






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


UpperCAmelCase__							  =						logging.get_logger(__name__)






class 						lowercase_   (      UpperCAmelCase_  ):






      '''simple docstring'''




      __snake_case	    =    ['''pixel_values''']







      def __init__( self :		Dict					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		Dict[str, int] = None					,   __UpperCAmelCase :		PILImageResampling = PIL.Image.BICUBIC					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		Dict[str, int] = None					,   __UpperCAmelCase :		Union[int, float] = 1 / 255					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		Optional[Union[float, List[float]]] = None					,   __UpperCAmelCase :		Optional[Union[float, List[float]]] = None					,   **__UpperCAmelCase :		Optional[int]					,   )       ->None:




       """simple docstring"""


       super().__init__(**__lowerCamelCase    )
       a						    = size if size is not None else {'''height''': 256, '''width''': 256}
       a						    = get_size_dict(__lowerCamelCase    )
       a						    = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       a						    = get_size_dict(__lowerCamelCase					,   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						    = 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







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




       """simple docstring"""


       a						    = get_size_dict(__lowerCamelCase    )
       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(
           __lowerCamelCase					,   size=(size['''height'''], size['''width'''])					,   resample=__lowerCamelCase					,   data_format=__lowerCamelCase					,   **__lowerCamelCase    )







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		np.ndarray					,   __UpperCAmelCase :		Dict[str, int]					,   __UpperCAmelCase :		Optional[Union[str, ChannelDimension]] = None					,   **__UpperCAmelCase :		List[Any]					,   )       ->np.ndarray:




       """simple docstring"""


       a						    = get_size_dict(__lowerCamelCase    )
       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(__lowerCamelCase					,   size=(size['''height'''], size['''width'''])					,   data_format=__lowerCamelCase					,   **__lowerCamelCase    )







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		np.ndarray					,   __UpperCAmelCase :		Union[int, float]					,   __UpperCAmelCase :		Optional[Union[str, ChannelDimension]] = None					,   **__UpperCAmelCase :		str					,   )       ->Optional[Any]:




       """simple docstring"""


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







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




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		ImageInput					,   __UpperCAmelCase :		bool = None					,   __UpperCAmelCase :		Dict[str, int] = None					,   __UpperCAmelCase :		Optional[Any]=None					,   __UpperCAmelCase :		bool = None					,   __UpperCAmelCase :		Dict[str, int] = None					,   __UpperCAmelCase :		bool = None					,   __UpperCAmelCase :		float = None					,   __UpperCAmelCase :		bool = None					,   __UpperCAmelCase :		Optional[Union[float, List[float]]] = None					,   __UpperCAmelCase :		Optional[Union[float, List[float]]] = None					,   __UpperCAmelCase :		Optional[Union[str, TensorType]] = None					,   __UpperCAmelCase :		ChannelDimension = ChannelDimension.FIRST					,   **__UpperCAmelCase :		Union[str, Any]					,   )       ->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						    = 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						    = size if size is not None else self.size
       a						    = get_size_dict(__lowerCamelCase    )
       a						    = crop_size if crop_size is not None else self.crop_size
       a						    = get_size_dict(__lowerCamelCase					,   param_name='''crop_size'''    )

       a						    = 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 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(__lowerCamelCase    ) for image in images]

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

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

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

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

       a						    = [to_channel_dimension_format(__lowerCamelCase					,   __lowerCamelCase    ) for image in images]

       a						    = {'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase					,   tensor_type=__lowerCamelCase    )









class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Dict					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Optional[Any]=None					,      __lowerCamelCase							:     Optional[Any]=None   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     data
			__lowercase											=     previous
			__lowercase											=     next_node




	def __str__(						self							:     int   )       -> str:

			'''simple docstring'''




			return F"{self.data}"




	def    UpperCAmelCase			(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.data




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




			return self.next






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			return self.previous





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Tuple					,      __lowerCamelCase							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     head




	def __iter__(						self							:     Union[str, Any]   )       -> List[Any]:

			'''simple docstring'''




			return self






	def    UpperCAmelCase			(						self							:     str   )       -> str:

			'''simple docstring'''




			if not self.current:
					raise StopIteration
			else:
					__lowercase											=     self.current.get_data()
					__lowercase											=     self.current.get_next()
					return value





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     List[str]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     None  # First node in list
			__lowercase											=     None  # Last node in list




	def __str__(						self							:     int   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.head
			__lowercase											=     []
			while current is not None:
					nodes.append(current.get_data()   )
					__lowercase											=     current.get_next()
			return " ".join(str(__lowerCamelCase   ) for node in nodes   )




	def __contains__(						self							:     Tuple					,      __lowerCamelCase							:     int   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.head
			while current:
					if current.get_data() == value:
							return True
					__lowercase											=     current.get_next()
			return False




	def __iter__(						self							:     Optional[int]   )       -> List[str]:

			'''simple docstring'''




			return LinkedListIterator(self.head   )




	def    UpperCAmelCase			(						self							:     Any   )       -> str:

			'''simple docstring'''




			if self.head:
					return self.head.get_data()
			return None




	def    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			if self.tail:
					return self.tail.get_data()
			return None




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if self.head is None:
					__lowercase											=     node
					__lowercase											=     node
			else:
					self.insert_before_node(self.head					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if self.head is None:
					self.set_head(__lowerCamelCase   )
			else:
					self.insert_after_node(self.tail					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     Node(__lowerCamelCase   )
			if self.head is None:
					self.set_head(__lowerCamelCase   )
			else:
					self.set_tail(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Node					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			__lowercase											=     node
			__lowercase											=     node.previous

			if node.get_previous() is None:
					__lowercase											=     node_to_insert
			else:
					__lowercase											=     node_to_insert

			__lowercase											=     node_to_insert




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Node					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			__lowercase											=     node
			__lowercase											=     node.next

			if node.get_next() is None:
					__lowercase											=     node_to_insert
			else:
					__lowercase											=     node_to_insert

			__lowercase											=     node_to_insert




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     1
			__lowercase											=     Node(__lowerCamelCase   )
			__lowercase											=     self.head
			while node:
					if current_position == position:
							self.insert_before_node(__lowerCamelCase					,      __lowerCamelCase   )
							return
					current_position += 1
					__lowercase											=     node.next
			self.insert_after_node(self.tail					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     int   )       -> Node:

			'''simple docstring'''




			__lowercase											=     self.head
			while node:
					if node.get_data() == item:
							return node
					__lowercase											=     node.get_next()
			raise Exception('Node not found'   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     List[str]   )       -> Dict:

			'''simple docstring'''




			if (node := self.get_node(__lowerCamelCase   )) is not None:
					if node == self.head:
							__lowercase											=     self.head.get_next()

					if node == self.tail:
							__lowercase											=     self.tail.get_previous()

					self.remove_node_pointers(__lowerCamelCase   )




	@staticmethod
	def    UpperCAmelCase			(						__lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if node.get_next():
					__lowercase											=     node.previous

			if node.get_previous():
					__lowercase											=     node.next

			__lowercase											=     None
			__lowercase											=     None






	def    UpperCAmelCase			(						self							:     str   )       -> List[str]:

			'''simple docstring'''




			return self.head is None





def  SCREAMING_SNAKE_CASE				(      )		->    None:
		pass


if __name__ == "__main__":
		import doctest

		doctest.testmod()




