


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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE				:						Dict			   = logging.get_logger(__name__)






class    UpperCamelCase    (						_SCREAMING_SNAKE_CASE   ):


   '''simple docstring'''







   lowercase    :		Union[str, Any]	  =["pixel_values"]
   def __init__(   self      ,							UpperCamelCase_ = True      ,							UpperCamelCase_ = None      ,							UpperCamelCase_ = True      ,							UpperCamelCase_ = None      ,							UpperCamelCase_ = PILImageResampling.BICUBIC      ,							UpperCamelCase_ = True      ,							UpperCamelCase_ = 1 / 255      ,							UpperCamelCase_ = True      ,							UpperCamelCase_ = None      ,							UpperCamelCase_ = None      ,							**UpperCamelCase_      ,							):
        super().__init__(**UpperCamelCase_						)
        lowercase_				:Tuple       			=				crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
        lowercase_				:Union[str, Any]       			=				get_size_dict(UpperCamelCase_      ,							param_name='''crop_size'''						)
        lowercase_				:Any       			=				size if size is not None else {'''height''': 224, '''width''': 224}
        lowercase_				:Union[str, Any]       			=				get_size_dict(UpperCamelCase_						)

        lowercase_				:str       			=				do_center_crop
        lowercase_				:str       			=				crop_size
        lowercase_				:Tuple       			=				do_resize
        lowercase_				:int       			=				size
        lowercase_				:str       			=				resample
        lowercase_				:str       			=				do_rescale
        lowercase_				:str       			=				rescale_factor
        lowercase_				:Optional[Any]       			=				do_normalize
        lowercase_				:Dict       			=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_				:Union[str, Any]       			=				image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 						UpperCamelCase     (   self      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_ = None      ,							UpperCamelCase_ = None      ,							**UpperCamelCase_      ,							):
        lowercase_				:str       			=				self.size if size is None else size
        lowercase_				:int       			=				get_size_dict(UpperCamelCase_						)
        lowercase_				:Tuple       			=				get_size_dict(UpperCamelCase_      ,							param_name='''crop_size'''						)

        lowercase_			,					lowercase_				:List[str]       			=				get_image_size(UpperCamelCase_						)
        lowercase_				:Optional[Any]       			=				min(UpperCamelCase_      ,							UpperCamelCase_						)
        lowercase_				:Optional[Any]       			=				(size['''height'''] / crop_size['''height''']) * min_dim
        lowercase_				:Dict       			=				(size['''width'''] / crop_size['''width''']) * min_dim
        return center_crop(UpperCamelCase_      ,							size=(cropped_height, cropped_width)      ,							data_format=UpperCamelCase_      ,							**UpperCamelCase_						)
   def 						UpperCamelCase     (   self      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_ = PIL.Image.BICUBIC      ,							UpperCamelCase_ = None      ,							**UpperCamelCase_      ,							):
        lowercase_				:str       			=				get_size_dict(UpperCamelCase_						)
        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()}"						)
        return resize(
            UpperCamelCase_      ,							size=(size['''height'''], size['''width'''])      ,							resample=UpperCamelCase_      ,							data_format=UpperCamelCase_      ,							**UpperCamelCase_						)
   def 						UpperCamelCase     (   self      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_ = None      ,							**UpperCamelCase_      ,							):
        return rescale(UpperCamelCase_      ,							scale=UpperCamelCase_      ,							data_format=UpperCamelCase_      ,							**UpperCamelCase_						)
   def 						UpperCamelCase     (   self      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_ = None      ,							**UpperCamelCase_      ,							):
        return normalize(UpperCamelCase_      ,							mean=UpperCamelCase_      ,							std=UpperCamelCase_      ,							data_format=UpperCamelCase_      ,							**UpperCamelCase_						)




   def 						UpperCamelCase     (   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_ = ChannelDimension.FIRST      ,							**UpperCamelCase_      ,							):
        lowercase_				:Dict       			=				do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_				:Any       			=				crop_size if crop_size is not None else self.crop_size
        lowercase_				:Optional[Any]       			=				get_size_dict(UpperCamelCase_      ,							param_name='''crop_size'''						)
        lowercase_				:Union[str, Any]       			=				do_resize if do_resize is not None else self.do_resize
        lowercase_				:Optional[Any]       			=				size if size is not None else self.size
        lowercase_				:Union[str, Any]       			=				get_size_dict(UpperCamelCase_						)
        lowercase_				:List[Any]       			=				resample if resample is not None else self.resample
        lowercase_				:Optional[Any]       			=				do_rescale if do_rescale is not None else self.do_rescale
        lowercase_				:List[Any]       			=				rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_				:Tuple       			=				do_normalize if do_normalize is not None else self.do_normalize
        lowercase_				:str       			=				image_mean if image_mean is not None else self.image_mean
        lowercase_				:str       			=				image_std if image_std is not None else self.image_std

        lowercase_				:Optional[Any]       			=				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_center_crop and crop_size is None:
             raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

        if do_resize and size is None:
             raise ValueError('''Size 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 image standard deviation must be specified if do_normalize is True.'''						)

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

        if do_center_crop:
             lowercase_				:Any       			=				[self.center_crop(UpperCamelCase_      ,							UpperCamelCase_      ,							size=UpperCamelCase_						) for image in images]

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

        if do_rescale:
             lowercase_				:Optional[Any]       			=				[self.rescale(image=UpperCamelCase_      ,							scale=UpperCamelCase_						) for image in images]

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

        lowercase_				:Tuple       			=				[to_channel_dimension_format(UpperCamelCase_      ,							UpperCamelCase_						) for image in images]

        lowercase_				:Any       			=				{'''pixel_values''': images}
        return BatchFeature(data=UpperCamelCase_      ,							tensor_type=UpperCamelCase_						)







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		bool:

    """simple docstring"""


    if not isinstance(lowercase_    ,   lowercase_					):
        __UpperCamelCase     						=  F"Input value of [number={number}] must be an integer"
        raise TypeError(lowercase_					)
    if number < 0 or number % 2 == 1:
        return False
    __UpperCamelCase     						=  int(number**0.5					)
    return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






