



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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase  :				int					=					logging.get_logger(__name__)





class 			__magic_name__ (				snake_case_    ):
		lowercase   :							Dict        =['''pixel_values''']






		def __init__(    self   :  str					,		UpperCamelCase__   :  Any = True					,		UpperCamelCase__   :  Union[str, Any] = None					,		UpperCamelCase__   :  Optional[Any] = PILImageResampling.BILINEAR					,		UpperCamelCase__   :  Any = True					,		UpperCamelCase__   :  Dict = None					,		UpperCamelCase__   :  List[str] = True					,		UpperCamelCase__   :  List[Any] = 1 / 2_55					,		UpperCamelCase__   :  Any = True					,		UpperCamelCase__   :  Any = None					,		UpperCamelCase__   :  Optional[Any] = None					,		**UpperCamelCase__   :  str					,		)					->       None:
								'''simple docstring'''







								super().__init__(**UpperCamelCase__   )
								UpperCAmelCase								=  size if size is not None else {"shortest_edge": 2_56}
								UpperCAmelCase								=  get_size_dict(UpperCamelCase__					,		default_to_square=UpperCamelCase__   )
								UpperCAmelCase								=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
								UpperCAmelCase								=  get_size_dict(UpperCamelCase__   )
								UpperCAmelCase								=  do_resize
								UpperCAmelCase								=  size
								UpperCAmelCase								=  resample
								UpperCAmelCase								=  do_center_crop
								UpperCAmelCase								=  crop_size
								UpperCAmelCase								=  do_rescale
								UpperCAmelCase								=  rescale_factor
								UpperCAmelCase								=  do_normalize
								UpperCAmelCase								=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase								=  image_std if image_std is not None else IMAGENET_STANDARD_STD






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]					,		UpperCamelCase__   :  List[Any]					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  Optional[Any] = PILImageResampling.BICUBIC					,		UpperCamelCase__   :  Optional[Any] = None					,		**UpperCamelCase__   :  Optional[int]					,		)					->       np.ndarray:
								'''simple docstring'''







								UpperCAmelCase								=  get_size_dict(UpperCamelCase__					,		default_to_square=UpperCamelCase__   )
								if "shortest_edge" not in size:
														raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
								UpperCAmelCase								=  get_resize_output_image_size(UpperCamelCase__					,		size=size["shortest_edge"]					,		default_to_square=UpperCamelCase__   )
								return resize(UpperCamelCase__					,		size=UpperCamelCase__					,		resample=UpperCamelCase__					,		data_format=UpperCamelCase__					,		**UpperCamelCase__   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  Any					,		UpperCamelCase__   :  str = None					,		**UpperCamelCase__   :  List[Any]					,		)					->       np.ndarray:
								'''simple docstring'''







								UpperCAmelCase								=  get_size_dict(UpperCamelCase__   )
								return center_crop(UpperCamelCase__					,		size=(size["height"], size["width"])					,		data_format=UpperCamelCase__					,		**UpperCamelCase__   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Any					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  List[Any] = None					,		**UpperCamelCase__   :  Tuple   )					->       np.ndarray:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]					,		UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  Tuple = None					,		**UpperCamelCase__   :  Any					,		)					->       np.ndarray:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]					,		UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  Optional[Any] = None					,		UpperCamelCase__   :  Optional[Any] = None					,		UpperCamelCase__   :  Union[str, Any] = None					,		UpperCamelCase__   :  Optional[int] = None					,		UpperCamelCase__   :  List[Any] = None					,		UpperCamelCase__   :  Dict = None					,		UpperCamelCase__   :  List[Any] = None					,		UpperCamelCase__   :  Any = None					,		UpperCamelCase__   :  Dict = None					,		UpperCamelCase__   :  Union[str, Any] = None					,		UpperCamelCase__   :  List[str] = None					,		UpperCamelCase__   :  str = ChannelDimension.FIRST					,		**UpperCamelCase__   :  Optional[Any]					,		)					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase								=  do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase								=  size if size is not None else self.size
								UpperCAmelCase								=  get_size_dict(UpperCamelCase__					,		default_to_square=UpperCamelCase__   )
								UpperCAmelCase								=  resample if resample is not None else self.resample
								UpperCAmelCase								=  do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase								=  crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase								=  get_size_dict(UpperCamelCase__   )
								UpperCAmelCase								=  do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase								=  rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase								=  do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase								=  image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase								=  image_std if image_std is not None else self.image_std

								UpperCAmelCase								=  make_list_of_images(UpperCamelCase__   )

								if not valid_images(UpperCamelCase__   ):
														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								=  [to_numpy_array(UpperCamelCase__   ) for image in images]

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

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

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

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

								UpperCAmelCase								=  [to_channel_dimension_format(UpperCamelCase__					,		UpperCamelCase__   ) for image in images]

								UpperCAmelCase								=  {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase__					,		tensor_type=UpperCamelCase__   )











class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





