


"""simple docstring"""


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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


a_											=     logging.get_logger(__name__)
class snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      ['pixel_values']







		def __init__(       self   :							Optional[int]     , a__   :							bool = True     , a__   :							Dict[str, int] = None     , a__   :							PILImageResampling = PIL.Image.NEAREST     , a__   :							bool = False     , a__   :							Dict[str, int] = None     , a__   :							Union[int, float] = 1 / 2_55     , a__   :							bool = False     , a__   :							bool = True     , a__   :							bool = True     , a__   :							Optional[Union[float, List[float]]] = None     , a__   :							Optional[Union[float, List[float]]] = None     , a__   :							bool = True     , **a__   :							Optional[int]     , )							->    None:





						'''simple docstring'''







						super().__init__(**a__		)
						_A       	=      size if size is not None else {"height": 3_46, "width": 3_46}
						_A       	=      get_size_dict(a__		)
						_A       	=      crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
						_A       	=      get_size_dict(a__     , param_name="crop_size"		)

						_A       	=      do_resize
						_A       	=      size
						_A       	=      resample
						_A       	=      do_center_crop
						_A       	=      crop_size
						_A       	=      do_rescale
						_A       	=      rescale_factor
						_A       	=      rescale_offset
						_A       	=      do_normalize
						_A       	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A       	=      image_std if image_std is not None else IMAGENET_STANDARD_STD
						_A       	=      include_top







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





						'''simple docstring'''







						_A       	=      get_size_dict(a__		)
						if "height" not in size or "width" not in size:
										raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
						return resize(
						    a__     , size=(size["height"], size["width"])     , resample=a__     , data_format=a__     , **a__		)







		def 			a_			(       self   :							Optional[int]     , a__   :							np.ndarray     , a__   :							Dict[str, int]     , a__   :							Optional[Union[str, ChannelDimension]] = None     , **a__   :							int     , )							->    np.ndarray:





						'''simple docstring'''







						_A       	=      get_size_dict(a__		)
						if "height" not in size or "width" not in size:
										raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
						return center_crop(a__     , size=(size["height"], size["width"])     , data_format=a__     , **a__		)







		def 			a_			(       self   :							Tuple     , a__   :							np.ndarray     , a__   :							Union[int, float]     , a__   :							bool = True     , a__   :							Optional[Union[str, ChannelDimension]] = None     , **a__   :							Optional[int]     , )							->    Union[str, Any]:





						'''simple docstring'''







						if offset:
										_A       	=      (image - 1_2_7.5) * scale
										if data_format is not None:
														_A       	=      to_channel_dimension_format(a__     , a__		)
										_A       	=      rescaled_image.astype(np.floataa		)
						else:
										_A       	=      rescale(a__     , scale=a__     , data_format=a__     , **a__		)
						return rescaled_image







		def 			a_			(       self   :							Any     , a__   :							np.ndarray     , a__   :							Union[float, List[float]]     , a__   :							Union[float, List[float]]     , a__   :							Optional[Union[str, ChannelDimension]] = None     , **a__   :							Dict     , )							->    np.ndarray:





						'''simple docstring'''







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







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





						'''simple docstring'''







						_A       	=      do_resize if do_resize is not None else self.do_resize
						_A       	=      resample if resample is not None else self.resample
						_A       	=      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A       	=      do_rescale if do_rescale is not None else self.do_rescale
						_A       	=      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A       	=      rescale_offset if rescale_offset is not None else self.rescale_offset
						_A       	=      do_normalize if do_normalize is not None else self.do_normalize
						_A       	=      image_mean if image_mean is not None else self.image_mean
						_A       	=      image_std if image_std is not None else self.image_std
						_A       	=      include_top if include_top is not None else self.include_top

						_A       	=      size if size is not None else self.size
						_A       	=      get_size_dict(a__		)
						_A       	=      crop_size if crop_size is not None else self.crop_size
						_A       	=      get_size_dict(a__     , param_name="crop_size"		)

						_A       	=      make_list_of_images(a__		)

						if not valid_images(a__		):
										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_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.
						_A       	=      [to_numpy_array(a__		) for image in images]

						if do_resize:
										_A       	=      [self.resize(image=a__     , size=a__     , resample=a__		) for image in images]

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

						if do_rescale:
										_A       	=      [self.rescale(image=a__     , scale=a__     , offset=a__		) for image in images]

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

						if include_top:
										_A       	=      [self.normalize(image=a__     , mean=[0, 0, 0]     , std=a__		) for image in images]

						_A       	=      [to_channel_dimension_format(a__     , a__		) for image in images]

						_A       	=      {"pixel_values": images}
						return BatchFeature(data=a__     , tensor_type=a__		)



"""simple docstring"""


from .hash_table import HashTable
class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Any     , *a__   :							Any     , **a__   :							Tuple		)							->    Tuple:





						'''simple docstring'''







						super().__init__(*a__     , **a__		)







		def 			a_			(       self   :							Tuple     , a__   :							int     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      1
						_A       	=      self.hash_function(key + i * i		)

						while self.values[new_key] is not None and self.values[new_key] != key:
										i += 1
										_A       	=      (
										    self.hash_function(key + i * i		)
										    if not self.balanced_factor() >= self.lim_charge
										    else None
										)

										if new_key is None:
														break

						return new_key