


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


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["pixel_values"]


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

            _A      :       List[str]				    = do_rescale
            _A      :       Dict				    = rescale_factor
            _A      :       Any				    = do_pad
            _A      :       Union[str, Any]				    = pad_size


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase)			->				np.ndarray:
            return rescale(__lowerCamelCase ,						scale=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Any:
            _A    ,			_A      :       Optional[int]				    = get_image_size(__lowerCamelCase)
            _A      :       Dict				    = (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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = ChannelDimension.FIRST ,						**__lowerCamelCase ,						)			->				Union[str, Any]:
            _A      :       int				    = do_rescale if do_rescale is not None else self.do_rescale
            _A      :       List[str]				    = rescale_factor if rescale_factor is not None else self.rescale_factor
            _A      :       Union[str, Any]				    = do_pad if do_pad is not None else self.do_pad
            _A      :       Dict				    = pad_size if pad_size is not None else self.pad_size

            _A      :       Optional[int]				    = 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[Any]				    = [to_numpy_array(__lowerCamelCase) for image in images]

            if do_rescale:
                   _A      :       Optional[int]				    = [self.rescale(image=__lowerCamelCase ,						scale=__lowerCamelCase) for image in images]

            if do_pad:
                   _A      :       str				    = [self.pad(__lowerCamelCase ,						size=__lowerCamelCase) for image in images]

            _A      :       List[Any]				    = [to_channel_dimension_format(__lowerCamelCase ,						__lowerCamelCase) for image in images]

            _A      :       Optional[Any]				    = {"pixel_values": images}
            return BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase)




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				None:
            warnings.warn(
                "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use BeitImageProcessor instead." ,						__lowerCamelCase ,						)
            super().__init__(*__lowerCamelCase ,						**__lowerCamelCase)
