





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


__UpperCAmelCase					:     Tuple	      =	logging.get_logger(__name__)


class 			lowerCamelCase						(		SCREAMING_SNAKE_CASE  ):
  UpperCAmelCase		:       Dict										=     ['pixel_values']







  def __init__(     self						:   List[str]  ,					__snake_case						:   Optional[Any] = True  ,					__snake_case						:   Tuple = None  ,					__snake_case						:   Tuple = 0.9  ,					__snake_case						:   Any = PILImageResampling.BICUBIC  ,					__snake_case						:   Union[str, Any] = True  ,					__snake_case						:   int = None  ,					__snake_case						:   Tuple = 1 / 255  ,					__snake_case						:   List[str] = True  ,					__snake_case						:   Optional[Any] = True  ,					__snake_case						:   List[Any] = None  ,					__snake_case						:   Dict = None  ,					**__snake_case						:   int  ,					)    ->		None:
        super().__init__(**_UpperCamelCase       )
        _a							:  Any				= size if size is not None else {'''shortest_edge''': 224}
        _a							:  Dict				= get_size_dict(_UpperCamelCase  ,					default_to_square=_UpperCamelCase       )
        _a							:  List[Any]				= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _a							:  Optional[int]				= get_size_dict(_UpperCamelCase  ,					param_name='''crop_size'''       )

        _a							:  Dict				= do_resize
        _a							:  str				= size
        _a							:  List[str]				= crop_pct
        _a							:  int				= resample
        _a							:  int				= do_center_crop
        _a							:  List[Any]				= crop_size
        _a							:  str				= do_rescale
        _a							:  int				= rescale_factor
        _a							:  Optional[Any]				= do_normalize
        _a							:  Tuple				= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _a							:  Optional[Any]				= image_std if image_std is not None else IMAGENET_DEFAULT_STD







  def      snake_case_							(     self						:   int  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   Tuple  ,					__snake_case						:   int = None  ,					__snake_case						:   List[Any] = PILImageResampling.BICUBIC  ,					__snake_case						:   List[str] = None  ,					**__snake_case						:   List[str]  ,					)    ->		np.ndarray:
        _a							:  Tuple				= get_size_dict(_UpperCamelCase  ,					default_to_square=_UpperCamelCase       )
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
              raise ValueError(f"""size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""       )
        if crop_pct is not None:
              if "shortest_edge" in size:
                    _a							:  List[str]				= int(size['''shortest_edge'''] / crop_pct       )
              elif "height" in size and "width" in size:
                    if size["height"] == size["width"]:
                          _a							:  List[Any]				= int(size['''height'''] / crop_pct       )
                    else:
                          _a							:  str				= (int(size['''height'''] / crop_pct       ), int(size['''width'''] / crop_pct       ))
              else:
                    raise ValueError('''Invalid size for resize: {}'''.format(_UpperCamelCase       )       )

              _a							:  List[Any]				= get_resize_output_image_size(_UpperCamelCase  ,					size=_UpperCamelCase  ,					default_to_square=_UpperCamelCase       )
        else:
              if "shortest_edge" in size:
                    _a							:  str				= get_resize_output_image_size(_UpperCamelCase  ,					size=size['''shortest_edge''']  ,					default_to_square=_UpperCamelCase       )
              elif "height" in size and "width" in size:
                    _a							:  List[str]				= (size['''height'''], size['''width'''])
              else:
                    raise ValueError('''Invalid size for resize: {}'''.format(_UpperCamelCase       )       )

        return resize(_UpperCamelCase  ,					size=_UpperCamelCase  ,					resample=_UpperCamelCase  ,					data_format=_UpperCamelCase  ,					**_UpperCamelCase       )







  def      snake_case_							(     self						:   Any  ,					__snake_case						:   List[str]  ,					__snake_case						:   List[Any]  ,					__snake_case						:   Union[str, Any] = None  ,					**__snake_case						:   List[str]  ,					)    ->		np.ndarray:
        _a							:  str				= get_size_dict(_UpperCamelCase       )
        if "height" not in size or "width" not in size:
              raise ValueError(f"""size must contain \'height\' and \'width\' as keys. Got {size.keys()}"""       )
        return center_crop(_UpperCamelCase  ,					size=(size['''height'''], size['''width'''])  ,					data_format=_UpperCamelCase  ,					**_UpperCamelCase       )







