'''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:
      lowercase__  :      Dict        =					False


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



def  __lowerCamelCase       (      ):


      '''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  :  Dict							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Dict					):


      '''simple docstring'''


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

      UpperCAmelCase_            =  image_tensor.unsqueeze(0					)
      UpperCAmelCase_            =  torch.nn.functional.unfold(_lowerCAmelCase							,    (patch_height, patch_width)							,    stride=(patch_height, patch_width)					)
      UpperCAmelCase_            =  patches.reshape(image_tensor.size(0					)							,    image_tensor.size(1					)							,    _lowerCAmelCase							,    _lowerCAmelCase							,    -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  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Tuple = 36							,    _UpperCamelCase  :  List[str] = "black"							,    _UpperCamelCase  :  Union[str, Any] = "white"							,    _UpperCamelCase  :  List[Any] = 5							,    _UpperCamelCase  :  int = 5							,    _UpperCamelCase  :  List[Any] = 5							,    _UpperCamelCase  :  int = 5							,    _UpperCamelCase  :  int = None							,    _UpperCamelCase  :  Optional[Any] = None							,    ):


      '''simple docstring'''


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

      UpperCAmelCase_            =  textwrap.TextWrapper(width=80					)
      UpperCAmelCase_            =  wrapper.wrap(text=_lowerCAmelCase					)
      UpperCAmelCase_            =  '''\n'''.join(_lowerCAmelCase					)

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

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

      # 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)							,    _lowerCAmelCase					)
      UpperCAmelCase_            =  ImageDraw.Draw(_lowerCAmelCase					)
      draw.text(xy=(left_padding, top_padding)							,    text=_lowerCAmelCase							,    fill=_lowerCAmelCase							,    font=_lowerCAmelCase					)
      return image



def  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]							,    **_UpperCamelCase  :  int					):


      '''simple docstring'''


      requires_backends(_lowerCAmelCase							,    '''vision'''					)

      # Convert to PIL image if necessary
      UpperCAmelCase_            =  to_pil_image(_lowerCAmelCase					)

      UpperCAmelCase_            =  render_text(_lowerCAmelCase							,    **_lowerCAmelCase					)
      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(_lowerCAmelCase					)

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

      return new_image







class lowerCamelCase							(	__UpperCAmelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	["flattened_patches"]






  def __init__(      self			:       str  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       int = 2048  ,							UpperCAmelCase__			:       bool = False  ,							**UpperCAmelCase__			:       int  ,							)				->Any:
        super().__init__(**_lowerCamelCase  )
        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  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       dict  ,							**UpperCAmelCase__			:       Union[str, Any]  )				->Any:
        requires_backends(self.extract_flattened_patches  ,							'''torch'''  )
        _check_torch_version()

        # convert to torch
        UpperCAmelCase_            =  to_channel_dimension_format(_lowerCamelCase  ,							ChannelDimension.FIRST  )
        UpperCAmelCase_            =  torch.from_numpy(_lowerCamelCase  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  patch_size['''height'''], patch_size['''width''']
        UpperCAmelCase_      ,		UpperCAmelCase_            =  get_image_size(_lowerCamelCase  )

        # 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  )  ,							_lowerCamelCase  )  ,							1  )
        UpperCAmelCase_            =  max(min(math.floor(scale * image_width / patch_width  )  ,							_lowerCamelCase  )  ,							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=_lowerCamelCase  ,							antialias=_lowerCamelCase  ,							).squeeze(0  )

        # [1, rows, columns, patch_height * patch_width * image_channels]
        UpperCAmelCase_            =  torch_extract_patches(_lowerCamelCase  ,							_lowerCamelCase  ,							_lowerCamelCase  )

        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(_lowerCamelCase  ).reshape([rows, 1]  ).repeat(1  ,							_lowerCamelCase  ).reshape([rows * columns, 1]  )
        UpperCAmelCase_            =  torch.arange(_lowerCamelCase  ).reshape([1, columns]  ).repeat(_lowerCamelCase  ,							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(_lowerCamelCase  ,							[0, 0, 0, max_patches - (rows * columns)]  ).float()

        UpperCAmelCase_            =  to_numpy_array(_lowerCamelCase  )

        return result






  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       str  )				->Optional[int]:
        if image.dtype == np.uinta:
              UpperCAmelCase_            =  image.astype(np.floataa  )

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

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






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       Optional[str] = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[bool] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[Dict[str, int]] = None  ,							UpperCAmelCase__			:       Optional[Union[str, TensorType]] = None  ,							UpperCAmelCase__			:       ChannelDimension = ChannelDimension.FIRST  ,							**UpperCAmelCase__			:       int  ,							)				->Union[str, Any]:
        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'''  ,							_lowerCamelCase  ) is not None:
              raise ValueError('''data_format is not an accepted input as the outputs are '''  )

        UpperCAmelCase_            =  make_list_of_images(_lowerCamelCase  )

        if not valid_images(_lowerCamelCase  ):
              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(_lowerCamelCase  ) for image in images]

        # All transformations expect numpy arrays.
        UpperCAmelCase_            =  [to_numpy_array(_lowerCamelCase  ) 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'''  ,							_lowerCamelCase  )
              UpperCAmelCase_            =  kwargs.pop('''font_path'''  ,							_lowerCamelCase  )

              if isinstance(_lowerCamelCase  ,							_lowerCamelCase  ):
                    UpperCAmelCase_            =  [header_text] * len(_lowerCamelCase  )

              UpperCAmelCase_            =  [
                  render_header(_lowerCamelCase  ,							header_text[i]  ,							font_bytes=_lowerCamelCase  ,							font_path=_lowerCamelCase  )
                  for i, image in enumerate(_lowerCamelCase  )
              ]

        if do_normalize:
              UpperCAmelCase_            =  [self.normalize(image=_lowerCamelCase  ) for image in images]

        # convert to torch tensor and permute
        UpperCAmelCase_            =  [
            self.extract_flattened_patches(image=_lowerCamelCase  ,							max_patches=_lowerCamelCase  ,							patch_size=_lowerCamelCase  )
            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=_lowerCamelCase  )

        return encoded_outputs



'''simple docstring'''
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ''''''
      for word_or_phrase in separated:
            if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
                  raise Exception('''join() accepts only strings to be joined'''					)
            joined += word_or_phrase + separator
      return joined.strip(_UpperCamelCase					)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





