



"""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 (
    center_crop,
    get_resize_output_image_size,
    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


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				["pixel_values"]







		def __init__(	self						:  str			,_snake_case						:  bool = True			,_snake_case						:  Optional[Dict[str, int]] = None			,_snake_case						:  PILImageResampling = PILImageResampling.BILINEAR			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  bool = True			,_snake_case						:  Union[int, float] = 1 / 255			,_snake_case						:  bool = True			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,**_snake_case						:  Optional[int]			,)					->		None:






				"""simple docstring"""



				super().__init__(**__SCREAMING_SNAKE_CASE						)
				lowercase__   :    Any				=			size if size is not None else {'''shortest_edge''': 256}
				lowercase__   :    Tuple				=			get_size_dict(__SCREAMING_SNAKE_CASE			,default_to_square=__SCREAMING_SNAKE_CASE						)
				lowercase__   :    Any				=			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				lowercase__   :    Union[str, Any]				=			get_size_dict(__SCREAMING_SNAKE_CASE						)
				lowercase__   :    Optional[int]				=			do_resize
				lowercase__   :    List[Any]				=			size
				lowercase__   :    Tuple				=			resample
				lowercase__   :    Union[str, Any]				=			do_center_crop
				lowercase__   :    List[Any]				=			crop_size
				lowercase__   :    Union[str, Any]				=			do_rescale
				lowercase__   :    str				=			rescale_factor
				lowercase__   :    List[Any]				=			do_normalize
				lowercase__   :    Optional[Any]				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				lowercase__   :    List[str]				=			image_std if image_std is not None else IMAGENET_STANDARD_STD







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






				"""simple docstring"""



				lowercase__   :    Tuple				=			get_size_dict(__SCREAMING_SNAKE_CASE			,default_to_square=__SCREAMING_SNAKE_CASE						)
				if "shortest_edge" not in size:
						raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
				lowercase__   :    Tuple				=			get_resize_output_image_size(__SCREAMING_SNAKE_CASE			,size=size['''shortest_edge''']			,default_to_square=__SCREAMING_SNAKE_CASE						)
				return resize(__SCREAMING_SNAKE_CASE			,size=__SCREAMING_SNAKE_CASE			,resample=__SCREAMING_SNAKE_CASE			,data_format=__SCREAMING_SNAKE_CASE			,**__SCREAMING_SNAKE_CASE						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Any			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    Any				=			get_size_dict(__SCREAMING_SNAKE_CASE						)
				return center_crop(__SCREAMING_SNAKE_CASE			,size=(size['''height'''], size['''width'''])			,data_format=__SCREAMING_SNAKE_CASE			,**__SCREAMING_SNAKE_CASE						)







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  np.ndarray			,_snake_case						:  float			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  str						)					->		np.ndarray:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  np.ndarray			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Union[float, List[float]]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Dict			,)					->		np.ndarray:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  ImageInput			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = None			,_snake_case						:  bool = None			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[float] = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[float, List[float]]] = None			,_snake_case						:  Optional[Union[str, TensorType]] = None			,_snake_case						:  Union[str, ChannelDimension] = ChannelDimension.FIRST			,**_snake_case						:  Dict			,)					->		str:






				"""simple docstring"""



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

				lowercase__   :    Dict				=			make_list_of_images(__SCREAMING_SNAKE_CASE						)

				if not valid_images(__SCREAMING_SNAKE_CASE						):
						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.
				lowercase__   :    List[Any]				=			[to_numpy_array(__SCREAMING_SNAKE_CASE						) for image in images]

				if do_resize:
						lowercase__   :    Tuple				=			[self.resize(image=__SCREAMING_SNAKE_CASE			,size=__SCREAMING_SNAKE_CASE			,resample=__SCREAMING_SNAKE_CASE						) for image in images]

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

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

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

				lowercase__   :    Any				=			[to_channel_dimension_format(__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE						) for image in images]

				lowercase__   :    Dict				=			{'''pixel_values''': images}
				return BatchFeature(data=__SCREAMING_SNAKE_CASE			,tensor_type=__SCREAMING_SNAKE_CASE						)



'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






