'''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    :				List[Any]							     =	logging.get_logger(__name__)







class __snake_case       (		__SCREAMING_SNAKE_CASE	):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(					self    :       str ,			_lowercase    :       bool = True ,			_lowercase    :       Dict[str, int] = None ,			_lowercase    :       PILImageResampling = PILImageResampling.BICUBIC ,			_lowercase    :       bool = True ,			_lowercase    :       Dict[str, int] = None ,			_lowercase    :       bool = True ,			_lowercase    :       Union[int, float] = 1 / 2_55 ,			_lowercase    :       bool = True ,			_lowercase    :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,			_lowercase    :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






     super().__init__(**__snake_case   )
     SCREAMING_SNAKE_CASE__    							=   size if size is not None else {'''shortest_edge''': 2_24}
     SCREAMING_SNAKE_CASE__    							=   get_size_dict(__snake_case ,			default_to_square=__snake_case   )
     SCREAMING_SNAKE_CASE__    							=   crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
     SCREAMING_SNAKE_CASE__    							=   get_size_dict(__snake_case ,			param_name="""crop_size"""   )

     SCREAMING_SNAKE_CASE__    							=   do_resize
     SCREAMING_SNAKE_CASE__    							=   size
     SCREAMING_SNAKE_CASE__    							=   resample
     SCREAMING_SNAKE_CASE__    							=   do_center_crop
     SCREAMING_SNAKE_CASE__    							=   crop_size
     SCREAMING_SNAKE_CASE__    							=   do_rescale
     SCREAMING_SNAKE_CASE__    							=   rescale_factor
     SCREAMING_SNAKE_CASE__    							=   do_normalize
     SCREAMING_SNAKE_CASE__    							=   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     SCREAMING_SNAKE_CASE__    							=   image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def 			__a		(					self    :       Tuple ,			_lowercase    :       np.ndarray ,			_lowercase    :       Dict[str, int] ,			_lowercase    :       PILImageResampling = PILImageResampling.BICUBIC ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = None ,			**_lowercase    :       int ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   get_size_dict(__snake_case ,			default_to_square=__snake_case   )
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
       SCREAMING_SNAKE_CASE__    							=   int((2_56 / 2_24) * size["""shortest_edge"""]   )
       SCREAMING_SNAKE_CASE__    							=   get_resize_output_image_size(__snake_case ,			size=__snake_case ,			default_to_square=__snake_case   )
       SCREAMING_SNAKE_CASE__    							=   {'''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(
         __snake_case ,			size=(size_dict["""height"""], size_dict["""width"""]) ,			resample=__snake_case ,			data_format=__snake_case ,			**__snake_case   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       np.ndarray ,			_lowercase    :       Dict[str, int] ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = None ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   get_size_dict(__snake_case   )
     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(__snake_case ,			size=(size["""height"""], size["""width"""]) ,			data_format=__snake_case ,			**__snake_case   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       np.ndarray ,			_lowercase    :       Union[int, float] ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = None ,			**_lowercase    :       Any ,			):



     """simple docstring"""






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




   def 			__a		(					self    :       int ,			_lowercase    :       np.ndarray ,			_lowercase    :       Union[float, List[float]] ,			_lowercase    :       Union[float, List[float]] ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = None ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__    							=   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__    							=   do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__    							=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__    							=   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__    							=   do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__    							=   image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__    							=   image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE__    							=   size if size is not None else self.size
     SCREAMING_SNAKE_CASE__    							=   get_size_dict(__snake_case ,			default_to_square=__snake_case   )
     SCREAMING_SNAKE_CASE__    							=   crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__    							=   get_size_dict(__snake_case ,			param_name="""crop_size"""   )

     SCREAMING_SNAKE_CASE__    							=   make_list_of_images(__snake_case   )

     if not valid_images(__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.
     SCREAMING_SNAKE_CASE__    							=   [to_numpy_array(__snake_case   ) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__    							=   [self.resize(__snake_case ,			__snake_case ,			__snake_case   ) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE__    							=   [self.center_crop(__snake_case ,			__snake_case   ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__    							=   [self.rescale(__snake_case ,			__snake_case   ) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE__    							=   [self.normalize(__snake_case ,			__snake_case ,			__snake_case   ) for image in images]

     SCREAMING_SNAKE_CASE__    							=   [to_channel_dimension_format(__snake_case ,			__snake_case   ) for image in images]

     SCREAMING_SNAKE_CASE__    							=   {'''pixel_values''': images}
     return BatchFeature(data=__snake_case ,			tensor_type=__snake_case   )

import enum
import shutil
import sys


__lowerCamelCase     ,		__lowerCamelCase    :				Optional[Any]							     =	shutil.get_terminal_size()

__lowerCamelCase    :				Any							     =	{'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}







class __snake_case       (		enum.Enum	):
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							1





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]=""			)							->      int:







  """simple docstring"""





  sys.stdout.write(str(__UpperCamelCase			) + end			)
  sys.stdout.flush()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Optional[Any]=""			)							->      Dict:







  """simple docstring"""





  forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[Any]:







  """simple docstring"""





  forceWrite("""\r"""			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: str			)							->      str:







  """simple docstring"""





  forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Dict:







  """simple docstring"""





  forceWrite(""" """ * TERMINAL_WIDTH			)
  reset_cursor()






def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  reset_cursor()
  forceWrite("""-""" * TERMINAL_WIDTH			)
