


"""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, 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


__UpperCAmelCase    				=      logging.get_logger(__name__)

class  lowercase_  ( a_						):
      __magic_name__     :							Union[str, Any]          =				["""pixel_values"""]







      def __init__(      self :							str		,  _lowercase :							bool = True		,  _lowercase :							Dict[str, int] = None		,  _lowercase :							PILImageResampling = PIL.Image.NEAREST		,  _lowercase :							bool = False		,  _lowercase :							Dict[str, int] = None		,  _lowercase :							Union[int, float] = 1 / 2_5_5		,  _lowercase :							bool = False		,  _lowercase :							bool = True		,  _lowercase :							bool = True		,  _lowercase :							Optional[Union[float, List[float]]] = None		,  _lowercase :							Optional[Union[float, List[float]]] = None		,  _lowercase :							bool = True		,  **_lowercase :							Dict		,  ):
        super().__init__(**_lowercase    )
        lowerCAmelCase__    :    Optional[Any]								=					size if size is not None else {"height": 3_4_6, "width": 3_4_6}
        lowerCAmelCase__    :    int								=					get_size_dict(_lowercase    )
        lowerCAmelCase__    :    str								=					crop_size if crop_size is not None else {"height": 2_8_9, "width": 2_8_9}
        lowerCAmelCase__    :    int								=					get_size_dict(_lowercase		,  param_name="crop_size"    )

        lowerCAmelCase__    :    Optional[int]								=					do_resize
        lowerCAmelCase__    :    str								=					size
        lowerCAmelCase__    :    Tuple								=					resample
        lowerCAmelCase__    :    str								=					do_center_crop
        lowerCAmelCase__    :    List[Any]								=					crop_size
        lowerCAmelCase__    :    List[Any]								=					do_rescale
        lowerCAmelCase__    :    List[Any]								=					rescale_factor
        lowerCAmelCase__    :    int								=					rescale_offset
        lowerCAmelCase__    :    Tuple								=					do_normalize
        lowerCAmelCase__    :    List[Any]								=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase__    :    Optional[int]								=					image_std if image_std is not None else IMAGENET_STANDARD_STD
        lowerCAmelCase__    :    Dict								=					include_top







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							np.ndarray		,  _lowercase :							Dict[str, int]		,  _lowercase :							PILImageResampling = PIL.Image.NEAREST		,  _lowercase :							Optional[Union[str, ChannelDimension]] = None		,  **_lowercase :							List[str]		,  ):
        lowerCAmelCase__    :    Union[str, Any]								=					get_size_dict(_lowercase    )
        if "height" not in size or "width" not in size:
          raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"    )
        return resize(
            _lowercase		,  size=(size["height"], size["width"])		,  resample=_lowercase		,  data_format=_lowercase		,  **_lowercase    )







      def 				_lowerCAmelCase       (      self :							Tuple		,  _lowercase :							np.ndarray		,  _lowercase :							Dict[str, int]		,  _lowercase :							Optional[Union[str, ChannelDimension]] = None		,  **_lowercase :							List[Any]		,  ):
        lowerCAmelCase__    :    Union[str, Any]								=					get_size_dict(_lowercase    )
        if "height" not in size or "width" not in size:
          raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"    )
        return center_crop(_lowercase		,  size=(size["height"], size["width"])		,  data_format=_lowercase		,  **_lowercase    )







      def 				_lowerCAmelCase       (      self :							Any		,  _lowercase :							np.ndarray		,  _lowercase :							Union[int, float]		,  _lowercase :							bool = True		,  _lowercase :							Optional[Union[str, ChannelDimension]] = None		,  **_lowercase :							Optional[Any]		,  ):
        if offset:
          lowerCAmelCase__    :    Optional[Any]								=					(image - 127.5) * scale
          if data_format is not None:
            lowerCAmelCase__    :    List[str]								=					to_channel_dimension_format(_lowercase		,  _lowercase    )
          lowerCAmelCase__    :    Any								=					rescaled_image.astype(np.floataa    )
        else:
          lowerCAmelCase__    :    str								=					rescale(_lowercase		,  scale=_lowercase		,  data_format=_lowercase		,  **_lowercase    )
        return rescaled_image







