




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


lowercase__							:	str											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''pixel_values''']






						def __init__(		self				:	int			,      __lowercase				:	bool = True			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	PILImageResampling = PIL.Image.BICUBIC			,      __lowercase				:	bool = True			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	Union[int, float] = 1 / 2_55			,      __lowercase				:	bool = True			,      __lowercase				:	bool = True			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      **__lowercase				:	str			,      ):






							"""simple docstring"""






							super().__init__(**__lowercase		)
							snake_case_						       =	size if size is not None else {"height": 2_56, "width": 2_56}
							snake_case_						       =	get_size_dict(__lowercase		)
							snake_case_						       =	crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
							snake_case_						       =	get_size_dict(__lowercase			,      param_name="crop_size"		)

							snake_case_						       =	do_resize
							snake_case_						       =	size
							snake_case_						       =	resample
							snake_case_						       =	do_center_crop
							snake_case_						       =	crop_size
							snake_case_						       =	do_rescale
							snake_case_						       =	rescale_factor
							snake_case_						       =	do_normalize
							snake_case_						       =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							snake_case_						       =	image_std if image_std is not None else IMAGENET_STANDARD_STD






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	np.ndarray			,      __lowercase				:	Dict[str, int]			,      __lowercase				:	PILImageResampling = PIL.Image.BICUBIC			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	int			,      ):






							"""simple docstring"""






							snake_case_						       =	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 			snake_case__		(		self				:	Dict			,      __lowercase				:	np.ndarray			,      __lowercase				:	Dict[str, int]			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	Optional[int]			,      ):






							"""simple docstring"""






							snake_case_						       =	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 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	np.ndarray			,      __lowercase				:	Union[int, float]			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	List[str]			,      ):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	np.ndarray			,      __lowercase				:	Union[float, List[float]]			,      __lowercase				:	Union[float, List[float]]			,      __lowercase				:	Optional[Union[str, ChannelDimension]] = None			,      **__lowercase				:	Dict			,      ):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	ImageInput			,      __lowercase				:	bool = None			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	int=None			,      __lowercase				:	bool = None			,      __lowercase				:	Dict[str, int] = None			,      __lowercase				:	bool = None			,      __lowercase				:	float = None			,      __lowercase				:	bool = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[float, List[float]]] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	ChannelDimension = ChannelDimension.FIRST			,      **__lowercase				:	Optional[int]			,      ):






							"""simple docstring"""






							snake_case_						       =	do_resize if do_resize is not None else self.do_resize
							snake_case_						       =	resample if resample is not None else self.resample
							snake_case_						       =	do_center_crop if do_center_crop is not None else self.do_center_crop
							snake_case_						       =	do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						       =	rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_						       =	do_normalize if do_normalize is not None else self.do_normalize
							snake_case_						       =	image_mean if image_mean is not None else self.image_mean
							snake_case_						       =	image_std if image_std is not None else self.image_std

							snake_case_						       =	size if size is not None else self.size
							snake_case_						       =	get_size_dict(__lowercase		)
							snake_case_						       =	crop_size if crop_size is not None else self.crop_size
							snake_case_						       =	get_size_dict(__lowercase			,      param_name="crop_size"		)

							snake_case_						       =	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.
							snake_case_						       =	[to_numpy_array(__lowercase		) for image in images]

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

							if do_center_crop:
								snake_case_						       =	[self.center_crop(image=__lowercase			,      size=__lowercase		) for image in images]

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

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

							snake_case_						       =	[to_channel_dimension_format(__lowercase			,      __lowercase		) for image in images]

							snake_case_						       =	{"pixel_values": images}
							return BatchFeature(data=__lowercase			,      tensor_type=__lowercase		)






from typing import TYPE_CHECKING

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


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "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

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
