"""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:
						SCREAMING_SNAKE_CASE									=						False


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


def      __lowerCAmelCase(		):






	"""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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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

	_lowercase :    List[Any]							=							image_tensor.unsqueeze(0			)
	_lowercase :    Dict							=							torch.nn.functional.unfold(__UpperCAmelCase     ,(patch_height, patch_width)     ,stride=(patch_height, patch_width)			)
	_lowercase :    List[Any]							=							patches.reshape(image_tensor.size(0			)     ,image_tensor.size(1			)     ,__UpperCAmelCase     ,__UpperCAmelCase     ,-1			)
	_lowercase :    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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase = 36     ,__UpperCAmelCase = "black"     ,__UpperCAmelCase = "white"     ,__UpperCAmelCase = 5     ,__UpperCAmelCase = 5     ,__UpperCAmelCase = 5     ,__UpperCAmelCase = 5     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,):






	"""simple docstring"""





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

	_lowercase :    List[Any]							=							textwrap.TextWrapper(width=80			)
	_lowercase :    int							=							wrapper.wrap(text=__UpperCAmelCase			)
	_lowercase :    Optional[int]							=							'\n'.join(__UpperCAmelCase			)

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

	# Use a temporary canvas to determine the width and height in pixels when
	# rendering the text.
	_lowercase :    Tuple							=							ImageDraw.Draw(Image.new('RGB'     ,(1, 1)     ,__UpperCAmelCase			)			)
	_lowercase				,      _lowercase				,      _lowercase				,      _lowercase :    Union[str, Any]							=							temp_draw.textbbox((0, 0)     ,__UpperCAmelCase     ,__UpperCAmelCase			)

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	requires_backends(__UpperCAmelCase     ,'vision'			)

	# Convert to PIL image if necessary
	_lowercase :    Optional[int]							=							to_pil_image(__UpperCAmelCase			)

	_lowercase :    Dict							=							render_text(__UpperCAmelCase     ,**__UpperCAmelCase			)
	_lowercase :    Dict							=							max(header_image.width     ,image.width			)

	_lowercase :    Union[str, Any]							=							int(image.height * (new_width / image.width)			)
	_lowercase :    Optional[Any]							=							int(header_image.height * (new_width / header_image.width)			)

	_lowercase :    Union[str, Any]							=							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 :    Dict							=							to_numpy_array(__UpperCAmelCase			)

	if infer_channel_dimension_format(__UpperCAmelCase			) == ChannelDimension.LAST:
		_lowercase :    int							=							to_channel_dimension_format(__UpperCAmelCase     ,ChannelDimension.LAST			)

	return new_image







class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["flattened_patches"]







				def __init__( self    :       Any			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Dict[str, int] = None			,					lowerCamelCase_    :       int = 2_0_4_8			,					lowerCamelCase_    :       bool = False			,					**lowerCamelCase_    :       Dict			,					):

					"""simple docstring"""






					super().__init__(**lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
					_lowercase :    Optional[Any]							=							do_normalize
					_lowercase :    str							=							do_convert_rgb
					_lowercase :    Union[str, Any]							=							max_patches
					_lowercase :    int							=							is_vqa







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       np.ndarray			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       dict			,					**lowerCamelCase_    :       Union[str, Any]						):

					"""simple docstring"""






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

					# convert to torch
					_lowercase :    str							=							to_channel_dimension_format(lowerCamelCase_			,					ChannelDimension.FIRST						)
					_lowercase :    List[Any]							=							torch.from_numpy(lowerCamelCase_						)

					_lowercase				,      _lowercase :    str							=							patch_size['height'], patch_size['width']
					_lowercase				,      _lowercase :    Optional[Any]							=							get_image_size(lowerCamelCase_						)

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

					_lowercase :    Dict							=							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]
					_lowercase :    Any							=							torch_extract_patches(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    str							=							patches.shape
					_lowercase :    List[Any]							=							patches_shape[1]
					_lowercase :    List[str]							=							patches_shape[2]
					_lowercase :    int							=							patches_shape[3]

