

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_     :    Optional[Any]     		=   False


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


def 							__lowerCAmelCase     (							)		->				Optional[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 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Optional[int]						)		->				Optional[int]:
 '''simple docstring'''


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

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


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

 SCREAMING_SNAKE_CASE		    =      textwrap.TextWrapper(width=80						)
 SCREAMING_SNAKE_CASE		    =      wrapper.wrap(text=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      '\n'.join(_UpperCamelCase						)

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

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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	np.ndarray		,				_UpperCamelCase		:	str		,				**_UpperCamelCase		:	Optional[int]						)		->				Union[str, Any]:
 '''simple docstring'''


 requires_backends(_UpperCamelCase		,				'vision'						)

 # Convert to PIL image if necessary
 SCREAMING_SNAKE_CASE		    =      to_pil_image(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      render_text(_UpperCamelCase		,				**_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      max(header_image.width		,				image.width						)

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

 SCREAMING_SNAKE_CASE		    =      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
 SCREAMING_SNAKE_CASE		    =      to_numpy_array(_UpperCamelCase						)

 if infer_channel_dimension_format(_UpperCamelCase						) == ChannelDimension.LAST:
  SCREAMING_SNAKE_CASE		    =      to_channel_dimension_format(_UpperCamelCase		,				ChannelDimension.LAST						)

 return new_image




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =["flattened_patches"]

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

     """simple docstring"""






     super().__init__(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
     SCREAMING_SNAKE_CASE		    =      do_normalize
     SCREAMING_SNAKE_CASE		    =      do_convert_rgb
     SCREAMING_SNAKE_CASE		    =      max_patches
     SCREAMING_SNAKE_CASE		    =      is_vqa

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   np.ndarray				,     snake_case__			:   int				,     snake_case__			:   dict				,     **snake_case__			:   Tuple     ):

     """simple docstring"""






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

     # convert to torch
     SCREAMING_SNAKE_CASE		    =      to_channel_dimension_format(snake_case__				,     ChannelDimension.FIRST     )
     SCREAMING_SNAKE_CASE		    =      torch.from_numpy(snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      patch_size['height'], patch_size['width']
     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_image_size(snake_case__     )

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

     SCREAMING_SNAKE_CASE		    =      torch.nn.functional.interpolate(
         image.unsqueeze(0     )				,     size=(resized_height, resized_width)				,     mode='bilinear'				,     align_corners=snake_case__				,     antialias=snake_case__				,     ).squeeze(0     )

     # [1, rows, columns, patch_height * patch_width * image_channels]
     SCREAMING_SNAKE_CASE		    =      torch_extract_patches(snake_case__				,     snake_case__				,     snake_case__     )

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

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

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

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

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

     SCREAMING_SNAKE_CASE		    =      to_numpy_array(snake_case__     )

     return result

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   np.ndarray				,     snake_case__			:   Optional[Union[str, ChannelDimension]] = None				,     **snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






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

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

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




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

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE		    =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
     SCREAMING_SNAKE_CASE		    =      patch_size if patch_size is not None else self.patch_size
     SCREAMING_SNAKE_CASE		    =      max_patches if max_patches is not None else self.max_patches
     SCREAMING_SNAKE_CASE		    =      self.is_vqa

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

     SCREAMING_SNAKE_CASE		    =      make_list_of_images(snake_case__     )

     if not valid_images(snake_case__     ):
      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:
      SCREAMING_SNAKE_CASE		    =      [convert_to_rgb(snake_case__     ) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE		    =      [to_numpy_array(snake_case__     ) for image in images]

     if is_vqa:
      if header_text is None:
       raise ValueError('A header text must be provided for VQA models.'     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('font_bytes'				,     snake_case__     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('font_path'				,     snake_case__     )

      if isinstance(snake_case__				,     snake_case__     ):
       SCREAMING_SNAKE_CASE		    =      [header_text] * len(snake_case__     )

      SCREAMING_SNAKE_CASE		    =      [
          render_header(snake_case__				,     header_text[i]				,     font_bytes=snake_case__				,     font_path=snake_case__     )
          for i, image in enumerate(snake_case__     )
      ]

     if do_normalize:
      SCREAMING_SNAKE_CASE		    =      [self.normalize(image=snake_case__     ) for image in images]

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

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

     SCREAMING_SNAKE_CASE		    =      BatchFeature(
         data={'flattened_patches': images, 'attention_mask': attention_masks}				,     tensor_type=snake_case__     )

     return encoded_outputs




from __future__ import annotations

from math import gcd


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int = 2		,				_UpperCamelCase		:	int = 1		,				_UpperCamelCase		:	int = 3		,				)		->				int | None:
 '''simple docstring'''


 if num < 2:
  raise ValueError('The input value cannot be less than 2'						)

 # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
 # algorithm struggles to find factors that are divisible by two.
 # As a workaround, we specifically check for two and even inputs.
 #   See: https://math.stackexchange.com/a/2856214/165820
 if num > 2 and num % 2 == 0:
  return 2

 # Pollard's Rho algorithm requires a function that returns pseudorandom
 # values between 0 <= X < ``num``.  It doesn't need to be random in the
 # sense that the output value is cryptographically secure or difficult
 # to calculate, it only needs to be random in the sense that all output
 # values should be equally likely to appear.
 # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
 # However, the success of Pollard's algorithm isn't guaranteed and is
 # determined in part by the initial seed and the chosen random function.
 # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
 # where ``C`` is a value that we can modify between each attempt.
 def rand_fn(_UpperCamelCase		:	int		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						) -> int:
  return (pow(_UpperCamelCase		,				2						) + step) % modulus

 for _ in range(_UpperCamelCase						):
  # These track the position within the cycle detection logic.
  SCREAMING_SNAKE_CASE		    =      seed
  SCREAMING_SNAKE_CASE		    =      seed

  while True:
   # At each iteration, the tortoise moves one step and the hare moves two.
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      rand_fn(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)

   # At some point both the tortoise and the hare will enter a cycle whose
   # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
   # the tortoise and hare will end up on the same value modulo ``p``.
   # We can detect when this happens because the position difference between
   # the tortoise and the hare will share a common divisor with ``num``.
   SCREAMING_SNAKE_CASE		    =      gcd(hare - tortoise		,				_UpperCamelCase						)

   if divisor == 1:
    # No common divisor yet, just keep searching.
    continue
   else:
    # We found a common divisor!
    if divisor == num:
     # Unfortunately, the divisor is ``num`` itself and is useless.
     break
    else:
     # The divisor is a nontrivial factor of ``num``!
     return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
  SCREAMING_SNAKE_CASE		    =      hare

  # The new step value for the random function can just be incremented.
  # At first the results will be similar to what the old function would
  # have produced, but the value will quickly diverge after a bit.
  step += 1

 # We haven't found a divisor within the requested number of attempts.
 # We were unlucky or ``num`` itself is actually prime.
 return None


if __name__ == "__main__":
      import argparse

      a_     :    int     		=   argparse.ArgumentParser()
      parser.add_argument(
          "num",
          type=int,
          help="The value to find a divisor of",
      )
      parser.add_argument(
          "--attempts",
          type=int,
          default=3,
          help="The number of attempts before giving up",
      )
      a_     :    Tuple     		=   parser.parse_args()

      a_     :    int     		=   pollard_rho(args.num, attempts=args.attempts)
      if divisor is None:
            print(F"""{args.num} is probably prime""")
      else:
            a_     :    Union[str, Any]     		=   args.num // divisor
            print(F"""{args.num} = {divisor} * {quotient}""")

