
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							=       False


__lowerCamelCase							=       logging.get_logger(__name__)
__lowerCamelCase							=       """ybelkada/fonts"""


def 			UpperCamelCase					(    ):


       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 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Optional[Any]    ):
       requires_backends(UpperCamelCase_				,					["torch"]    )
       _check_torch_version()

       snake_case       :   Any							=      image_tensor.unsqueeze(0    )
       snake_case       :   Any							=      torch.nn.functional.unfold(UpperCamelCase_				,					(patch_height, patch_width)				,					stride=(patch_height, patch_width)    )
       snake_case       :   List[Any]							=      patches.reshape(image_tensor.size(0    )				,					image_tensor.size(1    )				,					UpperCamelCase_				,					UpperCamelCase_				,					-1    )
       snake_case       :   List[str]							=      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 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int = 36				,					__lowerCamelCase     : str = "black"				,					__lowerCamelCase     : str = "white"				,					__lowerCamelCase     : int = 5				,					__lowerCamelCase     : int = 5				,					__lowerCamelCase     : int = 5				,					__lowerCamelCase     : int = 5				,					__lowerCamelCase     : Optional[bytes] = None				,					__lowerCamelCase     : Optional[str] = None				,					):
       requires_backends(UpperCamelCase_				,					"vision"    )
       # Add new lines so that each line is no more than 80 characters.

       snake_case       :   Tuple							=      textwrap.TextWrapper(width=80    )
       snake_case       :   Optional[int]							=      wrapper.wrap(text=UpperCamelCase_    )
       snake_case       :   List[Any]							=      "\n".join(UpperCamelCase_    )

       if font_bytes is not None and font_path is None:
              snake_case       :   List[str]							=      io.BytesIO(UpperCamelCase_    )
       elif font_path is not None:
              snake_case       :   List[str]							=      font_path
       else:
              snake_case       :   Optional[int]							=      hf_hub_download(UpperCamelCase_				,					"Arial.TTF"    )
       snake_case       :   Optional[int]							=      ImageFont.truetype(UpperCamelCase_				,					encoding="UTF-8"				,					size=UpperCamelCase_    )

       # Use a temporary canvas to determine the width and height in pixels when
       # rendering the text.
       snake_case       :   Dict							=      ImageDraw.Draw(Image.new("RGB"				,					(1, 1)				,					UpperCamelCase_    )    )
       snake_case				,					snake_case				,					snake_case				,					snake_case       :   Optional[int]							=      temp_draw.textbbox((0, 0)				,					UpperCamelCase_				,					UpperCamelCase_    )

       # Create the actual image with a bit of padding around the text.
       snake_case       :   str							=      text_width + left_padding + right_padding
       snake_case       :   Optional[Any]							=      text_height + top_padding + bottom_padding
       snake_case       :   Dict							=      Image.new("RGB"				,					(image_width, image_height)				,					UpperCamelCase_    )
       snake_case       :   Tuple							=      ImageDraw.Draw(UpperCamelCase_    )
       draw.text(xy=(left_padding, top_padding)				,					text=UpperCamelCase_				,					fill=UpperCamelCase_				,					font=UpperCamelCase_    )
       return image


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : str				,					**__lowerCamelCase     : Union[str, Any]    ):
       requires_backends(UpperCamelCase_				,					"vision"    )

       # Convert to PIL image if necessary
       snake_case       :   int							=      to_pil_image(UpperCamelCase_    )

       snake_case       :   Tuple							=      render_text(UpperCamelCase_				,					**UpperCamelCase_    )
       snake_case       :   Any							=      max(header_image.width				,					image.width    )

       snake_case       :   str							=      int(image.height * (new_width / image.width)    )
       snake_case       :   Any							=      int(header_image.height * (new_width / header_image.width)    )

       snake_case       :   str							=      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
       snake_case       :   Tuple							=      to_numpy_array(UpperCamelCase_    )

       if infer_channel_dimension_format(UpperCamelCase_    ) == ChannelDimension.LAST:
              snake_case       :   Optional[Any]							=      to_channel_dimension_format(UpperCamelCase_				,					ChannelDimension.LAST    )

       return new_image






class   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     ["flattened_patches"]



   def __init__(self		:					List[str]    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					int = 20_48    ,						snake_case__		:					bool = False    ,						**snake_case__		:					Dict    ,						)		->	int:


          '''simple docstring'''


          super().__init__(**UpperCAmelCase__ )
          snake_case       :   Dict							=      patch_size if patch_size is not None else {"height": 16, "width": 16}
          snake_case       :   Any							=      do_normalize
          snake_case       :   Optional[int]							=      do_convert_rgb
          snake_case       :   int							=      max_patches
          snake_case       :   Dict							=      is_vqa



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					int    ,						snake_case__		:					dict    ,						**snake_case__		:					Dict )		->	Union[str, Any]:


          '''simple docstring'''


