"""simple docstring"""


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


UpperCAmelCase__			=    logging.get_logger(__name__)






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  def constraint_to_multiple_of(lowercase ,lowercase ,lowercase=0 ,lowercase=None		):
    _UpperCAmelCase    			=   round(val / multiple		) * multiple

    if max_val is not None and x > max_val:
      _UpperCAmelCase    			=   math.floor(val / multiple		) * multiple

    if x < min_val:
      _UpperCAmelCase    			=   math.ceil(val / multiple		) * multiple

    return x

  _UpperCAmelCase    			=   (output_size, output_size) if isinstance(lowercase ,lowercase		) else output_size

  _UpperCAmelCase			,					_UpperCAmelCase    			=   get_image_size(lowercase		)
  _UpperCAmelCase			,					_UpperCAmelCase    			=   output_size

  # determine new height and width
  _UpperCAmelCase    			=   output_height / input_height
  _UpperCAmelCase    			=   output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width		) < abs(1 - scale_height		):
      # fit width
      _UpperCAmelCase    			=   scale_width
    else:
      # fit height
      _UpperCAmelCase    			=   scale_height

  _UpperCAmelCase    			=   constraint_to_multiple_of(scale_height * input_height ,multiple=lowercase		)
  _UpperCAmelCase    			=   constraint_to_multiple_of(scale_width * input_width ,multiple=lowercase		)

  return (new_height, new_width)


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[str]  				=				['pixel_values']


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Dict[str, int] = None ,					__lowerCAmelCase						:	PILImageResampling = PILImageResampling.BILINEAR ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	int = 1 ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Union[int, float] = 1 / 255 ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					**__lowerCAmelCase						:	List[Any] ,					):
      super().__init__(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   size if size is not None else {"""height""": 384, """width""": 384}
      _UpperCAmelCase    			=   get_size_dict(__lowerCAmelCase       )
      _UpperCAmelCase    			=   do_resize
      _UpperCAmelCase    			=   size
      _UpperCAmelCase    			=   keep_aspect_ratio
      _UpperCAmelCase    			=   ensure_multiple_of
      _UpperCAmelCase    			=   resample
      _UpperCAmelCase    			=   do_rescale
      _UpperCAmelCase    			=   rescale_factor
      _UpperCAmelCase    			=   do_normalize
      _UpperCAmelCase    			=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _UpperCAmelCase    			=   image_std if image_std is not None else IMAGENET_STANDARD_STD


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	np.ndarray ,					__lowerCAmelCase						:	Dict[str, int] ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	int = 1 ,					__lowerCAmelCase						:	PILImageResampling = PILImageResampling.BICUBIC ,					__lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None ,					**__lowerCAmelCase						:	Tuple ,					):
      _UpperCAmelCase    			=   get_size_dict(__lowerCAmelCase       )
      if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
      _UpperCAmelCase    			=   get_resize_output_image_size(
          __lowerCAmelCase ,					output_size=(size["""height"""], size["""width"""]) ,					keep_aspect_ratio=__lowerCAmelCase ,					multiple=__lowerCAmelCase ,					)
      return resize(__lowerCAmelCase ,					size=__lowerCAmelCase ,					resample=__lowerCAmelCase ,					data_format=__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	np.ndarray ,					__lowerCAmelCase						:	Union[int, float] ,					__lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None ,					**__lowerCAmelCase						:	str ,					):
      return rescale(__lowerCAmelCase ,					scale=__lowerCAmelCase ,					data_format=__lowerCAmelCase ,					**__lowerCAmelCase       )


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


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	ImageInput ,					__lowerCAmelCase						:	bool = None ,					__lowerCAmelCase						:	int = None ,					__lowerCAmelCase						:	bool = None ,					__lowerCAmelCase						:	int = None ,					__lowerCAmelCase						:	PILImageResampling = None ,					__lowerCAmelCase						:	bool = None ,					__lowerCAmelCase						:	float = None ,					__lowerCAmelCase						:	bool = None ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					__lowerCAmelCase						:	Optional[Union[str, TensorType]] = None ,					__lowerCAmelCase						:	ChannelDimension = ChannelDimension.FIRST ,					**__lowerCAmelCase						:	List[str] ,					):
      _UpperCAmelCase    			=   do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase    			=   size if size is not None else self.size
      _UpperCAmelCase    			=   get_size_dict(__lowerCAmelCase       )
      _UpperCAmelCase    			=   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
      _UpperCAmelCase    			=   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
      _UpperCAmelCase    			=   resample if resample is not None else self.resample
      _UpperCAmelCase    			=   do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase    			=   rescale_factor if rescale_factor is not None else self.rescale_factor
      _UpperCAmelCase    			=   do_normalize if do_normalize is not None else self.do_normalize
      _UpperCAmelCase    			=   image_mean if image_mean is not None else self.image_mean
      _UpperCAmelCase    			=   image_std if image_std is not None else self.image_std

      _UpperCAmelCase    			=   make_list_of_images(__lowerCAmelCase       )

      if not valid_images(__lowerCAmelCase       ):
        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_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.
      _UpperCAmelCase    			=   [to_numpy_array(__lowerCAmelCase       ) for image in images]

      if do_resize:
        _UpperCAmelCase    			=   [self.resize(image=__lowerCAmelCase ,					size=__lowerCAmelCase ,					resample=__lowerCAmelCase       ) for image in images]

      if do_rescale:
        _UpperCAmelCase    			=   [self.rescale(image=__lowerCAmelCase ,					scale=__lowerCAmelCase       ) for image in images]

      if do_normalize:
        _UpperCAmelCase    			=   [self.normalize(image=__lowerCAmelCase ,					mean=__lowerCAmelCase ,					std=__lowerCAmelCase       ) for image in images]

      _UpperCAmelCase    			=   [to_channel_dimension_format(__lowerCAmelCase ,					__lowerCAmelCase       ) for image in images]

      _UpperCAmelCase    			=   {"""pixel_values""": images}
      return BatchFeature(data=__lowerCAmelCase ,					tensor_type=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	List[Tuple] = None       ):
      _UpperCAmelCase    			=   outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
        if len(__lowerCAmelCase       ) != len(__lowerCAmelCase       ):
          raise ValueError(
              """Make sure that you pass in as many target sizes as the batch dimension of the logits"""       )

        if is_torch_tensor(__lowerCAmelCase       ):
          _UpperCAmelCase    			=   target_sizes.numpy()

        _UpperCAmelCase    			=   []

        for idx in range(len(__lowerCAmelCase       )       ):
          _UpperCAmelCase    			=   torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0       ) ,					size=target_sizes[idx] ,					mode="""bilinear""" ,					align_corners=__lowerCAmelCase       )
          _UpperCAmelCase    			=   resized_logits[0].argmax(dim=0       )
          semantic_segmentation.append(__lowerCAmelCase       )
      else:
        _UpperCAmelCase    			=   logits.argmax(dim=1       )
        _UpperCAmelCase    			=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

      return semantic_segmentation

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

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