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							np.ndarray		,  _lowercase :							Union[float, List[float]]		,  _lowercase :							Union[float, List[float]]		,  _lowercase :							Optional[Union[str, ChannelDimension]] = None		,  **_lowercase :							str		,  ):
        return normalize(_lowercase		,  mean=_lowercase		,  std=_lowercase		,  data_format=_lowercase		,  **_lowercase    )







      def 				_lowerCAmelCase       (      self :							Tuple		,  _lowercase :							ImageInput		,  _lowercase :							bool = None		,  _lowercase :							Dict[str, int] = None		,  _lowercase :							Dict=None		,  _lowercase :							bool = None		,  _lowercase :							Dict[str, int] = None		,  _lowercase :							bool = None		,  _lowercase :							float = None		,  _lowercase :							bool = None		,  _lowercase :							bool = None		,  _lowercase :							Optional[Union[float, List[float]]] = None		,  _lowercase :							Optional[Union[float, List[float]]] = None		,  _lowercase :							bool = None		,  _lowercase :							Optional[Union[str, TensorType]] = None		,  _lowercase :							ChannelDimension = ChannelDimension.FIRST		,  **_lowercase :							int		,  ):
        lowerCAmelCase__    :    Any								=					do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__    :    Optional[int]								=					resample if resample is not None else self.resample
        lowerCAmelCase__    :    List[str]								=					do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase__    :    Optional[int]								=					do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__    :    str								=					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__    :    Tuple								=					rescale_offset if rescale_offset is not None else self.rescale_offset
        lowerCAmelCase__    :    Union[str, Any]								=					do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__    :    str								=					image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__    :    Union[str, Any]								=					image_std if image_std is not None else self.image_std
        lowerCAmelCase__    :    Union[str, Any]								=					include_top if include_top is not None else self.include_top

        lowerCAmelCase__    :    Tuple								=					size if size is not None else self.size
        lowerCAmelCase__    :    Dict								=					get_size_dict(_lowercase    )
        lowerCAmelCase__    :    str								=					crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase__    :    List[Any]								=					get_size_dict(_lowercase		,  param_name="crop_size"    )

        lowerCAmelCase__    :    Optional[Any]								=					make_list_of_images(_lowercase    )

        if not valid_images(_lowercase    ):
          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_center_crop and crop_size is None:
          raise ValueError("Crop size must be specified if do_center_crop is True."    )

        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.
        lowerCAmelCase__    :    Any								=					[to_numpy_array(_lowercase    ) for image in images]

        if do_resize:
          lowerCAmelCase__    :    Any								=					[self.resize(image=_lowercase		,  size=_lowercase		,  resample=_lowercase    ) for image in images]

        if do_center_crop:
          lowerCAmelCase__    :    Union[str, Any]								=					[self.center_crop(image=_lowercase		,  size=_lowercase    ) for image in images]

        if do_rescale:
          lowerCAmelCase__    :    Optional[Any]								=					[self.rescale(image=_lowercase		,  scale=_lowercase		,  offset=_lowercase    ) for image in images]

        if do_normalize:
          lowerCAmelCase__    :    List[Any]								=					[self.normalize(image=_lowercase		,  mean=_lowercase		,  std=_lowercase    ) for image in images]

        if include_top:
          lowerCAmelCase__    :    str								=					[self.normalize(image=_lowercase		,  mean=[0, 0, 0]		,  std=_lowercase    ) for image in images]

        lowerCAmelCase__    :    Any								=					[to_channel_dimension_format(_lowercase		,  _lowercase    ) for image in images]

        lowerCAmelCase__    :    Any								=					{"pixel_values": images}
        return BatchFeature(data=_lowercase		,  tensor_type=_lowercase    )




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase    				=      {"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SEWDForCTC",
       "SEWDForSequenceClassification",
       "SEWDModel",
       "SEWDPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
