'''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


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Any      =       ['''pixel_values''']



			def __init__(   self       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = PIL.Image.BICUBIC       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = 1 / 255       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase   					=  size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase   					=  get_size_dict(__lowerCAmelCase)
					lowerCAmelCase   					=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
					lowerCAmelCase   					=  get_size_dict(__lowerCAmelCase       ,    param_name="""crop_size""")

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = PIL.Image.BICUBIC       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  get_size_dict(__lowerCAmelCase)
					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(
					    __lowerCAmelCase       ,    size=(size["""height"""], size["""width"""])       ,    resample=__lowerCAmelCase       ,    data_format=__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  get_size_dict(__lowerCAmelCase)
					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(__lowerCAmelCase       ,    size=(size["""height"""], size["""width"""])       ,    data_format=__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


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







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = ChannelDimension.FIRST       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


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

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

					lowerCAmelCase   					=  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_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.
					lowerCAmelCase   					=  [to_numpy_array(__lowerCAmelCase) for image in images]

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

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

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

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

					lowerCAmelCase   					=  [to_channel_dimension_format(__lowerCAmelCase       ,    __lowerCAmelCase) for image in images]

					lowerCAmelCase   					=  {"""pixel_values""": images}
					return BatchFeature(data=__lowerCAmelCase       ,    tensor_type=__lowerCAmelCase)





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					warnings.warn(
					    """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use DeformableDetrImageProcessor instead."""       ,    __lowerCAmelCase       ,    )
					super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase)




