
"""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			:       Dict   		=       False


_A			:       Optional[Any]   		=       logging.get_logger(__name__)
_A			:       Optional[Any]   		=       """ybelkada/fonts"""


def   __magic_name__	(      ) ->				Optional[Any]:
       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   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       Optional[Any]     , __snake_case				:       int  ) ->				Union[str, Any]:
       requires_backends(__snake_case     , ["torch"]  )
       _check_torch_version()

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

       lowercase    :				int       		=							textwrap.TextWrapper(width=80  )
       lowercase    :				Union[str, Any]       		=							wrapper.wrap(text=__snake_case  )
       lowercase    :				Dict       		=							"\n".join(__snake_case  )

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

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

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


def   __magic_name__	(      __snake_case				:       np.ndarray     , __snake_case				:       str     , **__snake_case				:       Optional[int]  ) ->				Optional[Any]:
       requires_backends(__snake_case     , "vision"  )

       # Convert to PIL image if necessary
       lowercase    :				List[Any]       		=							to_pil_image(__snake_case  )

       lowercase    :				Tuple       		=							render_text(__snake_case     , **__snake_case  )
       lowercase    :				Optional[Any]       		=							max(header_image.width     , image.width  )

       lowercase    :				Any       		=							int(image.height * (new_width / image.width)  )
       lowercase    :				Optional[Any]       		=							int(header_image.height * (new_width / header_image.width)  )

       lowercase    :				List[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    :				Any       		=							to_numpy_array(__snake_case  )

       if infer_channel_dimension_format(__snake_case  ) == ChannelDimension.LAST:
              lowercase    :				int       		=							to_channel_dimension_format(__snake_case     , ChannelDimension.LAST  )

       return new_image

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""flattened_patches"""]
   def __init__(			self	,    _a = True	,    _a = True	,    _a = None	,    _a = 2_048	,    _a = False	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				Tuple       		=							patch_size if patch_size is not None else {"height": 16, "width": 16}
          lowercase    :				Tuple       		=							do_normalize
          lowercase    :				Optional[int]       		=							do_convert_rgb
          lowercase    :				Any       		=							max_patches
          lowercase    :				Dict       		=							is_vqa
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    **_a				):
          requires_backends(self.extract_flattened_patches	,    "torch"				)
          _check_torch_version()

          # convert to torch
          lowercase    :				Any       		=							to_channel_dimension_format(_a	,    ChannelDimension.FIRST				)
          lowercase    :				Optional[Any]       		=							torch.from_numpy(_a				)

          lowercase		,    lowercase    :				Union[str, Any]       		=							patch_size["height"], patch_size["width"]
          lowercase		,    lowercase    :				Union[str, Any]       		=							get_image_size(_a				)

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

          lowercase    :				int       		=							torch.nn.functional.interpolate(
              image.unsqueeze(0				)	,    size=(resized_height, resized_width)	,    mode="bilinear"	,    align_corners=_a	,    antialias=_a	,    ).squeeze(0				)

          # [1, rows, columns, patch_height * patch_width * image_channels]
          lowercase    :				str       		=							torch_extract_patches(_a	,    _a	,    _a				)

          lowercase    :				Union[str, Any]       		=							patches.shape
          lowercase    :				Optional[Any]       		=							patches_shape[1]
          lowercase    :				List[str]       		=							patches_shape[2]
          lowercase    :				Tuple       		=							patches_shape[3]

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

          # [rows * columns, 1]
          lowercase    :				Dict       		=							torch.arange(_a				).reshape([rows, 1]				).repeat(1	,    _a				).reshape([rows * columns, 1]				)
          lowercase    :				Any       		=							torch.arange(_a				).reshape([1, columns]				).repeat(_a	,    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    :				Tuple       		=							row_ids.to(torch.floataa				)
          lowercase    :				Dict       		=							col_ids.to(torch.floataa				)

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

          # [max_patches, 2 + patch_height * patch_width * image_channels]
          lowercase    :				Optional[Any]       		=							torch.nn.functional.pad(_a	,    [0, 0, 0, max_patches - (rows * columns)]				).float()

          lowercase    :				Optional[int]       		=							to_numpy_array(_a				)

          return result
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    **_a				):
          if image.dtype == np.uinta:
                 lowercase    :				Tuple       		=							image.astype(np.floataa				)

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

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

   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				List[Any]       		=							do_normalize if do_normalize is not None else self.do_normalize
          lowercase    :				Any       		=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
          lowercase    :				List[str]       		=							patch_size if patch_size is not None else self.patch_size
          lowercase    :				Any       		=							max_patches if max_patches is not None else self.max_patches
          lowercase    :				Tuple       		=							self.is_vqa

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

          lowercase    :				Tuple       		=							make_list_of_images(_a				)

          if not valid_images(_a				):
                 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    :				str       		=							[convert_to_rgb(_a				) for image in images]

          # All transformations expect numpy arrays.
          lowercase    :				Tuple       		=							[to_numpy_array(_a				) 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"	,    _a				)
                 lowercase    :				Optional[Any]       		=							kwargs.pop("font_path"	,    _a				)

                 if isinstance(_a	,    _a				):
                        lowercase    :				Tuple       		=							[header_text] * len(_a				)

                 lowercase    :				str       		=							[
                     render_header(_a	,    header_text[i]	,    font_bytes=_a	,    font_path=_a				)
                     for i, image in enumerate(_a				)
                 ]

          if do_normalize:
                 lowercase    :				Dict       		=							[self.normalize(image=_a				) for image in images]

          # convert to torch tensor and permute
          lowercase    :				str       		=							[
              self.extract_flattened_patches(image=_a	,    max_patches=_a	,    patch_size=_a				)
              for image in images
          ]

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

          lowercase    :				Dict       		=							BatchFeature(
              data={"flattened_patches": images, "attention_mask": attention_masks}	,    tensor_type=_a				)

          return encoded_outputs




"""simple docstring"""




from __future__ import annotations

from functools import lru_cache
from math import ceil

_A			:       Optional[Any]   		=       1_00

_A			:       Optional[int]   		=       set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A			:       int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
   if prime not in primes:
      continue
   primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))


@lru_cache(maxsize=100  )
def   __magic_name__	(      __snake_case				:       int  ) ->				set[int]:
       if number_to_partition < 0:
              return set()
       elif number_to_partition == 0:
              return {1}

       lowercase    :				set[int]       		=							set()
       lowercase    :				int
       lowercase    :				int

       for prime in primes:
              if prime > number_to_partition:
                     continue
              for sub in partition(number_to_partition - prime  ):
                     ret.add(sub * prime  )

       return ret


def   __magic_name__	(      __snake_case				:       int = 5000  ) ->				int | None:
       for number_to_partition in range(1     , __snake_case  ):
              if len(partition(__snake_case  )  ) > number_unique_partitions:
                     return number_to_partition
       return None


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


