


'''simple docstring'''

import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
 import textwrap

 from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
 import torch

 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 lowerCamelCase				:  Union[str, Any]    		=      False


lowerCamelCase				:  Any    		=      logging.get_logger(__name__)
lowerCamelCase				:  List[str]    		=      'ybelkada/fonts'

def _SCREAMING_SNAKE_CASE ()			->			Dict:






       """simple docstring"""







       if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
              raise ImportError(
                  f"You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use "
                  '''Pix2StructImageProcessor. Please upgrade torch.'''			)

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			int:






       """simple docstring"""







       requires_backends(A    ,					['''torch''']			)
       _check_torch_version()

       lowercase__      =					image_tensor.unsqueeze(0			)
       lowercase__      =					torch.nn.functional.unfold(A    ,					(patch_height, patch_width)    ,					stride=(patch_height, patch_width)			)
       lowercase__      =					patches.reshape(image_tensor.size(0			)    ,					image_tensor.size(1			)    ,					A    ,					A    ,					-1			)
       lowercase__      =					patches.permute(0    ,					4    ,					2    ,					3    ,					1			).reshape(
           image_tensor.size(2			) // patch_height    ,					image_tensor.size(3			) // patch_width    ,					image_tensor.size(1			) * patch_height * patch_width    ,					)
       return patches.unsqueeze(0			)

def _SCREAMING_SNAKE_CASE (A    ,					A = 36    ,					A = "black"    ,					A = "white"    ,					A = 5    ,					A = 5    ,					A = 5    ,					A = 5    ,					A = None    ,					A = None    ,					)			->			Image.Image:






       """simple docstring"""







       requires_backends(A    ,					'''vision'''			)
       # Add new lines so that each line is no more than 80 characters.

       lowercase__      =					textwrap.TextWrapper(width=80			)
       lowercase__      =					wrapper.wrap(text=A			)
       lowercase__      =					'''\n'''.join(A			)

       if font_bytes is not None and font_path is None:
              lowercase__      =					io.BytesIO(A			)
       elif font_path is not None:
              lowercase__      =					font_path
       else:
              lowercase__      =					hf_hub_download(A    ,					'''Arial.TTF'''			)
       lowercase__      =					ImageFont.truetype(A    ,					encoding='''UTF-8'''    ,					size=A			)

       # Use a temporary canvas to determine the width and height in pixels when
       # rendering the text.
       lowercase__      =					ImageDraw.Draw(Image.new('''RGB'''    ,					(1, 1)    ,					A			)			)
       lowercase__     ,lowercase__     ,lowercase__     ,lowercase__      =					temp_draw.textbbox((0, 0)    ,					A    ,					A			)

       # Create the actual image with a bit of padding around the text.
       lowercase__      =					text_width + left_padding + right_padding
       lowercase__      =					text_height + top_padding + bottom_padding
       lowercase__      =					Image.new('''RGB'''    ,					(image_width, image_height)    ,					A			)
       lowercase__      =					ImageDraw.Draw(A			)
       draw.text(xy=(left_padding, top_padding)    ,					text=A    ,					fill=A    ,					font=A			)
       return image

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					**A			)			->			str:






       """simple docstring"""







       requires_backends(A    ,					'''vision'''			)

       # Convert to PIL image if necessary
       lowercase__      =					to_pil_image(A			)

       lowercase__      =					render_text(A    ,					**A			)
       lowercase__      =					max(header_image.width    ,					image.width			)

       lowercase__      =					int(image.height * (new_width / image.width)			)
       lowercase__      =					int(header_image.height * (new_width / header_image.width)			)

       lowercase__      =					Image.new('''RGB'''    ,					(new_width, new_height + new_header_height)    ,					'''white'''			)
       new_image.paste(header_image.resize((new_width, new_header_height)			)    ,					(0, 0)			)
       new_image.paste(image.resize((new_width, new_height)			)    ,					(0, new_header_height)			)

       # Convert back to the original framework if necessary
       lowercase__      =					to_numpy_array(A			)

       if infer_channel_dimension_format(A			) == ChannelDimension.LAST:
              lowercase__      =					to_channel_dimension_format(A    ,					ChannelDimension.LAST			)

       return new_image

class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	List[Any]       =				["""flattened_patches"""]



      def __init__(self       :       Dict  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       int = 2048  ,		UpperCamelCase       :       bool = False  ,		**UpperCamelCase       :       Optional[int]  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)
             lowercase__      =					patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
             lowercase__      =					do_normalize
             lowercase__      =					do_convert_rgb
             lowercase__      =					max_patches
             lowercase__      =					is_vqa



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       dict  ,		**UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             requires_backends(self.extract_flattened_patches  ,		'''torch'''					)
             _check_torch_version()

             # convert to torch
             lowercase__      =					to_channel_dimension_format(UpperCamelCase  ,		ChannelDimension.FIRST					)
             lowercase__      =					torch.from_numpy(UpperCamelCase					)

             lowercase__     ,lowercase__      =					patch_size['''height'''], patch_size['''width''']
             lowercase__     ,lowercase__      =					get_image_size(UpperCamelCase					)

