"""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    :							Dict													=						False


__lowerCAmelCase    :							Union[str, Any]													=						logging.get_logger(__name__)
__lowerCAmelCase    :							Tuple													=						"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		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

		lowerCAmelCase__        =   image_tensor.unsqueeze(0     )
		lowerCAmelCase__        =   torch.nn.functional.unfold(lowerCamelCase__			,  (patch_height, patch_width)			,  stride=(patch_height, patch_width)     )
		lowerCAmelCase__        =   patches.reshape(image_tensor.size(0     )			,  image_tensor.size(1     )			,  lowerCamelCase__			,  lowerCamelCase__			,  -1     )
		lowerCAmelCase__        =   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__			,  lowerCamelCase__ = 36			,  lowerCamelCase__ = "black"			,  lowerCamelCase__ = "white"			,  lowerCamelCase__ = 5			,  lowerCamelCase__ = 5			,  lowerCamelCase__ = 5			,  lowerCamelCase__ = 5			,  lowerCamelCase__ = None			,  lowerCamelCase__ = None			,  ):





		"""simple docstring"""




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

		lowerCAmelCase__        =   textwrap.TextWrapper(width=80     )
		lowerCAmelCase__        =   wrapper.wrap(text=lowerCamelCase__     )
		lowerCAmelCase__        =   """\n""".join(lowerCamelCase__     )

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

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

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  **lowerCamelCase__     ):





		"""simple docstring"""




		requires_backends(lowerCamelCase__			,  """vision"""     )

		# Convert to PIL image if necessary
		lowerCAmelCase__        =   to_pil_image(lowerCamelCase__     )

		lowerCAmelCase__        =   render_text(lowerCamelCase__			,  **lowerCamelCase__     )
		lowerCAmelCase__        =   max(header_image.width			,  image.width     )

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

		lowerCAmelCase__        =   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
		lowerCAmelCase__        =   to_numpy_array(lowerCamelCase__     )

		if infer_channel_dimension_format(lowerCamelCase__     ) == ChannelDimension.LAST:
				lowerCAmelCase__        =   to_channel_dimension_format(lowerCamelCase__			,  ChannelDimension.LAST     )

		return new_image






class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Dict			=    ["flattened_patches"]




					def __init__(	self						:     Tuple  ,						snake_case__						:     bool = True  ,						snake_case__						:     bool = True  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     int = 2048  ,						snake_case__						:     bool = False  ,						**snake_case__						:     Optional[Any]  ,						):
							super().__init__(**snake_case__     )
							lowerCAmelCase__        =   patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							lowerCAmelCase__        =   do_normalize
							lowerCAmelCase__        =   do_convert_rgb
							lowerCAmelCase__        =   max_patches
							lowerCAmelCase__        =   is_vqa




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     int  ,						snake_case__						:     dict  ,						**snake_case__						:     Any     ):
							requires_backends(self.extract_flattened_patches  ,						"""torch"""     )
							_check_torch_version()

							# convert to torch
							lowerCAmelCase__        =   to_channel_dimension_format(snake_case__  ,						ChannelDimension.FIRST     )
							lowerCAmelCase__        =   torch.from_numpy(snake_case__     )

							lowerCAmelCase__   ,					lowerCAmelCase__        =   patch_size["""height"""], patch_size["""width"""]
							lowerCAmelCase__   ,					lowerCAmelCase__        =   get_image_size(snake_case__     )

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

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

							# [1, rows, columns, patch_height * patch_width * image_channels]
							lowerCAmelCase__        =   torch_extract_patches(snake_case__  ,						snake_case__  ,						snake_case__     )

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

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

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

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

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

							lowerCAmelCase__        =   to_numpy_array(snake_case__     )

							return result




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Optional[int]     ):
							if image.dtype == np.uinta:
									lowerCAmelCase__        =   image.astype(np.floataa     )

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

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




					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__						:     Optional[int]  ,						):
							lowerCAmelCase__        =   do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase__        =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							lowerCAmelCase__        =   patch_size if patch_size is not None else self.patch_size
							lowerCAmelCase__        =   max_patches if max_patches is not None else self.max_patches
							lowerCAmelCase__        =   self.is_vqa

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

							lowerCAmelCase__        =   make_list_of_images(snake_case__     )

							if not valid_images(snake_case__     ):
									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:
									lowerCAmelCase__        =   [convert_to_rgb(snake_case__     ) for image in images]

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

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

									if isinstance(snake_case__  ,						snake_case__     ):
											lowerCAmelCase__        =   [header_text] * len(snake_case__     )

									lowerCAmelCase__        =   [
									    render_header(snake_case__  ,						header_text[i]  ,						font_bytes=snake_case__  ,						font_path=snake_case__     )
									    for i, image in enumerate(snake_case__     )
									]

							if do_normalize:
									lowerCAmelCase__        =   [self.normalize(image=snake_case__     ) for image in images]

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

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

							lowerCAmelCase__        =   BatchFeature(
							    data={"""flattened_patches""": images, """attention_mask""": attention_masks}  ,						tensor_type=snake_case__     )

							return encoded_outputs





"""simple docstring"""




import os







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   os.path.dirname(os.path.realpath(lowerCamelCase__     )     )
		lowerCAmelCase__        =   os.path.join(lowerCamelCase__			,  """triangle.txt"""     )

		with open(lowerCamelCase__     ) as f:
				lowerCAmelCase__        =   f.readlines()

		lowerCAmelCase__        =   []
		for line in triangle:
				lowerCAmelCase__        =   []
				for number in line.strip().split(""" """     ):
						numbers_from_line.append(int(lowerCamelCase__     )     )
				a.append(lowerCamelCase__     )

		for i in range(1			,  len(lowerCamelCase__     )     ):
				for j in range(len(a[i]     )     ):
						lowerCAmelCase__        =   a[i - 1][j] if j != len(a[i - 1]     ) else 0
						lowerCAmelCase__        =   a[i - 1][j - 1] if j > 0 else 0
						a[i][j] += max(lowerCamelCase__			,  lowerCamelCase__     )
		return max(a[-1]     )


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




