






"""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			(		):







      """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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

      UpperCAmelCase          =	image_tensor.unsqueeze(0							)
      UpperCAmelCase          =	torch.nn.functional.unfold(_snake_case				,	(patch_height, patch_width)				,	stride=(patch_height, patch_width)							)
      UpperCAmelCase          =	patches.reshape(image_tensor.size(0							)				,	image_tensor.size(1							)				,	_snake_case				,	_snake_case				,	-1							)
      UpperCAmelCase          =	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			(		_snake_case				,	_snake_case = 36				,	_snake_case = "black"				,	_snake_case = "white"				,	_snake_case = 5				,	_snake_case = 5				,	_snake_case = 5				,	_snake_case = 5				,	_snake_case = None				,	_snake_case = None				,	):







      """simple docstring"""






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

      UpperCAmelCase          =	textwrap.TextWrapper(width=80							)
      UpperCAmelCase          =	wrapper.wrap(text=_snake_case							)
      UpperCAmelCase          =	"""\n""".join(_snake_case							)

      if font_bytes is not None and font_path is None:
            UpperCAmelCase          =	io.BytesIO(_snake_case							)
      elif font_path is not None:
            UpperCAmelCase          =	font_path
      else:
            UpperCAmelCase          =	hf_hub_download(_snake_case				,	"""Arial.TTF"""							)
      UpperCAmelCase          =	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.
      UpperCAmelCase          =	ImageDraw.Draw(Image.new("""RGB"""				,	(1, 1)				,	_snake_case							)							)
      UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	temp_draw.textbbox((0, 0)				,	_snake_case				,	_snake_case							)

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

def _a			(		_snake_case				,	_snake_case				,	**_snake_case							):







      """simple docstring"""






      requires_backends(_snake_case				,	"""vision"""							)

      # Convert to PIL image if necessary
      UpperCAmelCase          =	to_pil_image(_snake_case							)

      UpperCAmelCase          =	render_text(_snake_case				,	**_snake_case							)
      UpperCAmelCase          =	max(header_image.width				,	image.width							)

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

      UpperCAmelCase          =	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          =	to_numpy_array(_snake_case							)

      if infer_channel_dimension_format(_snake_case							) == ChannelDimension.LAST:
            UpperCAmelCase          =	to_channel_dimension_format(_snake_case				,	ChannelDimension.LAST							)

      return new_image







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ['''flattened_patches''']






    def __init__(			self     ,A = True     ,A = True     ,A = None     ,A = 2_048     ,A = False     ,**A     ,):
          super().__init__(**A )
          UpperCAmelCase          =	patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
          UpperCAmelCase          =	do_normalize
          UpperCAmelCase          =	do_convert_rgb
          UpperCAmelCase          =	max_patches
          UpperCAmelCase          =	is_vqa






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,**A ):
          requires_backends(self.extract_flattened_patches     ,"""torch""" )
          _check_torch_version()

          # convert to torch
          UpperCAmelCase          =	to_channel_dimension_format(A     ,ChannelDimension.FIRST )
          UpperCAmelCase          =	torch.from_numpy(A )

          UpperCAmelCase      , UpperCAmelCase          =	patch_size["""height"""], patch_size["""width"""]
          UpperCAmelCase      , UpperCAmelCase          =	get_image_size(A )

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

          UpperCAmelCase          =	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]
          UpperCAmelCase          =	torch_extract_patches(A     ,A     ,A )

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

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

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

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

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

          UpperCAmelCase          =	to_numpy_array(A )

          return result






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,**A ):
          if image.dtype == np.uinta:
                UpperCAmelCase          =	image.astype(np.floataa )

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

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






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,**A     ,):
          UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
          UpperCAmelCase          =	patch_size if patch_size is not None else self.patch_size
          UpperCAmelCase          =	max_patches if max_patches is not None else self.max_patches
          UpperCAmelCase          =	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 """ )

          UpperCAmelCase          =	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:
                UpperCAmelCase          =	[convert_to_rgb(A ) for image in images]

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

                if isinstance(A     ,A ):
                      UpperCAmelCase          =	[header_text] * len(A )

                UpperCAmelCase          =	[
                    render_header(A     ,header_text[i]     ,font_bytes=A     ,font_path=A )
                    for i, image in enumerate(A )
                ]

          if do_normalize:
                UpperCAmelCase          =	[self.normalize(image=A ) for image in images]

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

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

          UpperCAmelCase          =	BatchFeature(
              data={"""flattened_patches""": images, """attention_mask""": attention_masks}     ,tensor_type=A )

          return encoded_outputs











"""simple docstring"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if (voltage, current, resistance).count(0							) != 1:
            raise ValueError("""One and only one argument must be 0"""							)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""							)
      if voltage == 0:
            return {"voltage": float(current * resistance							)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



