


"""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:
		a__				:							Tuple						=     False


a__				:							int						=     logging.get_logger(__name__)
a__				:							Optional[Any]						=     '''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()

					__SCREAMING_SNAKE_CASE						   =				image_tensor.unsqueeze(0   )
					__SCREAMING_SNAKE_CASE						   =				torch.nn.functional.unfold(lowerCAmelCase_  ,						(patch_height, patch_width)  ,						stride=(patch_height, patch_width)   )
					__SCREAMING_SNAKE_CASE						   =				patches.reshape(image_tensor.size(0   )  ,						image_tensor.size(1   )  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						-1   )
					__SCREAMING_SNAKE_CASE						   =				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.

					__SCREAMING_SNAKE_CASE						   =				textwrap.TextWrapper(width=80   )
					__SCREAMING_SNAKE_CASE						   =				wrapper.wrap(text=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				"\n".join(lowerCAmelCase_   )

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

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

					# Create the actual image with a bit of padding around the text.
					__SCREAMING_SNAKE_CASE						   =				text_width + left_padding + right_padding
					__SCREAMING_SNAKE_CASE						   =				text_height + top_padding + bottom_padding
					__SCREAMING_SNAKE_CASE						   =				Image.new("RGB"  ,						(image_width, image_height)  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				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
					__SCREAMING_SNAKE_CASE						   =				to_pil_image(lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				render_text(lowerCAmelCase_  ,						**lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				max(header_image.width  ,						image.width   )

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

					__SCREAMING_SNAKE_CASE						   =				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
					__SCREAMING_SNAKE_CASE						   =				to_numpy_array(lowerCAmelCase_   )

					if infer_channel_dimension_format(lowerCAmelCase_   ) == ChannelDimension.LAST:
										__SCREAMING_SNAKE_CASE						   =				to_channel_dimension_format(lowerCAmelCase_  ,						ChannelDimension.LAST   )

					return new_image




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





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




	def __init__( self			:				List[str]			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				int = 2_0_4_8			,			UpperCAmelCase__			:				bool = False			,			**UpperCAmelCase__			:				Dict			,			)       ->	None:
						super().__init__(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
						__SCREAMING_SNAKE_CASE						   =				do_normalize
						__SCREAMING_SNAKE_CASE						   =				do_convert_rgb
						__SCREAMING_SNAKE_CASE						   =				max_patches
						__SCREAMING_SNAKE_CASE						   =				is_vqa




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				dict			,			**UpperCAmelCase__			:				List[str]   )       ->	np.ndarray:
						requires_backends(self.extract_flattened_patches			,			"torch"   )
						_check_torch_version()

						# convert to torch
						__SCREAMING_SNAKE_CASE						   =				to_channel_dimension_format(UpperCAmelCase__			,			ChannelDimension.FIRST   )
						__SCREAMING_SNAKE_CASE						   =				torch.from_numpy(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				patch_size["height"], patch_size["width"]
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				get_image_size(UpperCAmelCase__   )

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

						__SCREAMING_SNAKE_CASE						   =				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]
						__SCREAMING_SNAKE_CASE						   =				torch_extract_patches(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )

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

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

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

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

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

						__SCREAMING_SNAKE_CASE						   =				to_numpy_array(UpperCAmelCase__   )

						return result




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Tuple   )       ->	np.ndarray:
						if image.dtype == np.uinta:
											__SCREAMING_SNAKE_CASE						   =				image.astype(np.floataa   )

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

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




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			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__			:				Any			,			)       ->	ImageInput:
						__SCREAMING_SNAKE_CASE						   =				do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE						   =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
						__SCREAMING_SNAKE_CASE						   =				patch_size if patch_size is not None else self.patch_size
						__SCREAMING_SNAKE_CASE						   =				max_patches if max_patches is not None else self.max_patches
						__SCREAMING_SNAKE_CASE						   =				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 "   )

						__SCREAMING_SNAKE_CASE						   =				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:
											__SCREAMING_SNAKE_CASE						   =				[convert_to_rgb(UpperCAmelCase__   ) for image in images]

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE						   =				[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."   )
											__SCREAMING_SNAKE_CASE						   =				kwargs.pop("font_bytes"			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				kwargs.pop("font_path"			,			UpperCAmelCase__   )

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

											__SCREAMING_SNAKE_CASE						   =				[
											    render_header(UpperCAmelCase__			,			header_text[i]			,			font_bytes=UpperCAmelCase__			,			font_path=UpperCAmelCase__   )
											    for i, image in enumerate(UpperCAmelCase__   )
											]

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

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

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

						__SCREAMING_SNAKE_CASE						   =				BatchFeature(
						    data={"flattened_patches": images, "attention_mask": attention_masks}			,			tensor_type=UpperCAmelCase__   )

						return encoded_outputs




"""simple docstring"""







from collections import defaultdict
from math import ceil, sqrt




def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000  ,						lowerCAmelCase_ = 10   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				defaultdict(lowerCAmelCase_   )

					for outer_width in range(3  ,						(t_limit // 4) + 2   ):
										if outer_width * outer_width > t_limit:
															__SCREAMING_SNAKE_CASE						   =				max(
															    ceil(sqrt(outer_width * outer_width - t_limit   )   )  ,						1   )
										else:
															__SCREAMING_SNAKE_CASE						   =				1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(lowerCAmelCase_  ,						outer_width - 1  ,						2   ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10   )


if __name__ == "__main__":
		print(F"{solution() = }")
