



'''simple docstring'''






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


A_				:					Tuple							=      logging.get_logger(__name__)



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    ["""pixel_values"""]







 def __init__(				self    ,a_ = True    ,a_ = None    ,a_ = PIL.Image.NEAREST    ,a_ = False    ,a_ = None    ,a_ = 1 / 255    ,a_ = False    ,a_ = True    ,a_ = True    ,a_ = None    ,a_ = None    ,a_ = True    ,**a_    ,)						->  None:
        super().__init__(**a_				)
        _UpperCAmelCase    :		List[Any]     	=					size if size is not None else {"""height""": 346, """width""": 346}
        _UpperCAmelCase    :		Optional[Any]     	=					get_size_dict(a_				)
        _UpperCAmelCase    :		Tuple     	=					crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
        _UpperCAmelCase    :		int     	=					get_size_dict(a_    ,param_name="""crop_size"""				)

        _UpperCAmelCase    :		Tuple     	=					do_resize
        _UpperCAmelCase    :		Optional[Any]     	=					size
        _UpperCAmelCase    :		int     	=					resample
        _UpperCAmelCase    :		Optional[int]     	=					do_center_crop
        _UpperCAmelCase    :		List[Any]     	=					crop_size
        _UpperCAmelCase    :		Tuple     	=					do_rescale
        _UpperCAmelCase    :		List[Any]     	=					rescale_factor
        _UpperCAmelCase    :		Optional[int]     	=					rescale_offset
        _UpperCAmelCase    :		Any     	=					do_normalize
        _UpperCAmelCase    :		Union[str, Any]     	=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCAmelCase    :		Any     	=					image_std if image_std is not None else IMAGENET_STANDARD_STD
        _UpperCAmelCase    :		Tuple     	=					include_top







 def _snake_case					(				self    ,a_    ,a_    ,a_ = PIL.Image.NEAREST    ,a_ = None    ,**a_    ,)						->  np.ndarray:
        _UpperCAmelCase    :		Union[str, Any]     	=					get_size_dict(a_				)
        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(
            a_    ,size=(size["""height"""], size["""width"""])    ,resample=a_    ,data_format=a_    ,**a_				)







 def _snake_case					(				self    ,a_    ,a_    ,a_ = None    ,**a_    ,)						->  np.ndarray:
        _UpperCAmelCase    :		str     	=					get_size_dict(a_				)
        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(a_    ,size=(size["""height"""], size["""width"""])    ,data_format=a_    ,**a_				)







 def _snake_case					(				self    ,a_    ,a_    ,a_ = True    ,a_ = None    ,**a_    ,)						->  List[str]:
        if offset:
               _UpperCAmelCase    :		int     	=					(image - 127.5) * scale
               if data_format is not None:
                      _UpperCAmelCase    :		Optional[Any]     	=					to_channel_dimension_format(a_    ,a_				)
               _UpperCAmelCase    :		Optional[Any]     	=					rescaled_image.astype(np.floataa				)
        else:
               _UpperCAmelCase    :		int     	=					rescale(a_    ,scale=a_    ,data_format=a_    ,**a_				)
        return rescaled_image







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_ = None    ,**a_    ,)						->  np.ndarray:
        return normalize(a_    ,mean=a_    ,std=a_    ,data_format=a_    ,**a_				)







 def _snake_case					(				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_ = None    ,a_ = ChannelDimension.FIRST    ,**a_    ,)						->  PIL.Image.Image:
        _UpperCAmelCase    :		Dict     	=					do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase    :		Optional[Any]     	=					resample if resample is not None else self.resample
        _UpperCAmelCase    :		Any     	=					do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase    :		Optional[int]     	=					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    :		List[str]     	=					rescale_offset if rescale_offset is not None else self.rescale_offset
        _UpperCAmelCase    :		Union[str, Any]     	=					do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase    :		str     	=					image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase    :		Union[str, Any]     	=					image_std if image_std is not None else self.image_std
        _UpperCAmelCase    :		Optional[int]     	=					include_top if include_top is not None else self.include_top

        _UpperCAmelCase    :		Optional[int]     	=					size if size is not None else self.size
        _UpperCAmelCase    :		Dict     	=					get_size_dict(a_				)
        _UpperCAmelCase    :		str     	=					crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase    :		List[Any]     	=					get_size_dict(a_    ,param_name="""crop_size"""				)

        _UpperCAmelCase    :		Dict     	=					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 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.
        _UpperCAmelCase    :		Union[str, Any]     	=					[to_numpy_array(a_				) for image in images]

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

        if do_center_crop:
               _UpperCAmelCase    :		Tuple     	=					[self.center_crop(image=a_    ,size=a_				) for image in images]

        if do_rescale:
               _UpperCAmelCase    :		int     	=					[self.rescale(image=a_    ,scale=a_    ,offset=a_				) for image in images]

        if do_normalize:
               _UpperCAmelCase    :		Tuple     	=					[self.normalize(image=a_    ,mean=a_    ,std=a_				) for image in images]

        if include_top:
               _UpperCAmelCase    :		Tuple     	=					[self.normalize(image=a_    ,mean=[0, 0, 0]    ,std=a_				) for image in images]

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

        _UpperCAmelCase    :		Union[str, Any]     	=					{"""pixel_values""": images}
        return BatchFeature(data=a_    ,tensor_type=a_				)





"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_  					= {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DeformableDetrForObjectDetection',
      'DeformableDetrModel',
      'DeformableDetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
    from .image_processing_deformable_detr import DeformableDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deformable_detr import (
        DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeformableDetrForObjectDetection,
        DeformableDetrModel,
        DeformableDetrPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE_  					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

