






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 convert_to_rgb, 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


if is_vision_available():
   import PIL


A_						:						Optional[Any]						      =					logging.get_logger(__name__)




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Optional[Any]										=['''pixel_values''']





       def __init__(   self    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = PILImageResampling.BICUBIC    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1 / 2_5_5    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = True    ,     **_lowerCamelCase    ,     ):
         super().__init__(**_lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
         UpperCamelCase_:   str        =							get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase				)

         UpperCamelCase_:   int        =							do_resize
         UpperCamelCase_:   Union[str, Any]        =							size
         UpperCamelCase_:   str        =							resample
         UpperCamelCase_:   str        =							do_rescale
         UpperCamelCase_:   Tuple        =							rescale_factor
         UpperCamelCase_:   Any        =							do_normalize
         UpperCamelCase_:   Optional[int]        =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         UpperCamelCase_:   List[Any]        =							image_std if image_std is not None else OPENAI_CLIP_STD
         UpperCamelCase_:   str        =							do_convert_rgb





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = PILImageResampling.BICUBIC    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         UpperCamelCase_:   Union[str, Any]        =							get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase				)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''				)
         UpperCamelCase_:   int        =							(size['height'], size['width'])
         return resize(_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         return rescale(_lowerCamelCase    ,     scale=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         return normalize(_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = ChannelDimension.FIRST    ,     **_lowerCamelCase    ,     ):
         UpperCamelCase_:   List[str]        =							do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_:   str        =							resample if resample is not None else self.resample
         UpperCamelCase_:   List[Any]        =							do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_:   Union[str, Any]        =							rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_:   Tuple        =							do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_:   List[str]        =							image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_:   Any        =							image_std if image_std is not None else self.image_std
         UpperCamelCase_:   Optional[Any]        =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         UpperCamelCase_:   int        =							size if size is not None else self.size
         UpperCamelCase_:   str        =							get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase				)

         UpperCamelCase_:   Optional[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_resize and size is None or resample is None:
           raise ValueError('Size and resample must be specified if do_resize 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:
           UpperCamelCase_:   str        =							[convert_to_rgb(_lowerCamelCase				) for image in images]

         # All transformations expect numpy arrays.
         UpperCamelCase_:   int        =							[to_numpy_array(_lowerCamelCase				) for image in images]

         if do_resize:
           UpperCamelCase_:   Union[str, Any]        =							[self.resize(image=_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase				) for image in images]

         if do_rescale:
           UpperCamelCase_:   List[str]        =							[self.rescale(image=_lowerCamelCase    ,     scale=_lowerCamelCase				) for image in images]

         if do_normalize:
           UpperCamelCase_:   Optional[Any]        =							[self.normalize(image=_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase				) for image in images]

         UpperCamelCase_:   Dict        =							[to_channel_dimension_format(_lowerCamelCase    ,     _lowerCamelCase				) for image in images]

         UpperCamelCase_:   int        =							BatchFeature(data={'pixel_values': images}    ,     tensor_type=_lowerCamelCase				)

         return encoded_outputs

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




