

from typing import TYPE_CHECKING

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


lowercase     :       Union[str, Any]        =       {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       int        =       ["DetrFeatureExtractor"]
					lowercase     :       Union[str, Any]        =       ["DetrImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Tuple        =       [
					    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DetrForObjectDetection",
					    "DetrForSegmentation",
					    "DetrModel",
					    "DetrPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_detr import DetrFeatureExtractor
										from .image_processing_detr import DetrImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_detr import (
										    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DetrForObjectDetection,
										    DetrForSegmentation,
										    DetrModel,
										    DetrPreTrainedModel,
										)

else:
					import sys

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


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowercase     :       str        =       logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				str											=							['pixel_values']

						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )							->	None:




									'''simple docstring'''


									super().__init__(**__UpperCamelCase    )

									__UpperCamelCase    :    Optional[Any]								=      size if size is not None else {"height": 25_60, "width": 19_20}
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# The previous feature extractor size parameter was in (width, height) format
												__UpperCamelCase    :    Dict								=      size[::-1]
									__UpperCamelCase    :    Optional[int]								=      get_size_dict(__UpperCamelCase    )

									__UpperCamelCase    :    List[Any]								=      do_resize
									__UpperCamelCase    :    Tuple								=      size
									__UpperCamelCase    :    Optional[int]								=      resample
									__UpperCamelCase    :    Optional[Any]								=      do_thumbnail
									__UpperCamelCase    :    Any								=      do_align_long_axis
									__UpperCamelCase    :    str								=      do_pad
									__UpperCamelCase    :    str								=      do_rescale
									__UpperCamelCase    :    Union[str, Any]								=      rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize
									__UpperCamelCase    :    List[Any]								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else IMAGENET_STANDARD_STD

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      size["height"], size["width"]

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
												__UpperCamelCase    :    str								=      np.rotaa(__UpperCamelCase , 3    )

									if data_format is not None:
												__UpperCamelCase    :    str								=      to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    )

									return image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = None , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      size["height"], size["width"]
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      get_image_size(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      output_width - input_width
									__UpperCamelCase    :    Union[str, Any]								=      output_height - input_height

									if random_padding:
												__UpperCamelCase    :    Any								=      np.random.randint(low=0 , high=delta_height + 1    )
												__UpperCamelCase    :    Union[str, Any]								=      np.random.randint(low=0 , high=delta_width + 1    )
									else:
												__UpperCamelCase    :    Any								=      delta_height // 2
												__UpperCamelCase    :    Dict								=      delta_width // 2

									__UpperCamelCase    :    Optional[int]								=      delta_height - pad_top
									__UpperCamelCase    :    Dict								=      delta_width - pad_left

									__UpperCamelCase    :    str								=      ((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(__UpperCamelCase , __UpperCamelCase , data_format=__UpperCamelCase    )

						def __lowerCamelCase					(				self , *__UpperCamelCase , **__UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
									return self.pad_image(*__UpperCamelCase , **__UpperCamelCase    )

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




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      size["height"], size["width"]

									# We always resize to the smallest of either the input or output size.
									__UpperCamelCase    :    int								=      min(__UpperCamelCase , __UpperCamelCase    )
									__UpperCamelCase    :    List[str]								=      min(__UpperCamelCase , __UpperCamelCase    )

									if height == input_height and width == input_width:
												return image

									if input_height > input_width:
												__UpperCamelCase    :    List[Any]								=      int(input_width * height / input_height    )
									elif input_width > input_height:
												__UpperCamelCase    :    Tuple								=      int(input_height * width / input_width    )

									return resize(
									    __UpperCamelCase , size=(height, width) , resample=__UpperCamelCase , reducing_gap=2.0 , data_format=__UpperCamelCase , **__UpperCamelCase    )

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




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    str								=      min(size["height"] , size["width"]    )
									__UpperCamelCase    :    Tuple								=      get_resize_output_image_size(__UpperCamelCase , size=__UpperCamelCase , default_to_square=__UpperCamelCase    )
									__UpperCamelCase    :    List[Any]								=      resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )
									return resized_image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	Dict:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


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




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , )							->	PIL.Image.Image:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      do_resize if do_resize is not None else self.do_resize
									__UpperCamelCase    :    Any								=      size if size is not None else self.size
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# Previous feature extractor had size in (width, height) format
												__UpperCamelCase    :    List[Any]								=      size[::-1]
									__UpperCamelCase    :    List[Any]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      resample if resample is not None else self.resample
									__UpperCamelCase    :    Any								=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									__UpperCamelCase    :    List[Any]								=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									__UpperCamelCase    :    Optional[int]								=      do_pad if do_pad is not None else self.do_pad
									__UpperCamelCase    :    Dict								=      do_rescale if do_rescale is not None else self.do_rescale
									__UpperCamelCase    :    List[str]								=      rescale_factor if rescale_factor is not None else self.rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize if do_normalize is not None else self.do_normalize
									__UpperCamelCase    :    Union[str, Any]								=      image_mean if image_mean is not None else self.image_mean
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else self.image_std

									__UpperCamelCase    :    List[Any]								=      make_list_of_images(__UpperCamelCase    )

									if not valid_images(__UpperCamelCase    ):
												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_rescale and rescale_factor is None:
												raise ValueError("Rescale factor must be specified if do_rescale is True."    )

									if do_pad and size is None:
												raise ValueError("Size must be specified if do_pad 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    :    Union[str, Any]								=      [to_numpy_array(__UpperCamelCase    ) for image in images]

									if do_align_long_axis:
												__UpperCamelCase    :    Tuple								=      [self.align_long_axis(__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_resize:
												__UpperCamelCase    :    Optional[int]								=      [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase    ) for image in images]

									if do_thumbnail:
												__UpperCamelCase    :    Union[str, Any]								=      [self.thumbnail(image=__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_pad:
												__UpperCamelCase    :    Optional[Any]								=      [self.pad_image(image=__UpperCamelCase , size=__UpperCamelCase , random_padding=__UpperCamelCase    ) for image in images]

									if do_rescale:
												__UpperCamelCase    :    Optional[int]								=      [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase    ) for image in images]

									if do_normalize:
												__UpperCamelCase    :    Union[str, Any]								=      [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    List[str]								=      [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    int								=      {"pixel_values": images}
									return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase    )