





'''simple docstring'''







from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
 import PIL

# soft dependency
if is_pytesseract_available():
 import pytesseract

SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[Any]:
     """simple docstring"""




     return [
         int(1000 * (box[0] / width)		),
         int(1000 * (box[1] / height)		),
         int(1000 * (box[2] / width)		),
         int(1000 * (box[3] / height)		),
     ]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE = None		)				->					Any:
     """simple docstring"""




     __a       =   tesseract_config if tesseract_config is not None else """"""

     # apply OCR
     __a       =   to_pil_image(__SCREAMING_SNAKE_CASE		)
     __a ,						__a       =   pil_image.size
     __a       =   pytesseract.image_to_data(__SCREAMING_SNAKE_CASE				,  lang=__SCREAMING_SNAKE_CASE				,  output_type="""dict"""				,  config=__SCREAMING_SNAKE_CASE		)
     __a ,						__a ,						__a ,						__a ,						__a       =   data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

     # filter empty words and corresponding coordinates
     __a       =   [idx for idx, word in enumerate(__SCREAMING_SNAKE_CASE		) if not word.strip()]
     __a       =   [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE		) if idx not in irrelevant_indices]
     __a       =   [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE		) if idx not in irrelevant_indices]
     __a       =   [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE		) if idx not in irrelevant_indices]
     __a       =   [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE		) if idx not in irrelevant_indices]
     __a       =   [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE		) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     __a       =   []
     for x, y, w, h in zip(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
          __a       =   [x, y, x + w, y + h]
          actual_boxes.append(__SCREAMING_SNAKE_CASE		)

     # finally, normalize the bounding boxes
     __a       =   []
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)		)

     assert len(__SCREAMING_SNAKE_CASE		) == len(__SCREAMING_SNAKE_CASE		), "Not as many words as there are bounding boxes"

     return words, normalized_boxes



class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[str]												=["""pixel_values"""]

  def __init__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE__     :							PILImageResampling = PILImageResampling.BILINEAR				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = ""				,							**SCREAMING_SNAKE_CASE__     :							int				,							):





       '''simple docstring'''

       super().__init__(**SCREAMING_SNAKE_CASE__						)
       __a       =   size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__						)

       __a       =   do_resize
       __a       =   size
       __a       =   resample
       __a       =   apply_ocr
       __a       =   ocr_lang
       __a       =   tesseract_config

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





       '''simple docstring'''

       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__						)
       if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''						)
       __a       =   (size["""height"""], size["""width"""])
       return resize(SCREAMING_SNAKE_CASE__				,							size=SCREAMING_SNAKE_CASE__				,							resample=SCREAMING_SNAKE_CASE__				,							data_format=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)


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





       '''simple docstring'''

       __a       =   do_resize if do_resize is not None else self.do_resize
       __a       =   size if size is not None else self.size
       __a       =   get_size_dict(SCREAMING_SNAKE_CASE__						)
       __a       =   resample if resample is not None else self.resample
       __a       =   apply_ocr if apply_ocr is not None else self.apply_ocr
       __a       =   ocr_lang if ocr_lang is not None else self.ocr_lang
       __a       =   tesseract_config if tesseract_config is not None else self.tesseract_config

       __a       =   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."""						)

       # All transformations expect numpy arrays.
       __a       =   [to_numpy_array(SCREAMING_SNAKE_CASE__						) for image in images]

       if apply_ocr:
            requires_backends(self				,							"""pytesseract"""						)
            __a       =   []
            __a       =   []
            for image in images:
                 __a ,						__a       =   apply_tesseract(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
                 words_batch.append(SCREAMING_SNAKE_CASE__						)
                 boxes_batch.append(SCREAMING_SNAKE_CASE__						)

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

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       __a       =   [flip_channel_order(SCREAMING_SNAKE_CASE__						) for image in images]
       __a       =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						) for image in images]

       __a       =   BatchFeature(data={"""pixel_values""": images}				,							tensor_type=SCREAMING_SNAKE_CASE__						)

       if apply_ocr:
            __a       =   words_batch
            __a       =   boxes_batch
       return data










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					bool:
     """simple docstring"""




     __a       =   [int(__SCREAMING_SNAKE_CASE		) for i in ip_va_address.split("""."""		) if i.isdigit()]
     return len(__SCREAMING_SNAKE_CASE		) == 4 and all(0 <= int(__SCREAMING_SNAKE_CASE		) <= 254 for octet in octets		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			input().strip()
 SCREAMING_SNAKE_CASE_      =			'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")






