

"""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							=  False


_A							=  logging.get_logger(__name__)
_A							=  """ybelkada/fonts"""




def 					a__						(						)	-> Dict:
      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 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[Any]:
      requires_backends(lowerCAmelCase			,	["""torch"""]    )
      _check_torch_version()

      UpperCAmelCase__			:							List[Any]							=    image_tensor.unsqueeze(0    )
      UpperCAmelCase__			:							Optional[Any]							=    torch.nn.functional.unfold(lowerCAmelCase			,	(patch_height, patch_width)			,	stride=(patch_height, patch_width)    )
      UpperCAmelCase__			:							str							=    patches.reshape(image_tensor.size(0    )			,	image_tensor.size(1    )			,	lowerCAmelCase			,	lowerCAmelCase			,	-1    )
      UpperCAmelCase__			:							Union[str, Any]							=    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 					a__						(						lowerCAmelCase			,	lowerCAmelCase = 36			,	lowerCAmelCase = "black"			,	lowerCAmelCase = "white"			,	lowerCAmelCase = 5			,	lowerCAmelCase = 5			,	lowerCAmelCase = 5			,	lowerCAmelCase = 5			,	lowerCAmelCase = None			,	lowerCAmelCase = None			,	)	-> Image.Image:
      requires_backends(lowerCAmelCase			,	"""vision"""    )
      # Add new lines so that each line is no more than 80 characters.

      UpperCAmelCase__			:							List[Any]							=    textwrap.TextWrapper(width=80    )
      UpperCAmelCase__			:							Optional[Any]							=    wrapper.wrap(text=lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    """\n""".join(lowerCAmelCase    )

      if font_bytes is not None and font_path is None:
            UpperCAmelCase__			:							Union[str, Any]							=    io.BytesIO(lowerCAmelCase    )
      elif font_path is not None:
            UpperCAmelCase__			:							List[str]							=    font_path
      else:
            UpperCAmelCase__			:							str							=    hf_hub_download(lowerCAmelCase			,	"""Arial.TTF"""    )
      UpperCAmelCase__			:							Dict							=    ImageFont.truetype(lowerCAmelCase			,	encoding="""UTF-8"""			,	size=lowerCAmelCase    )

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

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




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	**lowerCAmelCase    )	-> Union[str, Any]:
      requires_backends(lowerCAmelCase			,	"""vision"""    )

      # Convert to PIL image if necessary
      UpperCAmelCase__			:							Tuple							=    to_pil_image(lowerCAmelCase    )

      UpperCAmelCase__			:							Any							=    render_text(lowerCAmelCase			,	**lowerCAmelCase    )
      UpperCAmelCase__			:							Dict							=    max(header_image.width			,	image.width    )

      UpperCAmelCase__			:							Dict							=    int(image.height * (new_width / image.width)    )
      UpperCAmelCase__			:							List[str]							=    int(header_image.height * (new_width / header_image.width)    )

      UpperCAmelCase__			:							str							=    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
      UpperCAmelCase__			:							Union[str, Any]							=    to_numpy_array(lowerCAmelCase    )

      if infer_channel_dimension_format(lowerCAmelCase    ) == ChannelDimension.LAST:
            UpperCAmelCase__			:							Dict							=    to_channel_dimension_format(lowerCAmelCase			,	ChannelDimension.LAST    )

      return new_image





class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['flattened_patches']



  def __init__(self							,       _lowerCamelCase = True							,       _lowerCamelCase = True							,       _lowerCamelCase = None							,       _lowerCamelCase = 2048							,       _lowerCamelCase = False							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        super().__init__(**_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
        UpperCAmelCase__			:							Any							=    do_normalize
        UpperCAmelCase__			:							Tuple							=    do_convert_rgb
        UpperCAmelCase__			:							int							=    max_patches
        UpperCAmelCase__			:							Any							=    is_vqa



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       **_lowerCamelCase	):


        """simple docstring"""

