



"""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 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


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Optional[int]		    =   ["pixel_values"]






	def __init__(			self							,UpperCAmelCase_ = True							,UpperCAmelCase_ = None							,UpperCAmelCase_ = PILImageResampling.BILINEAR							,UpperCAmelCase_ = True							,UpperCAmelCase_ = 1 / 2_55							,UpperCAmelCase_ = True							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,**UpperCAmelCase_							,):
			super().__init__(**UpperCAmelCase_					)
			_lowercase							:      Tuple			=					size if size is not None else {"""height""": 2_24, """width""": 2_24}
			_lowercase							:      Dict			=					get_size_dict(UpperCAmelCase_					)
			_lowercase							:      Dict			=					do_resize
			_lowercase							:      Tuple			=					do_rescale
			_lowercase							:      Union[str, Any]			=					do_normalize
			_lowercase							:      List[str]			=					size
			_lowercase							:      str			=					resample
			_lowercase							:      Optional[Any]			=					rescale_factor
			_lowercase							:      Optional[Any]			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			_lowercase							:      Optional[int]			=					image_std if image_std is not None else IMAGENET_STANDARD_STD






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_ = PILImageResampling.BILINEAR							,UpperCAmelCase_ = None							,**UpperCAmelCase_							,):
			_lowercase							:      str			=					get_size_dict(UpperCAmelCase_					)
			if "height" not in size or "width" not in size:
					raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""					)
			return resize(
			    UpperCAmelCase_							,size=(size["""height"""], size["""width"""])							,resample=UpperCAmelCase_							,data_format=UpperCAmelCase_							,**UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_ = None							,**UpperCAmelCase_					):
			return rescale(UpperCAmelCase_							,scale=UpperCAmelCase_							,data_format=UpperCAmelCase_							,**UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_ = None							,**UpperCAmelCase_							,):
			return normalize(UpperCAmelCase_							,mean=UpperCAmelCase_							,std=UpperCAmelCase_							,data_format=UpperCAmelCase_							,**UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = None							,UpperCAmelCase_ = ChannelDimension.FIRST							,**UpperCAmelCase_							,):
			_lowercase							:      List[Any]			=					do_resize if do_resize is not None else self.do_resize
			_lowercase							:      Tuple			=					do_rescale if do_rescale is not None else self.do_rescale
			_lowercase							:      Optional[Any]			=					do_normalize if do_normalize is not None else self.do_normalize
			_lowercase							:      List[Any]			=					resample if resample is not None else self.resample
			_lowercase							:      int			=					rescale_factor if rescale_factor is not None else self.rescale_factor
			_lowercase							:      Tuple			=					image_mean if image_mean is not None else self.image_mean
			_lowercase							:      Optional[int]			=					image_std if image_std is not None else self.image_std

			_lowercase							:      Optional[Any]			=					size if size is not None else self.size
			_lowercase							:      List[Any]			=					get_size_dict(UpperCAmelCase_					)

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

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

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

			if do_rescale:
					_lowercase							:      Tuple			=					[self.rescale(image=UpperCAmelCase_							,scale=UpperCAmelCase_					) for image in images]

			if do_normalize:
					_lowercase							:      Tuple			=					[self.normalize(image=UpperCAmelCase_							,mean=UpperCAmelCase_							,std=UpperCAmelCase_					) for image in images]

			_lowercase							:      Optional[Any]			=					[to_channel_dimension_format(UpperCAmelCase_							,UpperCAmelCase_					) for image in images]

			_lowercase							:      Tuple			=					{"""pixel_values""": images}
			return BatchFeature(data=UpperCAmelCase_							,tensor_type=UpperCAmelCase_					)











"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase     			=  TypeVar("""T""")







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self	,A					):
          UpperCAmelCase							=       data  # Assign data
          UpperCAmelCase							=       None  # Initialize next as null
          UpperCAmelCase							=       None  # Initialize prev as null







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self					):
          UpperCAmelCase							=       None






    def 				_UpperCamelCase							( self	,A					):
          if self.head is None:
                UpperCAmelCase							=       Node(A					)
          else:
                UpperCAmelCase							=       Node(A					)
                UpperCAmelCase							=       new_node
                UpperCAmelCase							=       self.head
                UpperCAmelCase							=       None
                UpperCAmelCase							=       new_node






    def 				_UpperCamelCase							( self					):
          if self.head is None:
                return None
          else:
                assert self.head is not None
                UpperCAmelCase							=       self.head.data
                UpperCAmelCase							=       self.head.next
                if self.head is not None:
                      UpperCAmelCase							=       None
                return temp






    def 				_UpperCamelCase							( self					):
          return self.head.data if self.head is not None else None






    def __len__( self					):
          UpperCAmelCase							=       self.head
          UpperCAmelCase							=       0
          while temp is not None:
                count += 1
                UpperCAmelCase							=       temp.next
          return count






    def 				_UpperCamelCase							( self					):
          return self.head is None






    def 				_UpperCamelCase							( self					):
          print("""stack elements are:"""					)
          UpperCAmelCase							=       self.head
          while temp is not None:
                print(temp.data	,end="""->"""					)
                UpperCAmelCase							=       temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  _UpperCamelCase     			=  Stack()

  # Insert 4 at the beginning. So stack becomes 4->None
  print("""Stack operations using Doubly LinkedList""")
  stack.push(4)

  # Insert 5 at the beginning. So stack becomes 4->5->None
  stack.push(5)

  # Insert 6 at the beginning. So stack becomes 4->5->6->None
  stack.push(6)

  # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
  stack.push(7)

  # Print the stack
  stack.print_stack()

  # Print the top element
  print("""\nTop element is """, stack.top())

  # Print the stack size
  print("""Size of the stack is """, len(stack))

  # pop the top element
  stack.pop()

  # pop the top element
  stack.pop()

  # two elements have now been popped off
  stack.print_stack()

  # Print True if the stack is empty else False
  print("""\nstack is empty:""", stack.is_empty())



