

'''simple docstring'''




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowerCAmelCase         =						logging.get_logger(__name__)






class   _SCREAMING_SNAKE_CASE   (   UpperCamelCase__     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					["""pixel_values"""]



 def __init__(   self		:   Optional[Any]			,				a__		:   List[str] = True			,				a__		:   Any = 1 / 255			,				a__		:   Tuple = True			,				a__		:   List[Any] = 8			,				**a__		:   Dict			,				):
      super().__init__(**__lowerCamelCase      )

      __magic_name__						       =    do_rescale
      __magic_name__						       =    rescale_factor
      __magic_name__						       =    do_pad
      __magic_name__						       =    pad_size



 def 			snake_case__  (   self		:   str			,				a__		:   Dict			,				a__		:   Optional[int]			,				a__		:   List[str] = None			,				**a__		:   List[Any]      ):
      return rescale(__lowerCamelCase			,				scale=__lowerCamelCase			,				data_format=__lowerCamelCase			,				**__lowerCamelCase      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Optional[int]			,				a__		:   Tuple			,				a__		:   Dict = None      ):
      __magic_name__       ,  __magic_name__						       =    get_image_size(__lowerCamelCase      )
      __magic_name__						       =    (old_height // size + 1) * size - old_height
      __magic_name__						       =    (old_width // size + 1) * size - old_width

      return pad(__lowerCamelCase			,				((0, pad_height), (0, pad_width))			,				mode='''symmetric'''			,				data_format=__lowerCamelCase      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Dict			,				a__		:   Dict = None			,				a__		:   Union[str, Any] = None			,				a__		:   Optional[Any] = None			,				a__		:   Any = None			,				a__		:   Tuple = None			,				a__		:   List[Any] = ChannelDimension.FIRST			,				**a__		:   str			,				):
      __magic_name__						       =    do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
      __magic_name__						       =    do_pad if do_pad is not None else self.do_pad
      __magic_name__						       =    pad_size if pad_size is not None else self.pad_size

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

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

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

      if do_pad:
           __magic_name__						       =    [self.pad(__lowerCamelCase			,				size=__lowerCamelCase      ) for image in images]

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

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









'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase         =						{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						["GLPNFeatureExtractor"]
      _lowerCAmelCase         =						["GLPNImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GLPNForDepthEstimation",
          "GLPNLayer",
          "GLPNModel",
          "GLPNPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_glpn import GLPNFeatureExtractor
            from .image_processing_glpn import GLPNImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_glpn import (
                GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                GLPNForDepthEstimation,
                GLPNLayer,
                GLPNModel,
                GLPNPreTrainedModel,
            )


else:
      import sys

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






