



'''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 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Union[str, Any]  =      ["pixel_values"]




 def __init__(   self							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = PIL.Image.BICUBIC							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = 1 / 2_5_5							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      a_       		=size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      a_       		=get_size_dict(lowerCAmelCase_)
      a_       		=crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      a_       		=get_size_dict(lowerCAmelCase_							,  param_name="crop_size")

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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = PIL.Image.BICUBIC							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



      """simple docstring"""







      a_       		=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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



      """simple docstring"""







      a_       		=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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      List[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



      """simple docstring"""







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




 def       lowercase_  (   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_							,  ) ->      PIL.Image.Image:



      """simple docstring"""







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

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

      a_       		=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.
      a_       		=[to_numpy_array(lowerCAmelCase_) for image in images]

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

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

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

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

      a_       		=[to_channel_dimension_format(lowerCAmelCase_							,  lowerCAmelCase_) for image in images]

      a_       		={"pixel_values": images}
      return BatchFeature(data=lowerCAmelCase_							,  tensor_type=lowerCAmelCase_)







'''simple docstring'''


from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip