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

          # convert to torch
          snake_case       :   int							=      to_channel_dimension_format(UpperCAmelCase__    ,						ChannelDimension.FIRST )
          snake_case       :   int							=      torch.from_numpy(UpperCAmelCase__ )

          snake_case				,					snake_case       :   Any							=      patch_size["height"], patch_size["width"]
          snake_case				,					snake_case       :   List[Any]							=      get_image_size(UpperCAmelCase__ )

          # maximize scale s.t.
          snake_case       :   Tuple							=      math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
          snake_case       :   Union[str, Any]							=      max(min(math.floor(scale * image_height / patch_height )    ,						UpperCAmelCase__ )    ,						1 )
          snake_case       :   Optional[Any]							=      max(min(math.floor(scale * image_width / patch_width )    ,						UpperCAmelCase__ )    ,						1 )
          snake_case       :   List[Any]							=      max(num_feasible_rows * patch_height    ,						1 )
          snake_case       :   str							=      max(num_feasible_cols * patch_width    ,						1 )

          snake_case       :   Dict							=      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]
          snake_case       :   List[Any]							=      torch_extract_patches(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__ )

          snake_case       :   Optional[Any]							=      patches.shape
          snake_case       :   Optional[Any]							=      patches_shape[1]
          snake_case       :   List[str]							=      patches_shape[2]
          snake_case       :   List[Any]							=      patches_shape[3]

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

          # [rows * columns, 1]
          snake_case       :   int							=      torch.arange(UpperCAmelCase__ ).reshape([rows, 1] ).repeat(1    ,						UpperCAmelCase__ ).reshape([rows * columns, 1] )
          snake_case       :   Optional[int]							=      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]
          snake_case       :   Tuple							=      row_ids.to(torch.floataa )
          snake_case       :   Union[str, Any]							=      col_ids.to(torch.floataa )

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

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

          snake_case       :   List[Any]							=      to_numpy_array(UpperCAmelCase__ )

          return result



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Any )		->	List[Any]:


          '''simple docstring'''


          if image.dtype == np.uinta:
                 snake_case       :   Union[str, Any]							=      image.astype(np.floataa )

          # take mean across the whole `image`
          snake_case       :   str							=      np.mean(UpperCAmelCase__ )
          snake_case       :   str							=      np.std(UpperCAmelCase__ )
          snake_case       :   Union[str, Any]							=      max(UpperCAmelCase__    ,						1.0 / math.sqrt(np.prod(image.shape ) ) )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					ImageInput    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[Dict[str, int]] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					ChannelDimension = ChannelDimension.FIRST    ,						**snake_case__		:					Any    ,						)		->	Dict:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   List[str]							=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
          snake_case       :   List[Any]							=      patch_size if patch_size is not None else self.patch_size
          snake_case       :   List[str]							=      max_patches if max_patches is not None else self.max_patches
          snake_case       :   List[Any]							=      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 " )

          snake_case       :   Any							=      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:
                 snake_case       :   List[str]							=      [convert_to_rgb(UpperCAmelCase__ ) for image in images]

          # All transformations expect numpy arrays.
          snake_case       :   List[Any]							=      [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." )
                 snake_case       :   Tuple							=      kwargs.pop("font_bytes"    ,						UpperCAmelCase__ )
                 snake_case       :   Tuple							=      kwargs.pop("font_path"    ,						UpperCAmelCase__ )

                 if isinstance(UpperCAmelCase__    ,						UpperCAmelCase__ ):
                        snake_case       :   Tuple							=      [header_text] * len(UpperCAmelCase__ )

                 snake_case       :   Optional[Any]							=      [
                     render_header(UpperCAmelCase__    ,						header_text[i]    ,						font_bytes=UpperCAmelCase__    ,						font_path=UpperCAmelCase__ )
                     for i, image in enumerate(UpperCAmelCase__ )
                 ]

          if do_normalize:
                 snake_case       :   List[str]							=      [self.normalize(image=UpperCAmelCase__ ) for image in images]

          # convert to torch tensor and permute
          snake_case       :   Union[str, Any]							=      [
              self.extract_flattened_patches(image=UpperCAmelCase__    ,						max_patches=UpperCAmelCase__    ,						patch_size=UpperCAmelCase__ )
              for image in images
          ]

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

          snake_case       :   List[Any]							=      BatchFeature(
              data={"flattened_patches": images, "attention_mask": attention_masks}    ,						tensor_type=UpperCAmelCase__ )

          return encoded_outputs








"""simple docstring"""





def   _A  (  UpperCamelCase_     :       Any)	->						List[str]:
   '''simple docstring'''


   __lowercase		,__lowercase								=						[], []
   while len(UpperCamelCase_) > 1:
      __lowercase		,__lowercase								=						min(UpperCamelCase_), max(UpperCamelCase_)
      start.append(UpperCamelCase_)
      end.append(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
   end.reverse()
   return start + collection + end


if __name__ == "__main__":
   _a       =							input('Enter numbers separated by a comma:\n').strip()
   _a       =							[int(item) for item in user_input.split(',')]
   print(*merge_sort(unsorted), sep=',')




