

import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
  import ctypes
  import msvcrt  # noqa





  class A__    ( ctypes.Structure ):



   """simple docstring"""







   _lowercase     :					Dict      =						[('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]







def 	_snake_case		(    )       ->      Optional[int]:

     """simple docstring"""







     if os.name == "nt":
          _lowerCAmelCase   :					Optional[int]        =    CursorInfo()
          _lowerCAmelCase   :					int        =    ctypes.windll.kernelaa.GetStdHandle(-11				)
          ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE		,							ctypes.byref(SCREAMING_SNAKE_CASE				)				)
          _lowerCAmelCase   :					Dict        =    False
          ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE		,							ctypes.byref(SCREAMING_SNAKE_CASE				)				)
     elif os.name == "posix":
          sys.stdout.write("\033[?25l"				)
          sys.stdout.flush()







def 	_snake_case		(    )       ->      List[str]:

     """simple docstring"""







     if os.name == "nt":
          _lowerCAmelCase   :					Tuple        =    CursorInfo()
          _lowerCAmelCase   :					Dict        =    ctypes.windll.kernelaa.GetStdHandle(-11				)
          ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE		,							ctypes.byref(SCREAMING_SNAKE_CASE				)				)
          _lowerCAmelCase   :					Optional[int]        =    True
          ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE		,							ctypes.byref(SCREAMING_SNAKE_CASE				)				)
     elif os.name == "posix":
          sys.stdout.write("\033[?25h"				)
          sys.stdout.flush()







@contextmanager
def 	_snake_case		(    )       ->      List[str]:

     """simple docstring"""







     try:
          hide_cursor()
          yield
     finally:
          show_cursor()









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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	List[str]     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					str        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Union[str, Any]        =    do_center_crop
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return resize(
          A_     ,						size=(size["height"], size["width"])     ,						resample=A_     ,						data_format=A_     ,						**A_       )

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






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

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






      '''simple docstring'''




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

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






      '''simple docstring'''




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


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






      '''simple docstring'''




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

      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					int        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Tuple        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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 or resample is None:
           raise ValueError("Size and resample 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.
      _lowerCAmelCase   :					Union[str, Any]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					List[str]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Any        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					str        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					int        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )






