


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


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


if is_vision_available():
						import PIL







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




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




						def __init__(					self ,    __lowercase = True ,    __lowercase = None ,    __lowercase = PILImageResampling.BICUBIC ,    __lowercase = True ,    __lowercase = None ,    __lowercase = True ,    __lowercase = 1 / 2_5_5 ,    __lowercase = True ,    __lowercase = None ,    __lowercase = None ,    __lowercase = True ,    **__lowercase ,    )       ->     None:
										super().__init__(**__lowercase				)
										lowerCAmelCase_			:   Union[str, Any]     					=					size if size is not None else {'''shortest_edge''': 2_2_4}
										lowerCAmelCase_			:   List[Any]     					=					get_size_dict(__lowercase ,    default_to_square=__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										lowerCAmelCase_			:   Optional[int]     					=					get_size_dict(__lowercase				)

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




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = PILImageResampling.BICUBIC ,    __lowercase = None ,    **__lowercase ,    )       ->     np.ndarray:
										lowerCAmelCase_			:   Tuple     					=					get_size_dict(__lowercase ,    default_to_square=__lowercase				)
										lowerCAmelCase_			:   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     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = None ,    **__lowercase ,    )       ->     np.ndarray:
										lowerCAmelCase_			:   Optional[int]     					=					get_size_dict(__lowercase				)
										return center_crop(__lowercase ,    size=(size['''height'''], size['''width''']) ,    data_format=__lowercase ,    **__lowercase				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = None ,    **__lowercase ,    )       ->     int:
										return rescale(__lowercase ,    scale=__lowercase ,    data_format=__lowercase ,    **__lowercase				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase = None ,    **__lowercase ,    )       ->     np.ndarray:
										return normalize(__lowercase ,    mean=__lowercase ,    std=__lowercase ,    data_format=__lowercase ,    **__lowercase				)




						def     lowercase_			(					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 ,    )       ->     PIL.Image.Image:
										lowerCAmelCase_			:   str     					=					do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_			:   int     					=					size if size is not None else self.size
										lowerCAmelCase_			:   Dict     					=					get_size_dict(__lowercase ,    default_to_square=__lowercase				)
										lowerCAmelCase_			:   Any     					=					resample if resample is not None else self.resample
										lowerCAmelCase_			:   List[Any]     					=					do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCAmelCase_			:   Optional[int]     					=					crop_size if crop_size is not None else self.crop_size
										lowerCAmelCase_			:   List[str]     					=					get_size_dict(__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_			:   Optional[Any]     					=					rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_			:   str     					=					do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_			:   Optional[int]     					=					image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_			:   int     					=					image_std if image_std is not None else self.image_std
										lowerCAmelCase_			:   Optional[Any]     					=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCAmelCase_			:   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:
														lowerCAmelCase_			:   Optional[Any]     					=					[convert_to_rgb(__lowercase				) for image in images]

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

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

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

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

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

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

										lowerCAmelCase_			:   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