



'''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:
  lowercase							       =			False


lowercase							       =			logging.get_logger(__name__)
lowercase							       =			'''ybelkada/fonts'''






def  UpperCAmelCase_       (		):


     '''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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     requires_backends(lowercase__       ,	["torch"]       )
     _check_torch_version()

     a_       		=image_tensor.unsqueeze(0       )
     a_       		=torch.nn.functional.unfold(lowercase__       ,	(patch_height, patch_width)       ,	stride=(patch_height, patch_width)       )
     a_       		=patches.reshape(image_tensor.size(0       )       ,	image_tensor.size(1       )       ,	lowercase__       ,	lowercase__       ,	-1       )
     a_       		=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_       (		lowercase__       ,	lowercase__ = 3_6       ,	lowercase__ = "black"       ,	lowercase__ = "white"       ,	lowercase__ = 5       ,	lowercase__ = 5       ,	lowercase__ = 5       ,	lowercase__ = 5       ,	lowercase__ = None       ,	lowercase__ = None       ,	):


     '''simple docstring'''







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

     a_       		=textwrap.TextWrapper(width=8_0       )
     a_       		=wrapper.wrap(text=lowercase__       )
     a_       		="\n".join(lowercase__       )

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

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

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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	**lowercase__       ):


     '''simple docstring'''







     requires_backends(lowercase__       ,	"vision"       )

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

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

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

     a_       		=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
     a_       		=to_numpy_array(lowercase__       )

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

     return new_image







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: int  =      ["flattened_patches"]




 def __init__(   self							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = 2_0_4_8							,  lowerCAmelCase_ = False							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      a_       		=patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
      a_       		=do_normalize
      a_       		=do_convert_rgb
      a_       		=max_patches
      a_       		=is_vqa




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      np.ndarray:



      """simple docstring"""







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

      # convert to torch
      a_       		=to_channel_dimension_format(lowerCAmelCase_							,  ChannelDimension.FIRST)
      a_       		=torch.from_numpy(lowerCAmelCase_)

      a_		,						a_       		=patch_size["height"], patch_size["width"]
      a_		,						a_       		=get_image_size(lowerCAmelCase_)

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

      a_       		=torch.nn.functional.interpolate(
          image.unsqueeze(0)							,  size=(resized_height, resized_width)							,  mode="bilinear"							,  align_corners=lowerCAmelCase_							,  antialias=lowerCAmelCase_							,  ).squeeze(0)

      # [1, rows, columns, patch_height * patch_width * image_channels]
      a_       		=torch_extract_patches(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_)

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

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

      # [rows * columns, 1]
      a_       		=torch.arange(lowerCAmelCase_).reshape([rows, 1]).repeat(1							,  lowerCAmelCase_).reshape([rows * columns, 1])
      a_       		=torch.arange(lowerCAmelCase_).reshape([1, columns]).repeat(lowerCAmelCase_							,  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]
      a_       		=row_ids.to(torch.floataa)
      a_       		=col_ids.to(torch.floataa)

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

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

      a_       		=to_numpy_array(lowerCAmelCase_)

      return result




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_) ->      np.ndarray:



      """simple docstring"""







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

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

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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = ChannelDimension.FIRST							,  **lowerCAmelCase_							,  ) ->      ImageInput:



      """simple docstring"""







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

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

      a_       		=make_list_of_images(lowerCAmelCase_)

      if not valid_images(lowerCAmelCase_):
           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:
           a_       		=[convert_to_rgb(lowerCAmelCase_) for image in images]

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

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

           if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
                a_       		=[header_text] * len(lowerCAmelCase_)

           a_       		=[
               render_header(lowerCAmelCase_							,  header_text[i]							,  font_bytes=lowerCAmelCase_							,  font_path=lowerCAmelCase_)
               for i, image in enumerate(lowerCAmelCase_)
           ]

      if do_normalize:
           a_       		=[self.normalize(image=lowerCAmelCase_) for image in images]

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

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

      a_       		=BatchFeature(
          data={"flattened_patches": images, "attention_mask": attention_masks}							,  tensor_type=lowerCAmelCase_)

      return encoded_outputs







'''simple docstring'''


import os

# Precomputes a list of the 100 first triangular numbers
lowercase							       =			[int(0.5 * n * (n + 1)) for n in range(1, 101)]






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=os.path.dirname(os.path.realpath(lowercase__       )       )
     a_       		=os.path.join(lowercase__       ,	"words.txt"       )

     a_       		=""
     with open(lowercase__       ) as f:
          a_       		=f.readline()

     a_       		=[word.strip("\""       ) for word in words.strip("\r\n"       ).split(","       )]
     a_       		=[
         word
         for word in [sum(ord(lowercase__       ) - 6_4 for x in word       ) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(lowercase__       )


if __name__ == "__main__":
  print(solution())


