

"""simple docstring"""






from typing import TYPE_CHECKING

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


__lowerCamelCase										=		{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		["Mask2FormerImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		[
     "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Mask2FormerForUniversalSegmentation",
     "Mask2FormerModel",
     "Mask2FormerPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_maskaformer import MaskaFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskaformer import (
      MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskaFormerForUniversalSegmentation,
      MaskaFormerModel,
      MaskaFormerPreTrainedModel,
  )


else:
 import sys

 __lowerCamelCase										=		_LazyModule(__name__, globals()["__file__"], _import_structure)




"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 32 ,						UpperCamelCase_=PILImageResampling.BILINEAR ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize
      __magic_name__     =      do_rescale
      __magic_name__     =      size_divisor
      __magic_name__     =      resample
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      get_image_size(UpperCamelCase_       )
      # Rounds the height and width down to the closest multiple of size_divisor
      __magic_name__     =      height // size_divisor * size_divisor
      __magic_name__     =      width // size_divisor * size_divisor
      __magic_name__     =      resize(UpperCamelCase_ ,						(new_h, new_w) ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      return rescale(image=UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_=None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      size_divisor if size_divisor is not None else self.size_divisor
      __magic_name__     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
       raise ValueError('''size_divisor is required for resizing'''       )

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       raise ValueError('''Invalid image(s)'''       )

      # All transformations expect numpy arrays.
      __magic_name__     =      [to_numpy_array(UpperCamelCase_       ) for img in images]

      if do_resize:
       __magic_name__     =      [self.resize(UpperCamelCase_ ,						size_divisor=UpperCamelCase_ ,						resample=UpperCamelCase_       ) for image in images]

      if do_rescale:
       __magic_name__     =      [self.rescale(UpperCamelCase_ ,						scale=1 / 255       ) for image in images]

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