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

					# [rows * columns, 1]
					_lowercase :    Optional[int]							=							torch.arange(lowerCamelCase_						).reshape([rows, 1]						).repeat(1			,					lowerCamelCase_						).reshape([rows * columns, 1]						)
					_lowercase :    List[Any]							=							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]
					_lowercase :    Any							=							row_ids.to(torch.floataa						)
					_lowercase :    Optional[Any]							=							col_ids.to(torch.floataa						)

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

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

					_lowercase :    Tuple							=							to_numpy_array(lowerCamelCase_						)

					return result







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

					"""simple docstring"""






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

					# take mean across the whole `image`
					_lowercase :    List[Any]							=							np.mean(lowerCamelCase_						)
					_lowercase :    Any							=							np.std(lowerCamelCase_						)
					_lowercase :    Dict							=							max(lowerCamelCase_			,					1.0 / math.sqrt(np.prod(image.shape						)						)						)

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







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					lowerCamelCase_    :       ImageInput			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       bool = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[Dict[str, int]] = None			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					lowerCamelCase_    :       ChannelDimension = ChannelDimension.FIRST			,					**lowerCamelCase_    :       Tuple			,					):

					"""simple docstring"""






					_lowercase :    List[str]							=							do_normalize if do_normalize is not None else self.do_normalize
					_lowercase :    Union[str, Any]							=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
					_lowercase :    str							=							patch_size if patch_size is not None else self.patch_size
					_lowercase :    Dict							=							max_patches if max_patches is not None else self.max_patches
					_lowercase :    List[str]							=							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 '						)

					_lowercase :    Any							=							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:
						_lowercase :    Tuple							=							[convert_to_rgb(lowerCamelCase_						) for image in images]

					# All transformations expect numpy arrays.
					_lowercase :    Any							=							[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.'						)
						_lowercase :    str							=							kwargs.pop('font_bytes'			,					lowerCamelCase_						)
						_lowercase :    str							=							kwargs.pop('font_path'			,					lowerCamelCase_						)

						if isinstance(lowerCamelCase_			,					lowerCamelCase_						):
							_lowercase :    Any							=							[header_text] * len(lowerCamelCase_						)

						_lowercase :    Dict							=							[
						    render_header(lowerCamelCase_			,					header_text[i]			,					font_bytes=lowerCamelCase_			,					font_path=lowerCamelCase_						)
						    for i, image in enumerate(lowerCamelCase_						)
						]

					if do_normalize:
						_lowercase :    Tuple							=							[self.normalize(image=lowerCamelCase_						) for image in images]

					# convert to torch tensor and permute
					_lowercase :    List[Any]							=							[
					    self.extract_flattened_patches(image=lowerCamelCase_			,					max_patches=lowerCamelCase_			,					patch_size=lowerCamelCase_						)
					    for image in images
					]

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

					_lowercase :    str							=							BatchFeature(
					    data={'flattened_patches': images, 'attention_mask': attention_masks}			,					tensor_type=lowerCamelCase_						)

					return encoded_outputs




"""simple docstring"""







import math


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			):
		_lowercase :    List[Any]							=							F'''Input value of [number={number}] must be an integer'''
		raise TypeError(__UpperCAmelCase			)

	if number < 1:
		_lowercase :    List[Any]							=							F'''Input value of [number={number}] must be > 0'''
		raise ValueError(__UpperCAmelCase			)
	elif number == 1:
		return 3
	elif number == 2:
		return 5
	else:
		_lowercase :    str							=							int(math.log(number // 3     ,2			)			) + 2

		_lowercase :    Union[str, Any]							=							[3, 5]
		_lowercase :    Optional[int]							=							2
		_lowercase :    List[Any]							=							3
		for block in range(1     ,__UpperCAmelCase			):
			for _ in range(__UpperCAmelCase			):
				proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]			)
				proth_index += 1
			increment *= 2

	return proth_list[number - 1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for number in range(11):
												SCREAMING_SNAKE_CASE									=						0
												try:
																		SCREAMING_SNAKE_CASE									=						proth(number)
												except ValueError:
																		print(f"""ValueError: there is no {number}th Proth number""")
																		continue

												print(f"""The {number}th Proth number: {value}""")



