



'''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:
   _UpperCamelCase											=						False


_UpperCamelCase											=						logging.get_logger(__name__)
_UpperCamelCase											=						'ybelkada/fonts'

def        a_     (							)      ->      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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  requires_backends(_lowerCAmelCase       ,['torch']       )
  _check_torch_version()

  __lowerCamelCase       :       Optional[int]           = image_tensor.unsqueeze(0       )
  __lowerCamelCase       :       List[str]           = torch.nn.functional.unfold(_lowerCAmelCase       ,(patch_height, patch_width)       ,stride=(patch_height, patch_width)       )
  __lowerCamelCase       :       Tuple           = patches.reshape(image_tensor.size(0       )       ,image_tensor.size(1       )       ,_lowerCAmelCase       ,_lowerCAmelCase       ,-1       )
  __lowerCamelCase       :       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        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.

  __lowerCamelCase       :       Dict           = textwrap.TextWrapper(width=80       )
  __lowerCamelCase       :       Any           = wrapper.wrap(text=_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = '\n'.join(_lowerCAmelCase       )

  if font_bytes is not None and font_path is None:
    __lowerCamelCase       :       Dict           = io.BytesIO(_lowerCAmelCase       )
  elif font_path is not None:
    __lowerCamelCase       :       Union[str, Any]           = font_path
  else:
    __lowerCamelCase       :       Optional[Any]           = hf_hub_download(_lowerCAmelCase       ,'Arial.TTF'       )
  __lowerCamelCase       :       int           = 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       :       List[str]           = ImageDraw.Draw(Image.new('RGB'       ,(1, 1)       ,_lowerCAmelCase       )       )
  __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = temp_draw.textbbox((0, 0)       ,_lowerCAmelCase       ,_lowerCAmelCase       )

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,**_lowerCAmelCase       )      ->      List[str]:
  requires_backends(_lowerCAmelCase       ,'vision'       )

  # Convert to PIL image if necessary
  __lowerCamelCase       :       int           = to_pil_image(_lowerCAmelCase       )

  __lowerCamelCase       :       int           = render_text(_lowerCAmelCase       ,**_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = max(header_image.width       ,image.width       )

  __lowerCamelCase       :       Dict           = int(image.height * (new_width / image.width)       )
  __lowerCamelCase       :       Dict           = int(header_image.height * (new_width / header_image.width)       )

  __lowerCamelCase       :       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
  __lowerCamelCase       :       Optional[Any]           = to_numpy_array(_lowerCAmelCase       )

  if infer_channel_dimension_format(_lowerCAmelCase       ) == ChannelDimension.LAST:
    __lowerCamelCase       :       int           = to_channel_dimension_format(_lowerCAmelCase       ,ChannelDimension.LAST       )

  return new_image






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=["""flattened_patches"""]




  def __init__( self      :	List[Any]			,	_a      :	bool = True			,	_a      :	bool = True			,	_a      :	Dict[str, int] = None			,	_a      :	int = 2048			,	_a      :	bool = False			,	**_a      :	Dict			,	)					->					None:
    super().__init__(**_a   )
    __lowerCamelCase       :       Tuple           = patch_size if patch_size is not None else {'height': 16, 'width': 16}
    __lowerCamelCase       :       List[str]           = do_normalize
    __lowerCamelCase       :       str           = do_convert_rgb
    __lowerCamelCase       :       List[str]           = max_patches
    __lowerCamelCase       :       List[Any]           = is_vqa




  def 						_lowercase     ( self      :	str			,	_a      :	np.ndarray			,	_a      :	int			,	_a      :	dict			,	**_a      :	Union[str, Any]   )					->					np.ndarray:
    requires_backends(self.extract_flattened_patches			,	'torch'   )
    _check_torch_version()

    # convert to torch
    __lowerCamelCase       :       Dict           = to_channel_dimension_format(_a			,	ChannelDimension.FIRST   )
    __lowerCamelCase       :       Union[str, Any]           = torch.from_numpy(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = patch_size['height'], patch_size['width']
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = get_image_size(_a   )

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

    __lowerCamelCase       :       Optional[Any]           = 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]
    __lowerCamelCase       :       List[Any]           = torch_extract_patches(_a			,	_a			,	_a   )

