





from typing import Dict, Iterable, 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, logging


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



class  A__       (	A__      ):
						_UpperCAmelCase     :Optional[int]						   =       ['pixel_values']





						def __init__(			self	,							A_ = True	,							A_ = None	,							A_ = PILImageResampling.BICUBIC	,							A_ = True	,							A_ = None	,							A_ = True	,							A_ = 1 / 255	,							A_ = True	,							A_ = IMAGENET_DEFAULT_MEAN	,							A_ = IMAGENET_DEFAULT_STD	,							**A_	,							):
							'''simple docstring'''




							super().__init__(**lowerCamelCase__       )
							UpperCamelCase     : Tuple	     =       size if size is not None else {"shortest_edge": 224}
							UpperCamelCase     : List[Any]	     =       get_size_dict(lowerCamelCase__	,							default_to_square=lowerCamelCase__       )
							UpperCamelCase     : int	     =       crop_size if crop_size is not None else {"height": 224, "width": 224}
							UpperCamelCase     : int	     =       get_size_dict(lowerCamelCase__	,							param_name="crop_size"       )

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





						def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = PILImageResampling.BICUBIC	,							A_ = None	,							**A_	,							):
							'''simple docstring'''




							UpperCamelCase     : str	     =       get_size_dict(lowerCamelCase__	,							default_to_square=lowerCamelCase__       )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
								UpperCamelCase     : int	     =       int((256 / 224) * size["shortest_edge"]       )
								UpperCamelCase     : int	     =       get_resize_output_image_size(lowerCamelCase__	,							size=lowerCamelCase__	,							default_to_square=lowerCamelCase__       )
								UpperCamelCase     : int	     =       {"height": output_size[0], "width": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
								raise ValueError(
								    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""       )
							return resize(
							    lowerCamelCase__	,							size=(size_dict["height"], size_dict["width"])	,							resample=lowerCamelCase__	,							data_format=lowerCamelCase__	,							**lowerCamelCase__       )





						def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = None	,							**A_	,							):
							'''simple docstring'''




							UpperCamelCase     : List[str]	     =       get_size_dict(lowerCamelCase__       )
							if "height" not in size or "width" not in size:
								raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""       )
							return center_crop(lowerCamelCase__	,							size=(size["height"], size["width"])	,							data_format=lowerCamelCase__	,							**lowerCamelCase__       )





						def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = None	,							**A_	,							):
							'''simple docstring'''




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





						def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_ = None	,							**A_	,							):
							'''simple docstring'''




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





						def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = ChannelDimension.FIRST	,							**A_	,							):
							'''simple docstring'''




							UpperCamelCase     : Optional[int]	     =       do_resize if do_resize is not None else self.do_resize
							UpperCamelCase     : Tuple	     =       resample if resample is not None else self.resample
							UpperCamelCase     : Tuple	     =       do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase     : List[str]	     =       do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase     : Tuple	     =       rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase     : Any	     =       do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase     : str	     =       image_mean if image_mean is not None else self.image_mean
							UpperCamelCase     : int	     =       image_std if image_std is not None else self.image_std

							UpperCamelCase     : Tuple	     =       size if size is not None else self.size
							UpperCamelCase     : int	     =       get_size_dict(lowerCamelCase__	,							default_to_square=lowerCamelCase__       )
							UpperCamelCase     : Dict	     =       crop_size if crop_size is not None else self.crop_size
							UpperCamelCase     : Tuple	     =       get_size_dict(lowerCamelCase__	,							param_name="crop_size"       )

							UpperCamelCase     : Dict	     =       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:
								raise ValueError("Size must be specified if do_resize is True."       )

							if do_center_crop and crop_size is None:
								raise ValueError("Crop size 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.
							UpperCamelCase     : Union[str, Any]	     =       [to_numpy_array(lowerCamelCase__       ) for image in images]

							if do_resize:
								UpperCamelCase     : Dict	     =       [self.resize(lowerCamelCase__	,							lowerCamelCase__	,							lowerCamelCase__       ) for image in images]

							if do_center_crop:
								UpperCamelCase     : Union[str, Any]	     =       [self.center_crop(lowerCamelCase__	,							lowerCamelCase__       ) for image in images]

							if do_rescale:
								UpperCamelCase     : Union[str, Any]	     =       [self.rescale(lowerCamelCase__	,							lowerCamelCase__       ) for image in images]

							if do_normalize:
								UpperCamelCase     : Union[str, Any]	     =       [self.normalize(lowerCamelCase__	,							lowerCamelCase__	,							lowerCamelCase__       ) for image in images]

							UpperCamelCase     : Dict	     =       [to_channel_dimension_format(lowerCamelCase__	,							lowerCamelCase__       ) for image in images]

							UpperCamelCase     : List[Any]	     =       {"pixel_values": images}
							return BatchFeature(data=lowerCamelCase__	,							tensor_type=lowerCamelCase__       )







from __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
