
'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__SCREAMING_SNAKE_CASE  =			logging.get_logger(__name__)


if is_vision_available():
						import PIL




class 						__snake_case (						_a  ):
		a_        =				["pixel_values"]




		def __init__(     self				:str ,						_SCREAMING_SNAKE_CASE				:List[str] = True ,						_SCREAMING_SNAKE_CASE				:str = None ,						_SCREAMING_SNAKE_CASE				:Dict = PILImageResampling.BICUBIC ,						_SCREAMING_SNAKE_CASE				:Optional[Any] = True ,						_SCREAMING_SNAKE_CASE				:Tuple = None ,						_SCREAMING_SNAKE_CASE				:Tuple = True ,						_SCREAMING_SNAKE_CASE				:Any = 1 / 2_5_5 ,						_SCREAMING_SNAKE_CASE				:List[Any] = True ,						_SCREAMING_SNAKE_CASE				:Any = None ,						_SCREAMING_SNAKE_CASE				:List[Any] = None ,						_SCREAMING_SNAKE_CASE				:Optional[int] = True ,						**_SCREAMING_SNAKE_CASE				:List[Any] ,						)				-> None:






								'''simple docstring'''






								super().__init__(**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Union[str, Any]				    =    size if size is not None else {'''shortest_edge''': 2_2_4}
								UpperCamelCase_    :  Union[str, Any]				    =    get_size_dict(_SCREAMING_SNAKE_CASE ,						default_to_square=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Any				    =    crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								UpperCamelCase_    :  str				    =    get_size_dict(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Optional[Any]				    =    do_resize
								UpperCamelCase_    :  Optional[int]				    =    size
								UpperCamelCase_    :  Any				    =    resample
								UpperCamelCase_    :  Tuple				    =    do_center_crop
								UpperCamelCase_    :  Any				    =    crop_size
								UpperCamelCase_    :  int				    =    do_rescale
								UpperCamelCase_    :  str				    =    rescale_factor
								UpperCamelCase_    :  List[Any]				    =    do_normalize
								UpperCamelCase_    :  Tuple				    =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCamelCase_    :  Union[str, Any]				    =    image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCamelCase_    :  str				    =    do_convert_rgb




		def   _snake_case  (     self				:Dict ,						_SCREAMING_SNAKE_CASE				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Dict = PILImageResampling.BICUBIC ,						_SCREAMING_SNAKE_CASE				:str = None ,						**_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						)				-> np.ndarray:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    get_size_dict(_SCREAMING_SNAKE_CASE ,						default_to_square=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  int				    =    get_resize_output_image_size(
								    _SCREAMING_SNAKE_CASE ,						size=(size["""height"""], size["""width"""]) ,						default_to_square=_SCREAMING_SNAKE_CASE)
								return resize(_SCREAMING_SNAKE_CASE ,						size=_SCREAMING_SNAKE_CASE ,						resample=_SCREAMING_SNAKE_CASE ,						data_format=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:Dict ,						_SCREAMING_SNAKE_CASE				:List[str] ,						_SCREAMING_SNAKE_CASE				:List[str] ,						_SCREAMING_SNAKE_CASE				:int = None ,						**_SCREAMING_SNAKE_CASE				:Tuple ,						)				-> np.ndarray:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    get_size_dict(_SCREAMING_SNAKE_CASE)
								return center_crop(_SCREAMING_SNAKE_CASE ,						size=(size["""height"""], size["""width"""]) ,						data_format=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:Any ,						_SCREAMING_SNAKE_CASE				:Dict ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:List[str] = None ,						**_SCREAMING_SNAKE_CASE				:Optional[int] ,						)				-> Any:






								'''simple docstring'''






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




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] = None ,						**_SCREAMING_SNAKE_CASE				:List[Any] ,						)				-> np.ndarray:






								'''simple docstring'''