    __lowerCamelCase       :       List[str]           = patches.shape
    __lowerCamelCase       :       Tuple           = patches_shape[1]
    __lowerCamelCase       :       int           = patches_shape[2]
    __lowerCamelCase       :       Optional[int]           = patches_shape[3]

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

    # [rows * columns, 1]
    __lowerCamelCase       :       Any           = torch.arange(_a   ).reshape([rows, 1]   ).repeat(1			,	_a   ).reshape([rows * columns, 1]   )
    __lowerCamelCase       :       Optional[int]           = 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]
    __lowerCamelCase       :       Any           = row_ids.to(torch.floataa   )
    __lowerCamelCase       :       Optional[int]           = col_ids.to(torch.floataa   )

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

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

    __lowerCamelCase       :       Any           = to_numpy_array(_a   )

    return result




  def 						_lowercase     ( self      :	Dict			,	_a      :	np.ndarray			,	_a      :	Optional[Union[str, ChannelDimension]] = None			,	**_a      :	str   )					->					np.ndarray:
    if image.dtype == np.uinta:
      __lowerCamelCase       :       List[Any]           = image.astype(np.floataa   )

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

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




  def 						_lowercase     ( self      :	Any			,	_a      :	ImageInput			,	_a      :	Optional[str] = None			,	_a      :	bool = None			,	_a      :	Optional[bool] = None			,	_a      :	Optional[int] = None			,	_a      :	Optional[Dict[str, int]] = None			,	_a      :	Optional[Union[str, TensorType]] = None			,	_a      :	ChannelDimension = ChannelDimension.FIRST			,	**_a      :	List[str]			,	)					->					ImageInput:
    __lowerCamelCase       :       Dict           = do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase       :       Optional[int]           = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
    __lowerCamelCase       :       str           = patch_size if patch_size is not None else self.patch_size
    __lowerCamelCase       :       Union[str, Any]           = max_patches if max_patches is not None else self.max_patches
    __lowerCamelCase       :       List[str]           = 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 '   )

    __lowerCamelCase       :       List[str]           = 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:
      __lowerCamelCase       :       Optional[Any]           = [convert_to_rgb(_a   ) for image in images]

    # All transformations expect numpy arrays.
    __lowerCamelCase       :       str           = [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.'   )
      __lowerCamelCase       :       str           = kwargs.pop('font_bytes'			,	_a   )
      __lowerCamelCase       :       Tuple           = kwargs.pop('font_path'			,	_a   )

      if isinstance(_a			,	_a   ):
        __lowerCamelCase       :       int           = [header_text] * len(_a   )

      __lowerCamelCase       :       Optional[int]           = [
          render_header(_a			,	header_text[i]			,	font_bytes=_a			,	font_path=_a   )
          for i, image in enumerate(_a   )
      ]

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

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

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

    __lowerCamelCase       :       Union[str, Any]           = BatchFeature(
        data={'flattened_patches': images, 'attention_mask': attention_masks}			,	tensor_type=_a   )

    return encoded_outputs











'''simple docstring'''





from __future__ import annotations

import math
from collections.abc import Callable

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 100       ,)      ->      float:
  __lowerCamelCase       :       Dict           = x_start
  __lowerCamelCase       :       int           = fnc(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = 0.0

  for _ in range(_lowerCAmelCase       ):
    # Approximates curve as a sequence of linear lines and sums their length
    __lowerCamelCase       :       List[str]           = (x_end - x_start) / steps + xa
    __lowerCamelCase       :       List[Any]           = fnc(_lowerCAmelCase       )
    length += math.hypot(xa - xa       ,fxa - fxa       )

    # Increment step
    __lowerCamelCase       :       Any           = xa
    __lowerCamelCase       :       Tuple           = fxa

  return length


if __name__ == "__main__":

   def        a_     (							_lowerCAmelCase       )      ->      Dict:
     return math.sin(10 * x       )

   print('f(x) = sin(10 * x)')
   print('The length of the curve from x = -10 to x = 10 is:')
   _UpperCamelCase											=						10
   while i <= 100000:
      print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
      i *= 10






