


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


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

class    __magic_name__						(  __lowerCAmelCase):
     A:		int								=   ["pixel_values"]


     def __init__(   self				:  Tuple				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Dict[str, int] = None				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Dict[str, int] = None				,						lowerCamelCase__				:  PILImageResampling = PILImageResampling.BICUBIC				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Union[int, float] = 1 / 255				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Union[float, List[float]]] = None				,						lowerCamelCase__				:  Optional[Union[float, List[float]]] = None				,						**lowerCamelCase__				:  Optional[Any]				,						)	->   None:





      '''simple docstring'''






      super().__init__(**lowerCAmelCase_      )
      UpperCamelCase__       :     Union[str, Any] 							= crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
      UpperCamelCase__       :     List[str] 							= get_size_dict(lowerCAmelCase_				,						param_name='''crop_size'''      )
      UpperCamelCase__       :     str 							= size if size is not None else {'''height''': 224, '''width''': 224}
      UpperCamelCase__       :     Optional[Any] 							= get_size_dict(lowerCAmelCase_      )

      UpperCamelCase__       :     Any 							= do_center_crop
      UpperCamelCase__       :     Union[str, Any] 							= crop_size
      UpperCamelCase__       :     List[str] 							= do_resize
      UpperCamelCase__       :     int 							= size
      UpperCamelCase__       :     List[str] 							= resample
      UpperCamelCase__       :     Optional[int] 							= do_rescale
      UpperCamelCase__       :     int 							= rescale_factor
      UpperCamelCase__       :     Optional[Any] 							= do_normalize
      UpperCamelCase__       :     Union[str, Any] 							= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      UpperCamelCase__       :     Any 							= image_std if image_std is not None else IMAGENET_DEFAULT_STD


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Dict[str, int]				,						lowerCamelCase__				:  Optional[int] = None				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  List[Any]				,						)	->   np.ndarray:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= self.size if size is None else size
      UpperCamelCase__       :     Tuple 							= get_size_dict(lowerCAmelCase_      )
      UpperCamelCase__       :     List[Any] 							= get_size_dict(lowerCAmelCase_				,						param_name='''crop_size'''      )

      UpperCamelCase__							,					UpperCamelCase__       :     Optional[Any] 							= get_image_size(lowerCAmelCase_      )
      UpperCamelCase__       :     Tuple 							= min(lowerCAmelCase_				,						lowerCAmelCase_      )
      UpperCamelCase__       :     List[Any] 							= (size['''height'''] / crop_size['''height''']) * min_dim
      UpperCamelCase__       :     int 							= (size['''width'''] / crop_size['''width''']) * min_dim
      return center_crop(lowerCAmelCase_				,						size=(cropped_height, cropped_width)				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Dict[str, int]				,						lowerCamelCase__				:  PILImageResampling = PIL.Image.BICUBIC				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  Any				,						)	->   np.ndarray:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= get_size_dict(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()}"      )
      return resize(
          lowerCAmelCase_				,						size=(size['''height'''], size['''width'''])				,						resample=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Union[int, float]				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  Optional[int]				,						)	->   Optional[int]:





      '''simple docstring'''






      return rescale(lowerCAmelCase_				,						scale=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Union[float, List[float]]				,						lowerCamelCase__				:  Union[float, List[float]]				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  Optional[int]				,						)	->   np.ndarray:





      '''simple docstring'''






      return normalize(lowerCAmelCase_				,						mean=lowerCAmelCase_				,						std=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  ImageInput				,						lowerCamelCase__				:  Optional[bool] = None				,						lowerCamelCase__				:  Optional[Dict[str, int]] = None				,						lowerCamelCase__				:  Optional[bool] = None				,						lowerCamelCase__				:  Optional[Dict[str, int]] = None				,						lowerCamelCase__				:  PILImageResampling = None				,						lowerCamelCase__				:  Optional[bool] = None				,						lowerCamelCase__				:  Optional[float] = None				,						lowerCamelCase__				:  Optional[bool] = None				,						lowerCamelCase__				:  Optional[Union[float, List[float]]] = None				,						lowerCamelCase__				:  Optional[Union[float, List[float]]] = None				,						lowerCamelCase__				:  Optional[Union[str, TensorType]] = None				,						lowerCamelCase__				:  ChannelDimension = ChannelDimension.FIRST				,						**lowerCamelCase__				:  Optional[int]				,						)	->   PIL.Image.Image:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__       :     Optional[int] 							= crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__       :     List[str] 							= get_size_dict(lowerCAmelCase_				,						param_name='''crop_size'''      )
      UpperCamelCase__       :     List[str] 							= do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__       :     Optional[int] 							= size if size is not None else self.size
      UpperCamelCase__       :     List[str] 							= get_size_dict(lowerCAmelCase_      )
      UpperCamelCase__       :     Any 							= resample if resample is not None else self.resample
      UpperCamelCase__       :     Optional[Any] 							= do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__       :     Any 							= rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__       :     Dict 							= do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__       :     Union[str, Any] 							= image_mean if image_mean is not None else self.image_mean
      UpperCamelCase__       :     Dict 							= image_std if image_std is not None else self.image_std

      UpperCamelCase__       :     int 							= 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_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.
      UpperCamelCase__       :     str 							= [to_numpy_array(lowerCAmelCase_      ) for image in images]

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

      if do_resize:
       UpperCamelCase__       :     List[str] 							= [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__       :     Dict 							= [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__       :     Dict 							= {'''pixel_values''': images}
      return BatchFeature(data=lowerCAmelCase_				,						tensor_type=lowerCAmelCase_      )




'''simple docstring'''





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline

