


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


snake_case__				:	Tuple					       =					logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["pixel_values"]



       def __init__(						self				:				Dict		,				__a				:				bool = True		,				__a				:				Union[int, float] = 1 / 255		,				__a				:				bool = True		,				__a				:				int = 8		,				**__a				:				Union[str, Any]		,				)					->None:
          super().__init__(**__a							)

          lowerCamelCase_						:   List[Any]		= do_rescale
          lowerCamelCase_						:   Dict		= rescale_factor
          lowerCamelCase_						:   Dict		= do_pad
          lowerCamelCase_						:   Any		= pad_size



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				np.ndarray		,				__a				:				float		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict							)					->np.ndarray:
          return rescale(__a		,				scale=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				np.ndarray		,				__a				:				int		,				__a				:				Optional[Union[str, ChannelDimension]] = None							)					->Dict:
          lowerCamelCase_,	lowerCamelCase_						:   str		= get_image_size(__a							)
          lowerCamelCase_						:   List[str]		= (old_height // size + 1) * size - old_height
          lowerCamelCase_						:   List[Any]		= (old_width // size + 1) * size - old_width

          return pad(__a		,				((0, pad_height), (0, pad_width))		,				mode="""symmetric"""		,				data_format=__a							)




       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				ImageInput		,				__a				:				Optional[bool] = None		,				__a				:				Optional[float] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[int] = None		,				__a				:				Optional[Union[str, TensorType]] = None		,				__a				:				Union[str, ChannelDimension] = ChannelDimension.FIRST		,				**__a				:				Tuple		,				)					->List[Any]:
          lowerCamelCase_						:   List[str]		= do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase_						:   Union[str, Any]		= rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase_						:   Optional[int]		= do_pad if do_pad is not None else self.do_pad
          lowerCamelCase_						:   Dict		= pad_size if pad_size is not None else self.pad_size

          lowerCamelCase_						:   Tuple		= 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_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          # All transformations expect numpy arrays.
          lowerCamelCase_						:   Union[str, Any]		= [to_numpy_array(__a							) for image in images]

          if do_rescale:
             lowerCamelCase_						:   Optional[Any]		= [self.rescale(image=__a		,				scale=__a							) for image in images]

          if do_pad:
             lowerCamelCase_						:   Tuple		= [self.pad(__a		,				size=__a							) for image in images]

          lowerCamelCase_						:   int		= [to_channel_dimension_format(__a		,				__a							) for image in images]

          lowerCamelCase_						:   str		= {"""pixel_values""": images}
          return BatchFeature(data=__a		,				tensor_type=__a							)









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



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



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



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





