
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


A__   :		Tuple 		=logging.get_logger(__name__)


if is_vision_available():
     import PIL







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''pixel_values''']
 def __init__(   self			:    Union[str, Any]				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    PILImageResampling = PILImageResampling.BICUBIC				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Union[int, float] = 1 / 2_55				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    bool = True				,   **lowerCamelCase			:    str				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )
   __A		:     str												=		size if size is not None else {"""shortest_edge""": 2_24}
   __A		:     Tuple												=		get_size_dict(lowerCamelCase				,   default_to_square=lowerCamelCase       )
   __A		:     Optional[Any]												=		crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
   __A		:     Union[str, Any]												=		get_size_dict(lowerCamelCase				,   default_to_square=lowerCamelCase				,   param_name="""crop_size"""       )

   __A		:     List[Any]												=		do_resize
   __A		:     int												=		size
   __A		:     int												=		resample
   __A		:     Dict												=		do_center_crop
   __A		:     int												=		crop_size
   __A		:     Optional[int]												=		do_rescale
   __A		:     Union[str, Any]												=		rescale_factor
   __A		:     Tuple												=		do_normalize
   __A		:     Optional[Any]												=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __A		:     Tuple												=		image_std if image_std is not None else OPENAI_CLIP_STD
   __A		:     int												=		do_convert_rgb
 def 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Dict[str, int]				,   lowerCamelCase			:    PILImageResampling = PILImageResampling.BICUBIC				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   __A		:     Dict												=		get_size_dict(lowerCamelCase				,   default_to_square=lowerCamelCase       )
   if "shortest_edge" not in size:
     raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"       )
   __A		:     int												=		get_resize_output_image_size(lowerCamelCase				,   size=size["""shortest_edge"""]				,   default_to_square=lowerCamelCase       )
   return resize(lowerCamelCase				,   size=lowerCamelCase				,   resample=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Dict[str, int]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   __A		:     str												=		get_size_dict(lowerCamelCase       )
   if "height" not in size or "width" not in size:
     raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"       )
   return center_crop(lowerCamelCase				,   size=(size["""height"""], size["""width"""])				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Union[int, float]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    int				,   ):



   """simple docstring"""





   return rescale(lowerCamelCase				,   scale=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Union[float, List[float]]				,   lowerCamelCase			:    Union[float, List[float]]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





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






 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    ImageInput				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    PILImageResampling = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    int = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    Optional[ChannelDimension] = ChannelDimension.FIRST				,   **lowerCamelCase			:    List[str]				,   ):



   """simple docstring"""





   __A		:     List[Any]												=		do_resize if do_resize is not None else self.do_resize
   __A		:     Dict												=		size if size is not None else self.size
   __A		:     List[str]												=		get_size_dict(lowerCamelCase				,   param_name="""size"""				,   default_to_square=lowerCamelCase       )
   __A		:     Optional[Any]												=		resample if resample is not None else self.resample
   __A		:     List[Any]												=		do_center_crop if do_center_crop is not None else self.do_center_crop
   __A		:     Tuple												=		crop_size if crop_size is not None else self.crop_size
   __A		:     Tuple												=		get_size_dict(lowerCamelCase				,   param_name="""crop_size"""				,   default_to_square=lowerCamelCase       )
   __A		:     Union[str, Any]												=		do_rescale if do_rescale is not None else self.do_rescale
   __A		:     Union[str, Any]												=		rescale_factor if rescale_factor is not None else self.rescale_factor
   __A		:     Tuple												=		do_normalize if do_normalize is not None else self.do_normalize
   __A		:     int												=		image_mean if image_mean is not None else self.image_mean
   __A		:     Any												=		image_std if image_std is not None else self.image_std
   __A		:     Union[str, Any]												=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __A		:     Optional[int]												=		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:
     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:
     __A		:     Dict												=		[convert_to_rgb(lowerCamelCase       ) for image in images]

   # All transformations expect numpy arrays.
   __A		:     List[str]												=		[to_numpy_array(lowerCamelCase       ) for image in images]

   if do_resize:
     __A		:     Dict												=		[self.resize(image=lowerCamelCase				,   size=lowerCamelCase				,   resample=lowerCamelCase       ) for image in images]

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

   if do_rescale:
     __A		:     List[str]												=		[self.rescale(image=lowerCamelCase				,   scale=lowerCamelCase       ) for image in images]

   if do_normalize:
     __A		:     Any												=		[self.normalize(image=lowerCamelCase				,   mean=lowerCamelCase				,   std=lowerCamelCase       ) for image in images]

   __A		:     Union[str, Any]												=		[to_channel_dimension_format(lowerCamelCase				,   lowerCamelCase       ) for image in images]

   __A		:     Any												=		{"""pixel_values""": images}
   return BatchFeature(data=lowerCamelCase				,   tensor_type=lowerCamelCase       )





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[Any]:





  """simple docstring"""

  if not head:
    return True
  # split the list to two parts
  __A							,					__A		:     Any												=		head.next, head
  while fast and fast.next:
    __A		:     List[str]												=		fast.next.next
    __A		:     List[Any]												=		slow.next
  __A		:     int												=		slow.next
  __A		:     List[str]												=		None  # Don't forget here! But forget still works!
  # reverse the second part
  __A		:     Dict												=		None
  while second:
    __A		:     Dict												=		second.next
    __A		:     str												=		node
    __A		:     int												=		second
    __A		:     Optional[int]												=		nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    __A		:     List[Any]												=		node.next
    __A		:     Tuple												=		head.next
  return True







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Optional[int]:





  """simple docstring"""

  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  __A		:     int												=		head
  while fast and fast.next:
    __A							,					__A		:     Optional[int]												=		fast.next.next, slow.next

  # 2. Push the second half into the stack
  __A		:     Union[str, Any]												=		[slow.val]
  while slow.next:
    __A		:     List[str]												=		slow.next
    stack.append(slow.val )

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    __A		:     Tuple												=		cur.next

  return True







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Any:





  """simple docstring"""

  if not head or not head.next:
    return True
  __A		:     List[Any]												=		{}
  __A		:     Optional[int]												=		0
  while head:
    if head.val in d:
      d[head.val].append(__SCREAMING_SNAKE_CASE )
    else:
      __A		:     Tuple												=		[pos]
    __A		:     List[Any]												=		head.next
    pos += 1
  __A		:     List[Any]												=		pos - 1
  __A		:     List[str]												=		0
  for v in d.values():
    if len(__SCREAMING_SNAKE_CASE ) % 2 != 0:
      middle += 1
    else:
      __A		:     List[Any]												=		0
      for i in range(0			,     len(__SCREAMING_SNAKE_CASE ) ):
        if v[i] + v[len(__SCREAMING_SNAKE_CASE ) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True



