

'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCamelCase		       =    logging.get_logger(__name__)


if is_vision_available():
      import PIL


class 					A__      (      _snake_case			):
    lowercase						=		["pixel_values"]



    def __init__(					self		,				UpperCamelCase__ = True		,				UpperCamelCase__ = None		,				UpperCamelCase__ = PILImageResampling.BICUBIC		,				UpperCamelCase__ = True		,				UpperCamelCase__ = None		,				UpperCamelCase__ = True		,				UpperCamelCase__ = 1 / 255		,				UpperCamelCase__ = True		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = True		,				**UpperCamelCase__		,				)						->      None:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )
           A_						=      size if size is not None else {"""shortest_edge""": 224}
           A_						=      get_size_dict(UpperCamelCase__		,				default_to_square=UpperCamelCase__       )
           A_						=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           A_						=      get_size_dict(UpperCamelCase__		,				default_to_square=UpperCamelCase__		,				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 OPENAI_CLIP_MEAN
           A_						=      image_std if image_std is not None else OPENAI_CLIP_STD
           A_						=      do_convert_rgb



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = PILImageResampling.BICUBIC		,				UpperCamelCase__ = None		,				**UpperCamelCase__		,				)						->      np.ndarray:





           '''simple docstring'''
           A_						=      get_size_dict(UpperCamelCase__		,				default_to_square=UpperCamelCase__       )
           if "shortest_edge" not in size:
                  raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
           A_						=      get_resize_output_image_size(UpperCamelCase__		,				size=size["""shortest_edge"""]		,				default_to_square=UpperCamelCase__       )
           return resize(UpperCamelCase__		,				size=UpperCamelCase__		,				resample=UpperCamelCase__		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				**UpperCamelCase__		,				)						->      np.ndarray:





           '''simple docstring'''
           A_						=      get_size_dict(UpperCamelCase__       )
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
           return center_crop(UpperCamelCase__		,				size=(size["""height"""], size["""width"""])		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				**UpperCamelCase__		,				)						->      Any:





           '''simple docstring'''
           return rescale(UpperCamelCase__		,				scale=UpperCamelCase__		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				**UpperCamelCase__		,				)						->      np.ndarray:





           '''simple docstring'''
           return normalize(UpperCamelCase__		,				mean=UpperCamelCase__		,				std=UpperCamelCase__		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = ChannelDimension.FIRST		,				**UpperCamelCase__		,				)						->      PIL.Image.Image:





           '''simple docstring'''
           A_						=      do_resize if do_resize is not None else self.do_resize
           A_						=      size if size is not None else self.size
           A_						=      get_size_dict(UpperCamelCase__		,				param_name="""size"""		,				default_to_square=UpperCamelCase__       )
           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_						=      crop_size if crop_size is not None else self.crop_size
           A_						=      get_size_dict(UpperCamelCase__		,				param_name="""crop_size"""		,				default_to_square=UpperCamelCase__       )
           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_						=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           A_						=      make_list_of_images(UpperCamelCase__       )

           if not valid_images(UpperCamelCase__       ):
                  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:
                  raise ValueError("""Size 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."""       )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  A_						=      [convert_to_rgb(UpperCamelCase__       ) for image in images]

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

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

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

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

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

           A_						=      [to_channel_dimension_format(UpperCamelCase__		,				UpperCamelCase__       ) for image in images]

           A_						=      {"""pixel_values""": images}
           return BatchFeature(data=UpperCamelCase__		,				tensor_type=UpperCamelCase__       )



'''simple docstring'''







import warnings

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


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      None:





           '''simple docstring'''
           warnings.warn(
               """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
               """ use BeitImageProcessor instead."""		,				UpperCamelCase__		,				)
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
