



"""simple docstring"""





from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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  _lowerCAmelCase     (					__UpperCamelCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							['''pixel_values''']





		def __init__(       self				,     _lowercase = True				,     _lowercase = None				,     _lowercase = PILImageResampling.BICUBIC				,     _lowercase = True				,     _lowercase = None				,     _lowercase = True				,     _lowercase = 1 / 2_5_5				,     _lowercase = True				,     _lowercase = IMAGENET_DEFAULT_MEAN				,     _lowercase = IMAGENET_DEFAULT_STD				,     **_lowercase				,     )			->    None:







								'''simple docstring'''

								super().__init__(**__UpperCAmelCase )
								snake_case_ :			Optional[int]            =      size if size is not None else {"shortest_edge": 2_2_4}
								snake_case_ :			Any            =      get_size_dict(__UpperCAmelCase				,     default_to_square=__UpperCAmelCase )
								snake_case_ :			Tuple            =      crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
								snake_case_ :			Tuple            =      get_size_dict(__UpperCAmelCase				,     param_name="""crop_size""" )

								snake_case_ :			int            =      do_resize
								snake_case_ :			Tuple            =      size
								snake_case_ :			Any            =      resample
								snake_case_ :			Optional[int]            =      do_center_crop
								snake_case_ :			Any            =      crop_size
								snake_case_ :			Union[str, Any]            =      do_rescale
								snake_case_ :			Union[str, Any]            =      rescale_factor
								snake_case_ :			int            =      do_normalize
								snake_case_ :			Tuple            =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								snake_case_ :			Union[str, Any]            =      image_std if image_std is not None else IMAGENET_DEFAULT_STD





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = PILImageResampling.BICUBIC				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								snake_case_ :			int            =      get_size_dict(__UpperCAmelCase				,     default_to_square=__UpperCAmelCase )
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
														snake_case_ :			Union[str, Any]            =      int((2_5_6 / 2_2_4) * size["""shortest_edge"""] )
														snake_case_ :			Optional[Any]            =      get_resize_output_image_size(__UpperCAmelCase				,     size=__UpperCAmelCase				,     default_to_square=__UpperCAmelCase )
														snake_case_ :			int            =      {"height": output_size[0], "width": output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
														raise ValueError(
														    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}' )
								return resize(
								    __UpperCAmelCase				,     size=(size_dict["""height"""], size_dict["""width"""])				,     resample=__UpperCAmelCase				,     data_format=__UpperCAmelCase				,     **__UpperCAmelCase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								snake_case_ :			Any            =      get_size_dict(__UpperCAmelCase )
								if "height" not in size or "width" not in size:
														raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}' )
								return center_crop(__UpperCAmelCase				,     size=(size["""height"""], size["""width"""])				,     data_format=__UpperCAmelCase				,     **__UpperCAmelCase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = ChannelDimension.FIRST				,     **_lowercase				,     )			->    BatchFeature:







								'''simple docstring'''

								snake_case_ :			str            =      do_resize if do_resize is not None else self.do_resize
								snake_case_ :			int            =      resample if resample is not None else self.resample
								snake_case_ :			Optional[Any]            =      do_center_crop if do_center_crop is not None else self.do_center_crop
								snake_case_ :			Dict            =      do_rescale if do_rescale is not None else self.do_rescale
								snake_case_ :			str            =      rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_ :			Any            =      do_normalize if do_normalize is not None else self.do_normalize
								snake_case_ :			Optional[Any]            =      image_mean if image_mean is not None else self.image_mean
								snake_case_ :			List[Any]            =      image_std if image_std is not None else self.image_std

								snake_case_ :			Dict            =      size if size is not None else self.size
								snake_case_ :			Optional[int]            =      get_size_dict(__UpperCAmelCase				,     default_to_square=__UpperCAmelCase )
								snake_case_ :			List[str]            =      crop_size if crop_size is not None else self.crop_size
								snake_case_ :			str            =      get_size_dict(__UpperCAmelCase				,     param_name="""crop_size""" )

								snake_case_ :			int            =      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.
								snake_case_ :			List[str]            =      [to_numpy_array(__UpperCAmelCase ) for image in images]

								if do_resize:
														snake_case_ :			int            =      [self.resize(__UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase ) for image in images]

								if do_center_crop:
														snake_case_ :			Union[str, Any]            =      [self.center_crop(__UpperCAmelCase				,     __UpperCAmelCase ) for image in images]

								if do_rescale:
														snake_case_ :			Union[str, Any]            =      [self.rescale(__UpperCAmelCase				,     __UpperCAmelCase ) for image in images]

								if do_normalize:
														snake_case_ :			int            =      [self.normalize(__UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase ) for image in images]

								snake_case_ :			Union[str, Any]            =      [to_channel_dimension_format(__UpperCAmelCase				,     __UpperCAmelCase ) for image in images]

								snake_case_ :			Optional[int]            =      {"pixel_values": images}
								return BatchFeature(data=__UpperCAmelCase				,     tensor_type=__UpperCAmelCase )









def     a_   (__A      )				->						Dict:



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







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



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





