'''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


_A		:      Union[str, Any]						   =logging.get_logger(__name__)


if is_vision_available():
 import PIL




class _lowerCAmelCase			(	UpperCAmelCase__					):
      UpperCAmelCase_	:						Optional[int]	 =       ["""pixel_values"""]







      def __init__(  self  ,    lowercase = True  ,    lowercase = None  ,    lowercase = PILImageResampling.BICUBIC  ,    lowercase = True  ,    lowercase = None  ,    lowercase = True  ,    lowercase = 1 / 255  ,    lowercase = True  ,    lowercase = None  ,    lowercase = None  ,    lowercase = True  ,    **lowercase  ,    ):
        super().__init__(**__lowercase     )
        lowercase				:							Union[str, Any]									=   size if size is not None else {'''shortest_edge''': 224}
        lowercase				:							List[Any]									=   get_size_dict(__lowercase  ,    default_to_square=__lowercase     )
        lowercase				:							List[Any]									=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowercase				:							Optional[int]									=   get_size_dict(__lowercase     )

        lowercase				:							Optional[int]									=   do_resize
        lowercase				:							List[Any]									=   size
        lowercase				:							int									=   resample
        lowercase				:							str									=   do_center_crop
        lowercase				:							List[str]									=   crop_size
        lowercase				:							Dict									=   do_rescale
        lowercase				:							List[str]									=   rescale_factor
        lowercase				:							Dict									=   do_normalize
        lowercase				:							int									=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase				:							Optional[Any]									=   image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase				:							int									=   do_convert_rgb







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase = PILImageResampling.BICUBIC  ,    lowercase = None  ,    **lowercase  ,    ):
        lowercase				:							Tuple									=   get_size_dict(__lowercase  ,    default_to_square=__lowercase     )
        lowercase				:							List[str]									=   get_resize_output_image_size(
            __lowercase  ,    size=(size['''height'''], size['''width'''])  ,    default_to_square=__lowercase     )
        return resize(__lowercase  ,    size=__lowercase  ,    resample=__lowercase  ,    data_format=__lowercase  ,    **__lowercase     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase = None  ,    **lowercase  ,    ):
        lowercase				:							Optional[int]									=   get_size_dict(__lowercase     )
        return center_crop(__lowercase  ,    size=(size['''height'''], size['''width'''])  ,    data_format=__lowercase  ,    **__lowercase     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase = None  ,    **lowercase  ,    ):
        return rescale(__lowercase  ,    scale=__lowercase  ,    data_format=__lowercase  ,    **__lowercase     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase = None  ,    **lowercase  ,    ):
        return normalize(__lowercase  ,    mean=__lowercase  ,    std=__lowercase  ,    data_format=__lowercase  ,    **__lowercase     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = None  ,    lowercase = ChannelDimension.FIRST  ,    **lowercase  ,    ):
        lowercase				:							str									=   do_resize if do_resize is not None else self.do_resize
        lowercase				:							int									=   size if size is not None else self.size
        lowercase				:							Dict									=   get_size_dict(__lowercase  ,    default_to_square=__lowercase     )
        lowercase				:							Any									=   resample if resample is not None else self.resample
        lowercase				:							List[Any]									=   do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase				:							Optional[int]									=   crop_size if crop_size is not None else self.crop_size
        lowercase				:							List[str]									=   get_size_dict(__lowercase     )
        lowercase				:							List[Any]									=   do_rescale if do_rescale is not None else self.do_rescale
        lowercase				:							Optional[Any]									=   rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase				:							str									=   do_normalize if do_normalize is not None else self.do_normalize
        lowercase				:							Optional[int]									=   image_mean if image_mean is not None else self.image_mean
        lowercase				:							int									=   image_std if image_std is not None else self.image_std
        lowercase				:							Optional[Any]									=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase				:							Tuple									=   make_list_of_images(__lowercase     )

        if not valid_images(__lowercase     ):
          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:
          lowercase				:							Optional[Any]									=   [convert_to_rgb(__lowercase     ) for image in images]

        # All transformations expect numpy arrays.
        lowercase				:							Any									=   [to_numpy_array(__lowercase     ) for image in images]

        if do_resize:
          lowercase				:							Optional[int]									=   [self.resize(image=__lowercase  ,    size=__lowercase  ,    resample=__lowercase     ) for image in images]

        if do_center_crop:
          lowercase				:							List[str]									=   [self.center_crop(image=__lowercase  ,    size=__lowercase     ) for image in images]

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

        if do_normalize:
          lowercase				:							int									=   [self.normalize(image=__lowercase  ,    mean=__lowercase  ,    std=__lowercase     ) for image in images]

        lowercase				:							Optional[int]									=   [to_channel_dimension_format(__lowercase  ,    __lowercase     ) for image in images]

        lowercase				:							Any									=   {'''pixel_values''': images}
        return BatchFeature(data=__lowercase  ,    tensor_type=__lowercase     )



from transformers import PretrainedConfig







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     Dict:
										lowerCAmelCase_			:   List[Any]     					=					attribute
										super().__init__(**__lowercase				)







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					attribute