






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__							=     logging.get_logger(__name__)


if is_vision_available():
     import PIL




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				int 				= ["pixel_values"]



       def __init__(							self			,						a__ = True			,						a__ = None			,						a__ = PILImageResampling.BICUBIC			,						a__ = True			,						a__ = None			,						a__ = True			,						a__ = 1 / 2_55			,						a__ = True			,						a__ = None			,						a__ = None			,						a__ = True			,						**a__			,						) ->      None:






            '''simple docstring'''





            super().__init__(**a__    )
            __snake_case			:Tuple          =						size if size is not None else {"""shortest_edge""": 2_24}
            __snake_case			:Any          =						get_size_dict(a__			,						default_to_square=a__    )
            __snake_case			:str          =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            __snake_case			:Optional[int]          =						get_size_dict(a__			,						default_to_square=a__			,						param_name="""crop_size"""    )

            __snake_case			:Optional[Any]          =						do_resize
            __snake_case			:List[str]          =						size
            __snake_case			:List[Any]          =						resample
            __snake_case			:str          =						do_center_crop
            __snake_case			:List[str]          =						crop_size
            __snake_case			:Optional[Any]          =						do_rescale
            __snake_case			:Optional[int]          =						rescale_factor
            __snake_case			:Dict          =						do_normalize
            __snake_case			:int          =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            __snake_case			:List[str]          =						image_std if image_std is not None else OPENAI_CLIP_STD
            __snake_case			:List[Any]          =						do_convert_rgb



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = PILImageResampling.BICUBIC			,						a__ = None			,						**a__			,						) ->      np.ndarray:






            '''simple docstring'''





            __snake_case			:List[str]          =						get_size_dict(a__			,						default_to_square=a__    )
            if "shortest_edge" not in size:
                 raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
            __snake_case			:Optional[Any]          =						get_resize_output_image_size(a__			,						size=size["""shortest_edge"""]			,						default_to_square=a__    )
            return resize(a__			,						size=a__			,						resample=a__			,						data_format=a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = None			,						**a__			,						) ->      np.ndarray:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						get_size_dict(a__    )
            if "height" not in size or "width" not in size:
                 raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
            return center_crop(a__			,						size=(size["""height"""], size["""width"""])			,						data_format=a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = None			,						**a__			,						) ->      int:






            '''simple docstring'''





            return rescale(a__			,						scale=a__			,						data_format=a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__ = None			,						**a__			,						) ->      np.ndarray:






            '''simple docstring'''





            return normalize(a__			,						mean=a__			,						std=a__			,						data_format=a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = ChannelDimension.FIRST			,						**a__			,						) ->      PIL.Image.Image:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						do_resize if do_resize is not None else self.do_resize
            __snake_case			:Dict          =						size if size is not None else self.size
            __snake_case			:int          =						get_size_dict(a__			,						param_name="""size"""			,						default_to_square=a__    )
            __snake_case			:List[Any]          =						resample if resample is not None else self.resample
            __snake_case			:List[str]          =						do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case			:Tuple          =						crop_size if crop_size is not None else self.crop_size
            __snake_case			:Optional[int]          =						get_size_dict(a__			,						param_name="""crop_size"""			,						default_to_square=a__    )
            __snake_case			:Optional[Any]          =						do_rescale if do_rescale is not None else self.do_rescale
            __snake_case			:Optional[Any]          =						rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case			:Optional[int]          =						do_normalize if do_normalize is not None else self.do_normalize
            __snake_case			:List[Any]          =						image_mean if image_mean is not None else self.image_mean
            __snake_case			:Tuple          =						image_std if image_std is not None else self.image_std
            __snake_case			:Optional[Any]          =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            __snake_case			:List[Any]          =						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:
                 raise ValueError("""Size 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."""    )

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                 __snake_case			:Any          =						[convert_to_rgb(a__    ) for image in images]

            # All transformations expect numpy arrays.
            __snake_case			:List[str]          =						[to_numpy_array(a__    ) for image in images]

            if do_resize:
                 __snake_case			:str          =						[self.resize(image=a__			,						size=a__			,						resample=a__    ) for image in images]

            if do_center_crop:
                 __snake_case			:int          =						[self.center_crop(image=a__			,						size=a__    ) for image in images]

            if do_rescale:
                 __snake_case			:List[Any]          =						[self.rescale(image=a__			,						scale=a__    ) for image in images]

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

            __snake_case			:Any          =						[to_channel_dimension_format(a__			,						a__    ) for image in images]

            __snake_case			:List[str]          =						{"""pixel_values""": images}
            return BatchFeature(data=a__			,						tensor_type=a__    )







from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

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