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

        # convert to torch
        UpperCAmelCase__			:							Dict							=    to_channel_dimension_format(_lowerCamelCase							,       ChannelDimension.FIRST	)
        UpperCAmelCase__			:							List[str]							=    torch.from_numpy(_lowerCamelCase	)

        UpperCAmelCase__					,		UpperCAmelCase__			:							Union[str, Any]							=    patch_size["""height"""], patch_size["""width"""]
        UpperCAmelCase__					,		UpperCAmelCase__			:							Any							=    get_image_size(_lowerCamelCase	)

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

        UpperCAmelCase__			:							Any							=    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]
        UpperCAmelCase__			:							List[str]							=    torch_extract_patches(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							List[Any]							=    patches.shape
        UpperCAmelCase__			:							Tuple							=    patches_shape[1]
        UpperCAmelCase__			:							List[str]							=    patches_shape[2]
        UpperCAmelCase__			:							Any							=    patches_shape[3]

        # [rows * columns, patch_height * patch_width * image_channels]
        UpperCAmelCase__			:							List[str]							=    patches.reshape([rows * columns, depth]	)

        # [rows * columns, 1]
        UpperCAmelCase__			:							List[str]							=    torch.arange(_lowerCamelCase	).reshape([rows, 1]	).repeat(1							,       _lowerCamelCase	).reshape([rows * columns, 1]	)
        UpperCAmelCase__			:							Dict							=    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]
        UpperCAmelCase__			:							List[Any]							=    row_ids.to(torch.floataa	)
        UpperCAmelCase__			:							List[str]							=    col_ids.to(torch.floataa	)

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

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

        UpperCAmelCase__			:							Optional[Any]							=    to_numpy_array(_lowerCamelCase	)

        return result



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       **_lowerCamelCase	):


        """simple docstring"""

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

        # take mean across the whole `image`
        UpperCAmelCase__			:							str							=    np.mean(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    np.std(_lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    max(_lowerCamelCase							,       1.0 / math.sqrt(np.prod(image.shape	)	)	)

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




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = ChannelDimension.FIRST							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__			:							List[str]							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
        UpperCAmelCase__			:							List[str]							=    patch_size if patch_size is not None else self.patch_size
        UpperCAmelCase__			:							int							=    max_patches if max_patches is not None else self.max_patches
        UpperCAmelCase__			:							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 """	)

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

        # All transformations expect numpy arrays.
        UpperCAmelCase__			:							int							=    [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."""	)
              UpperCAmelCase__			:							Tuple							=    kwargs.pop("""font_bytes"""							,       _lowerCamelCase	)
              UpperCAmelCase__			:							str							=    kwargs.pop("""font_path"""							,       _lowerCamelCase	)

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

              UpperCAmelCase__			:							Optional[Any]							=    [
                  render_header(_lowerCamelCase							,       header_text[i]							,       font_bytes=_lowerCamelCase							,       font_path=_lowerCamelCase	)
                  for i, image in enumerate(_lowerCamelCase	)
              ]

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

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

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

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

        return encoded_outputs




"""simple docstring"""
from __future__ import annotations

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

        if right_element < a or left_element > b:
              return True
        if left_element >= a and right_element <= b:
              UpperCAmelCase__			:							Union[str, Any]							=    val
              if left_element != right_element:
                    UpperCAmelCase__			:							Union[str, Any]							=    val
                    UpperCAmelCase__			:							List[Any]							=    val
                    UpperCAmelCase__			:							Union[str, Any]							=    True
                    UpperCAmelCase__			:							List[Any]							=    True
              return True
        UpperCAmelCase__			:							Tuple							=    (left_element + right_element) // 2
        self.update(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        self.update(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    max(
            self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)
        return True



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    True
        if right_element < a or left_element > b:
              return -math.inf
        if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
        UpperCAmelCase__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) for i in range(1							,       self.size + 1	)]	)


if __name__ == "__main__":
    _A							=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _A							=  15
    _A							=  SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

