'''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_						(					)					->     Any:






 '''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_						(					snake_case_       :       List[str]						,		snake_case_       :       List[Any]						,		snake_case_       :       Dict      )					->     Optional[int]:






 '''simple docstring'''

 requires_backends(lowerCAmelCase_						,		["""torch"""]      )
 _check_torch_version()

 __lowerCAmelCase											=						image_tensor.unsqueeze(0      )
 __lowerCAmelCase											=						torch.nn.functional.unfold(lowerCAmelCase_						,		(patch_height, patch_width)						,		stride=(patch_height, patch_width)      )
 __lowerCAmelCase											=						patches.reshape(image_tensor.size(0      )						,		image_tensor.size(1      )						,		lowerCAmelCase_						,		lowerCAmelCase_						,		-1      )
 __lowerCAmelCase											=						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_						(					snake_case_       :       List[Any]						,		snake_case_       :       Union[str, Any] = 36						,		snake_case_       :       Union[str, Any] = "black"						,		snake_case_       :       Any = "white"						,		snake_case_       :       Tuple = 5						,		snake_case_       :       Optional[int] = 5						,		snake_case_       :       Optional[Any] = 5						,		snake_case_       :       Any = 5						,		snake_case_       :       Tuple = None						,		snake_case_       :       Dict = None						,		)					->     Optional[Any]:






 '''simple docstring'''

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

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

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

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

 # Create the actual image with a bit of padding around the text.
 __lowerCAmelCase											=						text_width + left_padding + right_padding
 __lowerCAmelCase											=						text_height + top_padding + bottom_padding
 __lowerCAmelCase											=						Image.new("""RGB"""						,		(image_width, image_height)						,		lowerCAmelCase_      )
 __lowerCAmelCase											=						ImageDraw.Draw(lowerCAmelCase_      )
 draw.text(xy=(left_padding, top_padding)						,		text=lowerCAmelCase_						,		fill=lowerCAmelCase_						,		font=lowerCAmelCase_      )
 return image







def      UpperCamelCase_						(					snake_case_       :       Optional[int]						,		snake_case_       :       Optional[Any]						,		**snake_case_       :       Optional[Any]      )					->     Optional[Any]:






 '''simple docstring'''

 requires_backends(lowerCAmelCase_						,		"""vision"""      )

 # Convert to PIL image if necessary
 __lowerCAmelCase											=						to_pil_image(lowerCAmelCase_      )

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

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

 __lowerCAmelCase											=						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
 __lowerCAmelCase											=						to_numpy_array(lowerCAmelCase_      )

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

 return new_image







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					["flattened_patches"]
 def __init__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	int = 20_48      ,       SCREAMING_SNAKE_CASE__   :	bool = False      ,       **SCREAMING_SNAKE_CASE__   :	Dict      ,       )  ->    None:
  super().__init__(**UpperCAmelCase__							)
  __lowerCAmelCase											=						patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
  __lowerCAmelCase											=						do_normalize
  __lowerCAmelCase											=						do_convert_rgb
  __lowerCAmelCase											=						max_patches
  __lowerCAmelCase											=						is_vqa
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	dict      ,       **SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    np.ndarray:
  requires_backends(self.extract_flattened_patches      ,       """torch"""							)
  _check_torch_version()

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

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

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

  __lowerCAmelCase											=						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]
  __lowerCAmelCase											=						torch_extract_patches(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__							)

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

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

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

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

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

  __lowerCAmelCase											=						to_numpy_array(UpperCAmelCase__							)

  return result
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    np.ndarray:
  if image.dtype == np.uinta:
   __lowerCAmelCase											=						image.astype(np.floataa							)

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

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





 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	ImageInput      ,       SCREAMING_SNAKE_CASE__   :	Optional[str] = None      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Dict[str, int]] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, TensorType]] = None      ,       SCREAMING_SNAKE_CASE__   :	ChannelDimension = ChannelDimension.FIRST      ,       **SCREAMING_SNAKE_CASE__   :	Any      ,       )  ->    ImageInput:
  __lowerCAmelCase											=						do_normalize if do_normalize is not None else self.do_normalize
  __lowerCAmelCase											=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  __lowerCAmelCase											=						patch_size if patch_size is not None else self.patch_size
  __lowerCAmelCase											=						max_patches if max_patches is not None else self.max_patches
  __lowerCAmelCase											=						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 """							)

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

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

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

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

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

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

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

  __lowerCAmelCase											=						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() = }")
