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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


UpperCamelCase__											=					logging.get_logger(__name__)




def    lowerCAmelCase_ (     __A							)     ->				List[List[ImageInput]]:







      '''simple docstring'''


      if isinstance(__A,  (list, tuple)							) and isinstance(videos[0],  (list, tuple)							) and is_valid_image(videos[0][0]							):
            return videos

      elif isinstance(__A,  (list, tuple)							) and is_valid_image(videos[0]							):
            return [videos]

      elif is_valid_image(__A							):
            return [[videos]]

      raise ValueError(f"""Could not make batched video from {videos}"""							)







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[Any]      =			['pixel_values']


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Dict[str, int] = None       ,  __UpperCAmelCase     :						PILImageResampling = PILImageResampling.BILINEAR       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Dict[str, int] = None       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Union[int, float] = 1 / 2_5_5       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				None:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)
         UpperCAmelCase__								=  size if size is not None else {"shortest_edge": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase       ,  default_to_square=__UpperCAmelCase		)
         UpperCAmelCase__								=  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase       ,  param_name="crop_size"		)

         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_center_crop
         UpperCAmelCase__								=  crop_size
         UpperCAmelCase__								=  resample
         UpperCAmelCase__								=  do_rescale
         UpperCAmelCase__								=  rescale_factor
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__								=  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Dict[str, int]       ,  __UpperCAmelCase     :						PILImageResampling = PILImageResampling.BILINEAR       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						int       ,  )						->				np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase       ,  default_to_square=__UpperCAmelCase		)
         if "shortest_edge" in size:
               UpperCAmelCase__								=  get_resize_output_image_size(__UpperCAmelCase       ,  size["shortest_edge"]       ,  default_to_square=__UpperCAmelCase		)
         elif "height" in size and "width" in size:
               UpperCAmelCase__								=  (size["height"], size["width"])
         else:
               raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
         return resize(__UpperCAmelCase       ,  size=__UpperCAmelCase       ,  resample=__UpperCAmelCase       ,  data_format=__UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Dict[str, int]       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						Any       ,  )						->				np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase		)
         if "height" not in size or "width" not in size:
               raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
         return center_crop(__UpperCAmelCase       ,  size=(size["height"], size["width"])       ,  data_format=__UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Union[int, float]       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				str:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						np.ndarray       ,  __UpperCAmelCase     :						Union[float, List[float]]       ,  __UpperCAmelCase     :						Union[float, List[float]]       ,  __UpperCAmelCase     :						Optional[Union[str, ChannelDimension]] = None       ,  **__UpperCAmelCase     :						Dict       ,  )						->				np.ndarray:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						ImageInput       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						Dict[str, int] = None       ,  __UpperCAmelCase     :						PILImageResampling = None       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						Dict[str, int] = None       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						float = None       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[ChannelDimension] = ChannelDimension.FIRST       ,  )						->				np.ndarray:
         """simple docstring"""



         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.
         UpperCAmelCase__								=  to_numpy_array(__UpperCAmelCase		)

         if do_resize:
               UpperCAmelCase__								=  self.resize(image=__UpperCAmelCase       ,  size=__UpperCAmelCase       ,  resample=__UpperCAmelCase		)

         if do_center_crop:
               UpperCAmelCase__								=  self.center_crop(__UpperCAmelCase       ,  size=__UpperCAmelCase		)

         if do_rescale:
               UpperCAmelCase__								=  self.rescale(image=__UpperCAmelCase       ,  scale=__UpperCAmelCase		)

         if do_normalize:
               UpperCAmelCase__								=  self.normalize(image=__UpperCAmelCase       ,  mean=__UpperCAmelCase       ,  std=__UpperCAmelCase		)

         UpperCAmelCase__								=  to_channel_dimension_format(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return image





   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						ImageInput       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						Dict[str, int] = None       ,  __UpperCAmelCase     :						PILImageResampling = None       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						Dict[str, int] = None       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						float = None       ,  __UpperCAmelCase     :						bool = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[float, List[float]]] = None       ,  __UpperCAmelCase     :						Optional[Union[str, TensorType]] = None       ,  __UpperCAmelCase     :						ChannelDimension = ChannelDimension.FIRST       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				PIL.Image.Image:
         """simple docstring"""



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

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

         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."		)

         UpperCAmelCase__								=  make_batched(__UpperCAmelCase		)

