

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 						_snake_case(   )							->				Dict:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Dict:





    '''simple docstring'''






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

    A__   				=       image_tensor.unsqueeze(0				)
    A__   				=       torch.nn.functional.unfold(SCREAMING_SNAKE_CASE__   ,      (patch_height, patch_width)   ,      stride=(patch_height, patch_width)				)
    A__   				=       patches.reshape(image_tensor.size(0				)   ,      image_tensor.size(1				)   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      -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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int = 36   ,      SCREAMING_SNAKE_CASE__      :     str = "black"   ,      SCREAMING_SNAKE_CASE__      :     str = "white"   ,      SCREAMING_SNAKE_CASE__      :     int = 5   ,      SCREAMING_SNAKE_CASE__      :     int = 5   ,      SCREAMING_SNAKE_CASE__      :     int = 5   ,      SCREAMING_SNAKE_CASE__      :     int = 5   ,      SCREAMING_SNAKE_CASE__      :     Optional[bytes] = None   ,      SCREAMING_SNAKE_CASE__      :     Optional[str] = None   ,      )							->				Image.Image:





    '''simple docstring'''






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

    A__   				=       textwrap.TextWrapper(width=80				)
    A__   				=       wrapper.wrap(text=SCREAMING_SNAKE_CASE__				)
    A__   				=       '\n'.join(SCREAMING_SNAKE_CASE__				)

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

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

    # 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)   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       ImageDraw.Draw(SCREAMING_SNAKE_CASE__				)
    draw.text(xy=(left_padding, top_padding)   ,      text=SCREAMING_SNAKE_CASE__   ,      fill=SCREAMING_SNAKE_CASE__   ,      font=SCREAMING_SNAKE_CASE__				)
    return image
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     np.ndarray   ,      SCREAMING_SNAKE_CASE__      :     str   ,      **SCREAMING_SNAKE_CASE__      :     int				)							->				List[Any]:





    '''simple docstring'''






    requires_backends(SCREAMING_SNAKE_CASE__   ,      'vision'				)

    # Convert to PIL image if necessary
    A__   				=       to_pil_image(SCREAMING_SNAKE_CASE__				)

    A__   				=       render_text(SCREAMING_SNAKE_CASE__   ,      **SCREAMING_SNAKE_CASE__				)
    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(SCREAMING_SNAKE_CASE__				)

    if infer_channel_dimension_format(SCREAMING_SNAKE_CASE__				) == ChannelDimension.LAST:
        A__   				=       to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,      ChannelDimension.LAST				)

    return new_image


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							['flattened_patches']


    def __init__(				self			:	Optional[int],lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 2_0_4_8,lowercase_			:	bool = False,**lowercase_			:	str,)->   None:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        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 						snake_case__					(				self			:	List[Any],lowercase_			:	np.ndarray,lowercase_			:	int,lowercase_			:	dict,**lowercase_			:	Optional[int]		)->   np.ndarray:





        '''simple docstring'''







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

        # convert to torch
        A__   				=       to_channel_dimension_format(lowercase_,ChannelDimension.FIRST		)
        A__   				=       torch.from_numpy(lowercase_		)

        A__				,						A__   				=       patch_size['height'], patch_size['width']
        A__				,						A__   				=       get_image_size(lowercase_		)

        # 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		),lowercase_		),1		)
        A__   				=       max(min(math.floor(scale * image_width / patch_width		),lowercase_		),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=lowercase_,antialias=lowercase_,).squeeze(0		)

        # [1, rows, columns, patch_height * patch_width * image_channels]
        A__   				=       torch_extract_patches(lowercase_,lowercase_,lowercase_		)

        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(lowercase_		).reshape([rows, 1]		).repeat(1,lowercase_		).reshape([rows * columns, 1]		)
        A__   				=       torch.arange(lowercase_		).reshape([1, columns]		).repeat(lowercase_,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(lowercase_,[0, 0, 0, max_patches - (rows * columns)]		).float()

        A__   				=       to_numpy_array(lowercase_		)

        return result


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





        '''simple docstring'''







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

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

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


    def 						snake_case__					(				self			:	str,lowercase_			:	ImageInput,lowercase_			:	Optional[str] = None,lowercase_			:	bool = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[int] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	Optional[Union[str, TensorType]] = None,lowercase_			:	ChannelDimension = ChannelDimension.FIRST,**lowercase_			:	Dict,)->   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',lowercase_		) is not None:
            raise ValueError('data_format is not an accepted input as the outputs are '		)

        A__   				=       make_list_of_images(lowercase_		)

        if not valid_images(lowercase_		):
            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(lowercase_		) for image in images]

        # All transformations expect numpy arrays.
        A__   				=       [to_numpy_array(lowercase_		) 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',lowercase_		)
            A__   				=       kwargs.pop('font_path',lowercase_		)

            if isinstance(lowercase_,lowercase_		):
                A__   				=       [header_text] * len(lowercase_		)

            A__   				=       [
                render_header(lowercase_,header_text[i],font_bytes=lowercase_,font_path=lowercase_		)
                for i, image in enumerate(lowercase_		)
            ]

        if do_normalize:
            A__   				=       [self.normalize(image=lowercase_		) for image in images]

        # convert to torch tensor and permute
        A__   				=       [
            self.extract_flattened_patches(image=lowercase_,max_patches=lowercase_,patch_size=lowercase_		)
            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=lowercase_		)

        return encoded_outputs




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowercase_			=  (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_			=  (4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42




class 			A						:







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	Iterable[int]		)->   None:





        '''simple docstring'''







        A__   				=       None
        for i in sorted(lowercase_,reverse=lowercase_		):
            A__   				=       Node(lowercase_,self.head		)


    def __iter__(				self			:	List[str]		)->   Iterator[int]:





        '''simple docstring'''







        A__   				=       self.head
        while node:
            yield node.data
            A__   				=       node.next_node


    def __len__(				self			:	str		)->   int:





        '''simple docstring'''







        return sum(1 for _ in self		)


    def __str__(				self			:	Optional[int]		)->   str:





        '''simple docstring'''







        return " -> ".join([str(lowercase_		) for node in self]		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     SortedLinkedList   ,      SCREAMING_SNAKE_CASE__      :     SortedLinkedList				)							->				SortedLinkedList:





    '''simple docstring'''






    return SortedLinkedList(list(SCREAMING_SNAKE_CASE__				) + list(SCREAMING_SNAKE_CASE__				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     lowercase_			=  SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