             # maximize scale s.t.
             lowercase__      =					math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)					)
             lowercase__      =					max(min(math.floor(scale * image_height / patch_height					)  ,		UpperCamelCase					)  ,		1					)
             lowercase__      =					max(min(math.floor(scale * image_width / patch_width					)  ,		UpperCamelCase					)  ,		1					)
             lowercase__      =					max(num_feasible_rows * patch_height  ,		1					)
             lowercase__      =					max(num_feasible_cols * patch_width  ,		1					)

             lowercase__      =					torch.nn.functional.interpolate(
                 image.unsqueeze(0					)  ,		size=(resized_height, resized_width)  ,		mode='''bilinear'''  ,		align_corners=UpperCamelCase  ,		antialias=UpperCamelCase  ,		).squeeze(0					)

             # [1, rows, columns, patch_height * patch_width * image_channels]
             lowercase__      =					torch_extract_patches(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)

             lowercase__      =					patches.shape
             lowercase__      =					patches_shape[1]
             lowercase__      =					patches_shape[2]
             lowercase__      =					patches_shape[3]

             # [rows * columns, patch_height * patch_width * image_channels]
             lowercase__      =					patches.reshape([rows * columns, depth]					)

             # [rows * columns, 1]
             lowercase__      =					torch.arange(UpperCamelCase					).reshape([rows, 1]					).repeat(1  ,		UpperCamelCase					).reshape([rows * columns, 1]					)
             lowercase__      =					torch.arange(UpperCamelCase					).reshape([1, columns]					).repeat(UpperCamelCase  ,		1					).reshape([rows * columns, 1]					)

             # Offset by 1 so the ids do not contain zeros, which represent padding.
             row_ids += 1
             col_ids += 1

             # Prepare additional patch features.
             # [rows * columns, 1]
             lowercase__      =					row_ids.to(torch.floataa					)
             lowercase__      =					col_ids.to(torch.floataa					)

             # [rows * columns, 2 + patch_height * patch_width * image_channels]
             lowercase__      =					torch.cat([row_ids, col_ids, patches]  ,		-1					)

             # [max_patches, 2 + patch_height * patch_width * image_channels]
             lowercase__      =					torch.nn.functional.pad(UpperCamelCase  ,		[0, 0, 0, max_patches - (rows * columns)]					).float()

             lowercase__      =					to_numpy_array(UpperCamelCase					)

             return result



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

             '''simple docstring'''







             if image.dtype == np.uinta:
                    lowercase__      =					image.astype(np.floataa					)

             # take mean across the whole `image`
             lowercase__      =					np.mean(UpperCamelCase					)
             lowercase__      =					np.std(UpperCamelCase					)
             lowercase__      =					max(UpperCamelCase  ,		1.0 / math.sqrt(np.prod(image.shape					)					)					)

             return normalize(UpperCamelCase  ,		mean=UpperCamelCase  ,		std=UpperCamelCase  ,		**UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       Optional[str] = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Dict[str, int]] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       ChannelDimension = ChannelDimension.FIRST  ,		**UpperCamelCase       :       List[str]  ,		):

             '''simple docstring'''







             lowercase__      =					do_normalize if do_normalize is not None else self.do_normalize
             lowercase__      =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
             lowercase__      =					patch_size if patch_size is not None else self.patch_size
             lowercase__      =					max_patches if max_patches is not None else self.max_patches
             lowercase__      =					self.is_vqa

             if kwargs.get('''data_format'''  ,		UpperCamelCase					) is not None:
                    raise ValueError('''data_format is not an accepted input as the outputs are '''					)

             lowercase__      =					make_list_of_images(UpperCamelCase					)

             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.'''					)

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    lowercase__      =					[convert_to_rgb(UpperCamelCase					) for image in images]

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

             if is_vqa:
                    if header_text is None:
                           raise ValueError('''A header text must be provided for VQA models.'''					)
                    lowercase__      =					kwargs.pop('''font_bytes'''  ,		UpperCamelCase					)
                    lowercase__      =					kwargs.pop('''font_path'''  ,		UpperCamelCase					)

                    if isinstance(UpperCamelCase  ,		UpperCamelCase					):
                           lowercase__      =					[header_text] * len(UpperCamelCase					)

                    lowercase__      =					[
                        render_header(UpperCamelCase  ,		header_text[i]  ,		font_bytes=UpperCamelCase  ,		font_path=UpperCamelCase					)
                        for i, image in enumerate(UpperCamelCase					)
                    ]

             if do_normalize:
                    lowercase__      =					[self.normalize(image=UpperCamelCase					) for image in images]

             # convert to torch tensor and permute
             lowercase__      =					[
                 self.extract_flattened_patches(image=UpperCamelCase  ,		max_patches=UpperCamelCase  ,		patch_size=UpperCamelCase					)
                 for image in images
             ]

             # create attention mask in numpy
             lowercase__      =					[(image.sum(axis=-1					) != 0).astype(np.floataa					) for image in images]

             lowercase__      =					BatchFeature(
                 data={'''flattened_patches''': images, '''attention_mask''': attention_masks}  ,		tensor_type=UpperCamelCase					)

             return encoded_outputs








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







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


if __name__ == "__main__":
 lowerCamelCase				:  Optional[Any]    		=      input().strip()
 lowerCamelCase				:  Union[str, Any]    		=      'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




