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


__lowerCamelCase			: Optional[int]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Optional[Any]       ,	_lowercase			:				bool = True       ,	_lowercase			:				Dict[str, int] = None       ,	_lowercase			:				int = 0.9       ,	_lowercase			:				PILImageResampling = PILImageResampling.BICUBIC       ,	_lowercase			:				bool = True       ,	_lowercase			:				Dict[str, int] = None       ,	_lowercase			:				Union[int, float] = 1 / 2_55       ,	_lowercase			:				bool = True       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Union[float, List[float]]] = None       ,	_lowercase			:				Optional[Union[float, List[float]]] = None       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     super().__init__(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__										=      get_size_dict(_lowercase       ,	default_to_square=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
     SCREAMING_SNAKE_CASE__										=      get_size_dict(_lowercase       ,	param_name="""crop_size"""      )

     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      size
     SCREAMING_SNAKE_CASE__										=      crop_pct
     SCREAMING_SNAKE_CASE__										=      resample
     SCREAMING_SNAKE_CASE__										=      do_center_crop
     SCREAMING_SNAKE_CASE__										=      crop_size
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      rescale_factor
     SCREAMING_SNAKE_CASE__										=      do_normalize
     SCREAMING_SNAKE_CASE__										=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     SCREAMING_SNAKE_CASE__										=      image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def 	__a						(    self			:				str       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				Dict[str, int]       ,	_lowercase			:				Optional[float] = None       ,	_lowercase			:				PILImageResampling = PILImageResampling.BICUBIC       ,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None       ,	**_lowercase			:				List[str]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      get_size_dict(_lowercase       ,	default_to_square=_lowercase      )
     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:
         SCREAMING_SNAKE_CASE__										=      int(size["""shortest_edge"""] / crop_pct      )
       elif "height" in size and "width" in size:
         if size["height"] == size["width"]:
           SCREAMING_SNAKE_CASE__										=      int(size["""height"""] / crop_pct      )
         else:
           SCREAMING_SNAKE_CASE__										=      (int(size["""height"""] / crop_pct      ), int(size["""width"""] / crop_pct      ))
       else:
         raise ValueError("""Invalid size for resize: {}""".format(_lowercase      )      )

       SCREAMING_SNAKE_CASE__										=      get_resize_output_image_size(_lowercase       ,	size=_lowercase       ,	default_to_square=_lowercase      )
     else:
       if "shortest_edge" in size:
         SCREAMING_SNAKE_CASE__										=      get_resize_output_image_size(_lowercase       ,	size=size["""shortest_edge"""]       ,	default_to_square=_lowercase      )
       elif "height" in size and "width" in size:
         SCREAMING_SNAKE_CASE__										=      (size["""height"""], size["""width"""])
       else:
         raise ValueError("""Invalid size for resize: {}""".format(_lowercase      )      )

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




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				Dict[str, int]       ,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None       ,	**_lowercase			:				Optional[Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      get_size_dict(_lowercase      )
     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(_lowercase       ,	size=(size["""height"""], size["""width"""])       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Any       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				Union[int, float]       ,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






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




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				Union[float, List[float]]       ,	_lowercase			:				Union[float, List[float]]       ,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None       ,	**_lowercase			:				str       ,	):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				ImageInput       ,	_lowercase			:				bool = None       ,	_lowercase			:				Dict[str, int] = None       ,	_lowercase			:				int = None       ,	_lowercase			:				PILImageResampling = None       ,	_lowercase			:				bool = None       ,	_lowercase			:				Dict[str, int] = None       ,	_lowercase			:				bool = None       ,	_lowercase			:				float = None       ,	_lowercase			:				bool = None       ,	_lowercase			:				Optional[Union[float, List[float]]] = None       ,	_lowercase			:				Optional[Union[float, List[float]]] = None       ,	_lowercase			:				Optional[Union[str, TensorType]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				List[Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__										=      do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__										=      do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__										=      image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__										=      image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE__										=      size if size is not None else self.size
     SCREAMING_SNAKE_CASE__										=      get_size_dict(_lowercase       ,	default_to_square=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__										=      get_size_dict(_lowercase       ,	param_name="""crop_size"""      )

     SCREAMING_SNAKE_CASE__										=      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 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.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(image=_lowercase       ,	size=_lowercase       ,	crop_pct=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE__										=      [self.center_crop(image=_lowercase       ,	size=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(image=_lowercase       ,	scale=_lowercase      ) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE__										=      [self.normalize(image=_lowercase       ,	mean=_lowercase       ,	std=_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
