


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


__UpperCamelCase							:							Union[str, Any]    							=					logging.get_logger(__name__)


if is_vision_available():
       import PIL

class    __magic_name__						(  lowercase__):
     A:		List[str]								=   ["""pixel_values"""]


     def __init__(   self				:  List[str]				,						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 / 255				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Union[float, List[float]]] = None				,						lowerCamelCase__				:  Optional[Union[float, List[float]]] = None				,						lowerCamelCase__				:  bool = True				,						**lowerCamelCase__				:  List[str]				,						)	->   None:





      '''simple docstring'''






      super().__init__(**lowerCamelCase__      )
      UpperCamelCase__       :     List[Any] 							= size if size is not None else {'''shortest_edge''': 224}
      UpperCamelCase__       :     Tuple 							= get_size_dict(lowerCamelCase__				,						default_to_square=lowerCamelCase__      )
      UpperCamelCase__       :     Tuple 							= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      UpperCamelCase__       :     List[Any] 							= get_size_dict(lowerCamelCase__      )

      UpperCamelCase__       :     Dict 							= do_resize
      UpperCamelCase__       :     List[Any] 							= size
      UpperCamelCase__       :     Any 							= resample
      UpperCamelCase__       :     Union[str, Any] 							= do_center_crop
      UpperCamelCase__       :     List[Any] 							= crop_size
      UpperCamelCase__       :     Optional[Any] 							= do_rescale
      UpperCamelCase__       :     Optional[Any] 							= rescale_factor
      UpperCamelCase__       :     List[str] 							= do_normalize
      UpperCamelCase__       :     str 							= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      UpperCamelCase__       :     Tuple 							= image_std if image_std is not None else OPENAI_CLIP_STD
      UpperCamelCase__       :     List[str] 							= do_convert_rgb


     def   UpperCAmelCase__						(   self				:  Dict				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Dict[str, int]				,						lowerCamelCase__				:  PILImageResampling = PILImageResampling.BICUBIC				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  str				,						)	->   np.ndarray:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= get_size_dict(lowerCamelCase__				,						default_to_square=lowerCamelCase__      )
      UpperCamelCase__       :     Union[str, Any] 							= get_resize_output_image_size(
          lowerCamelCase__				,						size=(size['''height'''], size['''width'''])				,						default_to_square=lowerCamelCase__      )
      return resize(lowerCamelCase__				,						size=lowerCamelCase__				,						resample=lowerCamelCase__				,						data_format=lowerCamelCase__				,						**lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Dict[str, int]				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  int				,						)	->   np.ndarray:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= get_size_dict(lowerCamelCase__      )
      return center_crop(lowerCamelCase__				,						size=(size['''height'''], size['''width'''])				,						data_format=lowerCamelCase__				,						**lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Union[int, float]				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  Tuple				,						)	->   Optional[int]:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  np.ndarray				,						lowerCamelCase__				:  Union[float, List[float]]				,						lowerCamelCase__				:  Union[float, List[float]]				,						lowerCamelCase__				:  Optional[Union[str, ChannelDimension]] = None				,						**lowerCamelCase__				:  Tuple				,						)	->   np.ndarray:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						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[Any]				,						)	->   PIL.Image.Image:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__       :     str 							= size if size is not None else self.size
      UpperCamelCase__       :     Dict 							= get_size_dict(lowerCamelCase__				,						default_to_square=lowerCamelCase__      )
      UpperCamelCase__       :     List[str] 							= resample if resample is not None else self.resample
      UpperCamelCase__       :     List[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__       :     Tuple 							= get_size_dict(lowerCamelCase__      )
      UpperCamelCase__       :     Tuple 							= do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__       :     Optional[int] 							= rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__       :     Any 							= do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__       :     int 							= image_mean if image_mean is not None else self.image_mean
      UpperCamelCase__       :     List[str] 							= image_std if image_std is not None else self.image_std
      UpperCamelCase__       :     List[Any] 							= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      UpperCamelCase__       :     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:
       UpperCamelCase__       :     Dict 							= [convert_to_rgb(lowerCamelCase__      ) for image in images]

      # All transformations expect numpy arrays.
      UpperCamelCase__       :     Tuple 							= [to_numpy_array(lowerCamelCase__      ) for image in images]

      if do_resize:
       UpperCamelCase__       :     str 							= [self.resize(image=lowerCamelCase__				,						size=lowerCamelCase__				,						resample=lowerCamelCase__      ) for image in images]

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

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

      if do_normalize:
       UpperCamelCase__       :     str 							= [self.normalize(image=lowerCamelCase__				,						mean=lowerCamelCase__				,						std=lowerCamelCase__      ) for image in images]

      UpperCamelCase__       :     int 							= [to_channel_dimension_format(lowerCamelCase__				,						lowerCamelCase__      ) for image in images]

      UpperCamelCase__       :     int 							= {'''pixel_values''': images}
      return BatchFeature(data=lowerCamelCase__				,						tensor_type=lowerCamelCase__      )








from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_    :       List[str]       =							TypeVar('''T''')







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     pass







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     pass







class 		__UpperCAmelCase(		Generic[T]			):





     """simple docstring"""



     def __init__(   self			:							str    ,					A			:							int = 10     )      ->      str:






         """simple docstring"""
         a__  			=							[]
         a__  			=							limit

     def __bool__(   self			:							Dict     )      ->      bool:






         """simple docstring"""
         return bool(self.stack     )

     def __str__(   self			:							str     )      ->      str:






         """simple docstring"""
         return str(self.stack     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							T     )      ->      None:






         """simple docstring"""
         if len(self.stack     ) >= self.limit:
             raise StackOverflowError
         self.stack.append(A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      T:






         """simple docstring"""
         if not self.stack:
             raise StackUnderflowError
         return self.stack.pop()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      T:






         """simple docstring"""
         if not self.stack:
             raise StackUnderflowError
         return self.stack[-1]

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      bool:






         """simple docstring"""
         return not bool(self.stack     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      bool:






         """simple docstring"""
         return self.size() == self.limit

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      int:






         """simple docstring"""
         return len(self.stack     )






     def __contains__(   self			:							int    ,					A			:							T     )      ->      bool:






         """simple docstring"""
         return item in self.stack






def 							__lowerCAmelCase			():
    a__  			=							Stack(10		)
    assert bool(UpperCAmelCase_		) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(UpperCAmelCase_		) == "[]"

    try:
        a__  			=							stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        a__  			=							stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10		):
        assert stack.size() == i
        stack.push(UpperCAmelCase_		)

    assert bool(UpperCAmelCase_		)
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(UpperCAmelCase_		) == str(list(range(10		)		)		)
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100		)
    assert str(UpperCAmelCase_		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

    try:
        stack.push(200		)
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()






