





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


UpperCAmelCase	:	Dict		     =       logging.get_logger(__name__)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			["""pixel_values"""]







      def __init__(    self		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = 1 / 2_5_5		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = 8		,      **lowerCAmelCase__		,      )   ->						None:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__	:					Optional[Any]										=do_rescale
        a__	:					Dict										=rescale_factor
        a__	:					Optional[Any]										=do_pad
        a__	:					Tuple										=pad_size







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						np.ndarray:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						str:



        '''simple docstring'''




        a__					,							a__	:					Tuple										=get_image_size(lowerCAmelCase__							)
        a__	:					int										=(old_height // size + 1) * size - old_height
        a__	:					str										=(old_width // size + 1) * size - old_width

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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = ChannelDimension.FIRST		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




        a__	:					List[Any]										=do_rescale if do_rescale is not None else self.do_rescale
        a__	:					Dict										=rescale_factor if rescale_factor is not None else self.rescale_factor
        a__	:					Tuple										=do_pad if do_pad is not None else self.do_pad
        a__	:					Tuple										=pad_size if pad_size is not None else self.pad_size

        a__	:					List[Any]										=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_rescale and rescale_factor is None:
          raise ValueError("Rescale factor must be specified if do_rescale is True."							)

        # All transformations expect numpy arrays.
        a__	:					List[str]										=[to_numpy_array(lowerCAmelCase__							) for image in images]

        if do_rescale:
          a__	:					Tuple										=[self.rescale(image=lowerCAmelCase__		,      scale=lowerCAmelCase__							) for image in images]

        if do_pad:
          a__	:					List[Any]										=[self.pad(lowerCAmelCase__		,      size=lowerCAmelCase__							) for image in images]

        a__	:					int										=[to_channel_dimension_format(lowerCAmelCase__		,      lowerCAmelCase__							) for image in images]

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










'''simple docstring'''







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

A__:    Tuple					    =       [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A__:    Any					    =       {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