  def      snake_case_							(     self						:   Union[str, Any]  ,					__snake_case						:   List[Any]  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   List[str] = None  ,					**__snake_case						:   int  ,					)    ->		Union[str, Any]:
        return rescale(_UpperCamelCase  ,					scale=_UpperCamelCase  ,					data_format=_UpperCamelCase  ,					**_UpperCamelCase       )







  def      snake_case_							(     self						:   int  ,					__snake_case						:   List[str]  ,					__snake_case						:   Dict  ,					__snake_case						:   List[str]  ,					__snake_case						:   Union[str, Any] = None  ,					**__snake_case						:   Optional[int]  ,					)    ->		np.ndarray:
        return normalize(_UpperCamelCase  ,					mean=_UpperCamelCase  ,					std=_UpperCamelCase  ,					data_format=_UpperCamelCase  ,					**_UpperCamelCase       )







  def      snake_case_							(     self						:   Union[str, Any]  ,					__snake_case						:   Dict  ,					__snake_case						:   List[str] = None  ,					__snake_case						:   Union[str, Any] = None  ,					__snake_case						:   Optional[int] = None  ,					__snake_case						:   List[str] = None  ,					__snake_case						:   Optional[Any] = None  ,					__snake_case						:   List[Any] = None  ,					__snake_case						:   Any = None  ,					__snake_case						:   int = None  ,					__snake_case						:   Optional[int] = None  ,					__snake_case						:   List[Any] = None  ,					__snake_case						:   Any = None  ,					__snake_case						:   Optional[Any] = None  ,					__snake_case						:   Optional[int] = ChannelDimension.FIRST  ,					**__snake_case						:   Optional[int]  ,					)    ->		PIL.Image.Image:
        _a							:  Dict				= do_resize if do_resize is not None else self.do_resize
        _a							:  Union[str, Any]				= crop_pct if crop_pct is not None else self.crop_pct
        _a							:  Optional[int]				= resample if resample is not None else self.resample
        _a							:  Optional[int]				= do_center_crop if do_center_crop is not None else self.do_center_crop
        _a							:  Tuple				= do_rescale if do_rescale is not None else self.do_rescale
        _a							:  Tuple				= rescale_factor if rescale_factor is not None else self.rescale_factor
        _a							:  int				= do_normalize if do_normalize is not None else self.do_normalize
        _a							:  List[Any]				= image_mean if image_mean is not None else self.image_mean
        _a							:  Optional[Any]				= image_std if image_std is not None else self.image_std

        _a							:  List[Any]				= size if size is not None else self.size
        _a							:  Optional[Any]				= get_size_dict(_UpperCamelCase  ,					default_to_square=_UpperCamelCase       )
        _a							:  Optional[int]				= crop_size if crop_size is not None else self.crop_size
        _a							:  Any				= get_size_dict(_UpperCamelCase  ,					param_name='''crop_size'''       )

        _a							:  Optional[int]				= 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 or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

        if do_center_crop and crop_pct is None:
              raise ValueError('''Crop_pct 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.'''       )

        # All transformations expect numpy arrays.
        _a							:  Union[str, Any]				= [to_numpy_array(_UpperCamelCase       ) for image in images]

        if do_resize:
              _a							:  Dict				= [self.resize(image=_UpperCamelCase  ,					size=_UpperCamelCase  ,					crop_pct=_UpperCamelCase  ,					resample=_UpperCamelCase       ) for image in images]

        if do_center_crop:
              _a							:  int				= [self.center_crop(image=_UpperCamelCase  ,					size=_UpperCamelCase       ) for image in images]

        if do_rescale:
              _a							:  Tuple				= [self.rescale(image=_UpperCamelCase  ,					scale=_UpperCamelCase       ) for image in images]

        if do_normalize:
              _a							:  Dict				= [self.normalize(image=_UpperCamelCase  ,					mean=_UpperCamelCase  ,					std=_UpperCamelCase       ) for image in images]

        _a							:  Optional[int]				= [to_channel_dimension_format(_UpperCamelCase  ,					_UpperCamelCase       ) for image in images]

        _a							:  Optional[int]				= {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase  ,					tensor_type=_UpperCamelCase       )









'''simple docstring'''
from statistics import mean, stdev

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							min(__UpperCamelCase		)
     _A				=							max(__UpperCamelCase		)
     # normalize data
     return [round((x - x_min) / (x_max - x_min)							,    __UpperCamelCase		) for x in data]

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							mean(__UpperCamelCase		)
     _A				=							stdev(__UpperCamelCase		)
     # standardize data
     return [round((x - mu) / (sigma)							,    __UpperCamelCase		) for x in data]