         UpperCAmelCase__								=  [
             [
                 self._preprocess_image(
                     image=__UpperCAmelCase       ,  do_resize=__UpperCAmelCase       ,  size=__UpperCAmelCase       ,  resample=__UpperCAmelCase       ,  do_center_crop=__UpperCAmelCase       ,  crop_size=__UpperCAmelCase       ,  do_rescale=__UpperCAmelCase       ,  rescale_factor=__UpperCAmelCase       ,  do_normalize=__UpperCAmelCase       ,  image_mean=__UpperCAmelCase       ,  image_std=__UpperCAmelCase       ,  data_format=__UpperCAmelCase       ,  )
                 for img in video
             ]
             for video in videos
         ]

         UpperCAmelCase__								=  {"pixel_values": videos}
         return BatchFeature(data=__UpperCAmelCase       ,  tensor_type=__UpperCAmelCase		)




'''simple docstring'''




import os
import string
import sys


__lowercase          =  1 << 8

__lowercase          =  {
    '''tab''': ord('''\t'''),
    '''newline''': ord('''\r'''),
    '''esc''': 2_7,
    '''up''': 6_5 + ARROW_KEY_FLAG,
    '''down''': 6_6 + ARROW_KEY_FLAG,
    '''right''': 6_7 + ARROW_KEY_FLAG,
    '''left''': 6_8 + ARROW_KEY_FLAG,
    '''mod_int''': 9_1,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 5_0,
    '''delete''': 5_1,
    '''pg_up''': 5_3,
    '''pg_down''': 5_4,
}

__lowercase          =  KEYMAP['''up''']
__lowercase          =  KEYMAP['''left''']

if sys.platform == "win32":
						__lowercase          =  []
						__lowercase          =  {
						    B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
						    B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
						    B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
						    B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
						    B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
						    B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
						    B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
						    B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
						}

for i in range(1_0):
						__lowercase          =  ord(str(i))






def 				snake_case__							(			)    ->	List[Any]:

		'''simple docstring'''


		if os.name == "nt":
				import msvcrt

				lowerCAmelCase   					=  """mbcs"""
				# Flush the keyboard buffer
				while msvcrt.kbhit():
						msvcrt.getch()
				if len(_A ) == 0:
						# Read the keystroke
						lowerCAmelCase   					=  msvcrt.getch()

						# If it is a prefix char, get second part
						if ch in (b"\x00", b"\xe0"):
								lowerCAmelCase   					=  ch + msvcrt.getch()
								# Translate actual Win chars to bullet char types
								try:
										lowerCAmelCase   					=  chr(WIN_KEYMAP[cha] )
										WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
										WIN_CH_BUFFER.append(_A )
										if ord(_A ) in (
										    KEYMAP["insert"] - 1 << 9,
										    KEYMAP["delete"] - 1 << 9,
										    KEYMAP["pg_up"] - 1 << 9,
										    KEYMAP["pg_down"] - 1 << 9,
										):
												WIN_CH_BUFFER.append(chr(126 ) )
										lowerCAmelCase   					=  chr(KEYMAP["""esc"""] )
								except KeyError:
										lowerCAmelCase   					=  cha[1]
						else:
								lowerCAmelCase   					=  ch.decode(_A )
				else:
						lowerCAmelCase   					=  WIN_CH_BUFFER.pop(0 )
		elif os.name == "posix":
				import termios
				import tty

				lowerCAmelCase   					=  sys.stdin.fileno()
				lowerCAmelCase   					=  termios.tcgetattr(_A )
				try:
						tty.setraw(_A )
						lowerCAmelCase   					=  sys.stdin.read(1 )
				finally:
						termios.tcsetattr(_A      ,   termios.TCSADRAIN      ,   _A )
		return ch






def 				snake_case__							(			)    ->	Tuple:

		'''simple docstring'''


		lowerCAmelCase   					=  get_raw_chars()
		if ord(_A ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
				return char

		elif ord(_A ) == KEYMAP["esc"]:
				lowerCAmelCase   					=  get_raw_chars()
				if ord(_A ) == KEYMAP["mod_int"]:
						lowerCAmelCase   					=  get_raw_chars()
						if ord(_A ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_A ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
								return chr(ord(_A ) + ARROW_KEY_FLAG )
						else:
								return KEYMAP["undefined"]
				else:
						return get_raw_chars()

		else:
				if char in string.printable:
						return char
				else:
						return KEYMAP["undefined"]