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





		def   _snake_case  (     self				:str ,						_SCREAMING_SNAKE_CASE				:List[str] ,						_SCREAMING_SNAKE_CASE				:Optional[Any] = None ,						_SCREAMING_SNAKE_CASE				:Tuple = None ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] = None ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] = None ,						_SCREAMING_SNAKE_CASE				:str = None ,						_SCREAMING_SNAKE_CASE				:List[Any] = None ,						_SCREAMING_SNAKE_CASE				:int = None ,						_SCREAMING_SNAKE_CASE				:List[Any] = None ,						_SCREAMING_SNAKE_CASE				:int = None ,						_SCREAMING_SNAKE_CASE				:Optional[int] = None ,						_SCREAMING_SNAKE_CASE				:int = None ,						_SCREAMING_SNAKE_CASE				:List[str] = None ,						_SCREAMING_SNAKE_CASE				:Optional[Any] = ChannelDimension.FIRST ,						**_SCREAMING_SNAKE_CASE				:str ,						)				-> PIL.Image.Image:






								'''simple docstring'''






								UpperCamelCase_    :  str				    =    do_resize if do_resize is not None else self.do_resize
								UpperCamelCase_    :  int				    =    size if size is not None else self.size
								UpperCamelCase_    :  Tuple				    =    get_size_dict(_SCREAMING_SNAKE_CASE ,						default_to_square=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Optional[int]				    =    resample if resample is not None else self.resample
								UpperCamelCase_    :  Dict				    =    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_    :  str				    =    get_size_dict(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Tuple				    =    do_rescale if do_rescale is not None else self.do_rescale
								UpperCamelCase_    :  Optional[int]				    =    rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCamelCase_    :  Tuple				    =    do_normalize if do_normalize is not None else self.do_normalize
								UpperCamelCase_    :  Optional[int]				    =    image_mean if image_mean is not None else self.image_mean
								UpperCamelCase_    :  Any				    =    image_std if image_std is not None else self.image_std
								UpperCamelCase_    :  Optional[Any]				    =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCamelCase_    :  Optional[int]				    =    make_list_of_images(_SCREAMING_SNAKE_CASE)

								if not valid_images(_SCREAMING_SNAKE_CASE):
														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.""")

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
														UpperCamelCase_    :  Union[str, Any]				    =    [convert_to_rgb(_SCREAMING_SNAKE_CASE) for image in images]

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

								if do_resize:
														UpperCamelCase_    :  int				    =    [self.resize(image=_SCREAMING_SNAKE_CASE ,						size=_SCREAMING_SNAKE_CASE ,						resample=_SCREAMING_SNAKE_CASE) for image in images]

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

								if do_rescale:
														UpperCamelCase_    :  int				    =    [self.rescale(image=_SCREAMING_SNAKE_CASE ,						scale=_SCREAMING_SNAKE_CASE) for image in images]

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

								UpperCamelCase_    :  Optional[Any]				    =    [to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE) for image in images]

								UpperCamelCase_    :  Tuple				    =    {'''pixel_values''': images}
								return BatchFeature(data=_SCREAMING_SNAKE_CASE ,						tensor_type=_SCREAMING_SNAKE_CASE)






'''simple docstring'''


from __future__ import annotations

from typing import Any




class 						__snake_case :




		def __init__(     self				:int ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:  # noqa: A002






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    item
								UpperCamelCase_    :  int				    =    next







class 						__snake_case :




		def __init__(     self				:Optional[Any])				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  Node | None				    =    None
								UpperCamelCase_    :  Tuple				    =    0




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    Node(_SCREAMING_SNAKE_CASE ,						self.head)
								self.size += 1




		def   _snake_case  (     self				:int)				-> Any:






								'''simple docstring'''






								if self.head is None:
														return None
								else:
														UpperCamelCase_    :  Any				    =    self.head.item
														UpperCamelCase_    :  List[str]				    =    self.head.next
														self.size -= 1
														return item




		def   _snake_case  (     self				:int)				-> bool:






								'''simple docstring'''






								return self.head is None




		def __str__(     self				:Any)				-> str:






								'''simple docstring'''






								if self.is_empty():
														return ""
								else:
														UpperCamelCase_    :  List[str]				    =    self.head
														UpperCamelCase_    :  Tuple				    =    """"""
														UpperCamelCase_    :  list[str]				    =    []
														while iterate:
																				item_list.append(str(iterate.item))
																				UpperCamelCase_    :  Tuple				    =    iterate.next

														UpperCamelCase_    :  Optional[int]				    =    """ --> """.join(_SCREAMING_SNAKE_CASE)

														return item_str





		def __len__(     self				:List[Any])				-> int:






								'''simple docstring'''






								return self.size




