

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''pixel_values''']




 def __init__(			self  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = PILImageResampling.BICUBIC  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = 1 / 255  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   super().__init__(**_UpperCAmelCase							)
   __UpperCAmelCase	:  Any		=     crop_size if crop_size is not None else {'height': 256, 'width': 256}
   __UpperCAmelCase	:  List[str]		=     get_size_dict(_UpperCAmelCase  ,  param_name='crop_size'							)
   __UpperCAmelCase	:  List[Any]		=     size if size is not None else {'height': 224, 'width': 224}
   __UpperCAmelCase	:  Optional[Any]		=     get_size_dict(_UpperCAmelCase							)

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




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  Optional[Any]		=     self.size if size is None else size
   __UpperCAmelCase	:  List[str]		=     get_size_dict(_UpperCAmelCase							)
   __UpperCAmelCase	:  Union[str, Any]		=     get_size_dict(_UpperCAmelCase  ,  param_name='crop_size'							)

   __UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     get_image_size(_UpperCAmelCase							)
   __UpperCAmelCase	:  List[str]		=     min(_UpperCAmelCase  ,  _UpperCAmelCase							)
   __UpperCAmelCase	:  Any		=     (size['height'] / crop_size['height']) * min_dim
   __UpperCAmelCase	:  List[str]		=     (size['width'] / crop_size['width']) * min_dim
   return center_crop(_UpperCAmelCase  ,  size=(cropped_height, cropped_width)  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = PIL.Image.BICUBIC  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  Any		=     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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   return rescale(_UpperCAmelCase  ,  scale=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   return normalize(_UpperCAmelCase  ,  mean=_UpperCAmelCase  ,  std=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = ChannelDimension.FIRST  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  Any		=     do_center_crop if do_center_crop is not None else self.do_center_crop
   __UpperCAmelCase	:  Union[str, Any]		=     crop_size if crop_size is not None else self.crop_size
   __UpperCAmelCase	:  Any		=     get_size_dict(_UpperCAmelCase  ,  param_name='crop_size'							)
   __UpperCAmelCase	:  Union[str, Any]		=     do_resize if do_resize is not None else self.do_resize
   __UpperCAmelCase	:  Optional[int]		=     size if size is not None else self.size
   __UpperCAmelCase	:  int		=     get_size_dict(_UpperCAmelCase							)
   __UpperCAmelCase	:  str		=     resample if resample is not None else self.resample
   __UpperCAmelCase	:  int		=     do_rescale if do_rescale is not None else self.do_rescale
   __UpperCAmelCase	:  str		=     rescale_factor if rescale_factor is not None else self.rescale_factor
   __UpperCAmelCase	:  Optional[int]		=     do_normalize if do_normalize is not None else self.do_normalize
   __UpperCAmelCase	:  List[Any]		=     image_mean if image_mean is not None else self.image_mean
   __UpperCAmelCase	:  Optional[Any]		=     image_std if image_std is not None else self.image_std

   __UpperCAmelCase	:  Dict		=     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_center_crop and crop_size is None:
     raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'							)

   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.'							)

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'							)

   # All transformations expect numpy arrays.
   __UpperCAmelCase	:  str		=     [to_numpy_array(_UpperCAmelCase							) for image in images]

   if do_center_crop:
     __UpperCAmelCase	:  Tuple		=     [self.center_crop(_UpperCAmelCase  ,  _UpperCAmelCase  ,  size=_UpperCAmelCase							) for image in images]

   if do_resize:
     __UpperCAmelCase	:  Optional[Any]		=     [self.resize(image=_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  resample=_UpperCAmelCase							) for image in images]

   if do_rescale:
     __UpperCAmelCase	:  Optional[int]		=     [self.rescale(image=_UpperCAmelCase  ,  scale=_UpperCAmelCase							) for image in images]

   if do_normalize:
     __UpperCAmelCase	:  List[str]		=     [self.normalize(image=_UpperCAmelCase  ,  mean=_UpperCAmelCase  ,  std=_UpperCAmelCase							) for image in images]

   __UpperCAmelCase	:  Tuple		=     [to_channel_dimension_format(_UpperCAmelCase  ,  _UpperCAmelCase							) for image in images]

   __UpperCAmelCase	:  Optional[Any]		=     {'pixel_values': images}
   return BatchFeature(data=_UpperCAmelCase  ,  tensor_type=_UpperCAmelCase							)











'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




