




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowercase__				:      Optional[int]	   = logging.get_logger(__name__)




class       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     ["""pixel_values"""]







     def __init__(     self  ,		A = True  ,		A = 1 / 255  ,		A = True  ,		A = 8  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          super().__init__(**A			)

          a     =				do_rescale
          a     =				rescale_factor
          a     =				do_pad
          a     =				pad_size







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



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A = None			)  ->   Tuple:



          '''simple docstring'''



          a  ,				a     =				get_image_size(A			)
          a     =				(old_height // size + 1) * size - old_height
          a     =				(old_width // size + 1) * size - old_width

          return pad(A  ,		((0, pad_height), (0, pad_width))  ,		mode="symmetric"  ,		data_format=A			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = ChannelDimension.FIRST  ,		**A  ,		)  ->   Any:



          '''simple docstring'''



          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_pad if do_pad is not None else self.do_pad
          a     =				pad_size if pad_size is not None else self.pad_size

          a     =				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_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."			)

          # All transformations expect numpy arrays.
          a     =				[to_numpy_array(A			) for image in images]

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

          if do_pad:
               a     =				[self.pad(A  ,		size=A			) for image in images]

          a     =				[to_channel_dimension_format(A  ,		A			) for image in images]

          a     =				{"pixel_values": images}
          return BatchFeature(data=A  ,		tensor_type=A			)







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

