__lowercase      (      __SCREAMING_SNAKE_CASE				):







					'''simple docstring'''




					__lowerCAmelCase	=	["""pixel_values"""]







					def __init__(    self						,      _UpperCAmelCase = True						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = PILImageResampling.BILINEAR						,      _UpperCAmelCase = True						,      _UpperCAmelCase = 1 / 255						,      _UpperCAmelCase = True						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      **_UpperCAmelCase						,      ):
									super().__init__(**A_  )
									__a			:    int      							=	size if size is not None else {'''shortest_edge''': 384}
									__a			:    Optional[Any]      							=	get_size_dict(A_						,      default_to_square=A_  )

									__a			:    Tuple      							=	do_resize
									__a			:    str      							=	size
									# Default value set here for backwards compatibility where the value in config is None
									__a			:    List[str]      							=	crop_pct if crop_pct is not None else 224 / 256
									__a			:    List[Any]      							=	resample
									__a			:    List[str]      							=	do_rescale
									__a			:    Any      							=	rescale_factor
									__a			:    Optional[int]      							=	do_normalize
									__a			:    int      							=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__a			:    str      							=	image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase = PILImageResampling.BICUBIC						,      _UpperCAmelCase = None						,      **_UpperCAmelCase						,      ):
									__a			:    Dict      							=	get_size_dict(A_						,      default_to_square=A_  )
									if "shortest_edge" not in size:
													raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""  )
									__a			:    Any      							=	size['''shortest_edge''']

									if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													__a			:    Any      							=	int(shortest_edge / crop_pct  )
													__a			:    List[str]      							=	get_resize_output_image_size(A_						,      size=A_						,      default_to_square=A_  )
													__a			:    Optional[int]      							=	resize(image=A_						,      size=A_						,      resample=A_						,      data_format=A_						,      **A_  )
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=A_						,      size=(shortest_edge, shortest_edge)						,      data_format=A_						,      **A_  )
									else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    A_						,      size=(shortest_edge, shortest_edge)						,      resample=A_						,      data_format=A_						,      **A_  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase = None						,      **_UpperCAmelCase						,      ):
									return rescale(A_						,      scale=A_						,      data_format=A_						,      **A_  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase = None						,      **_UpperCAmelCase						,      ):
									return normalize(A_						,      mean=A_						,      std=A_						,      data_format=A_						,      **A_  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = ChannelDimension.FIRST						,      **_UpperCAmelCase						,      ):
									__a			:    List[Any]      							=	do_resize if do_resize is not None else self.do_resize
									__a			:    Tuple      							=	crop_pct if crop_pct is not None else self.crop_pct
									__a			:    List[Any]      							=	resample if resample is not None else self.resample
									__a			:    Tuple      							=	do_rescale if do_rescale is not None else self.do_rescale
									__a			:    List[str]      							=	rescale_factor if rescale_factor is not None else self.rescale_factor
									__a			:    Any      							=	do_normalize if do_normalize is not None else self.do_normalize
									__a			:    Optional[Any]      							=	image_mean if image_mean is not None else self.image_mean
									__a			:    List[Any]      							=	image_std if image_std is not None else self.image_std

									__a			:    List[Any]      							=	size if size is not None else self.size
									__a			:    Dict      							=	get_size_dict(A_						,      default_to_square=A_  )

									__a			:    int      							=	make_list_of_images(A_  )

									if not valid_images(A_  ):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

									if do_resize and size is None or resample is None:
													raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

									if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError('''crop_pct must be specified if size < 384.'''  )

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

									# All transformations expect numpy arrays.
									__a			:    List[Any]      							=	[to_numpy_array(A_  ) for image in images]

									if do_resize:
													__a			:    List[str]      							=	[self.resize(image=A_						,      size=A_						,      crop_pct=A_						,      resample=A_  ) for image in images]

									if do_rescale:
													__a			:    str      							=	[self.rescale(image=A_						,      scale=A_  ) for image in images]

									if do_normalize:
													__a			:    str      							=	[self.normalize(image=A_						,      mean=A_						,      std=A_  ) for image in images]

									__a			:    Optional[Any]      							=	[to_channel_dimension_format(A_						,      A_  ) for image in images]

									__a			:    Any      							=	{'''pixel_values''': images}
									return BatchFeature(data=A_						,      tensor_type=A_  )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
