



'''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          =		False


__a          =		logging.get_logger(__name__)
__a          =		"ybelkada/fonts"





def 						__snake_case(				)		-> 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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      requires_backends(_lowerCAmelCase     ,   ["""torch"""]		)
      _check_torch_version()

      snake_case__  :    Any    						=      image_tensor.unsqueeze(0		)
      snake_case__  :    Union[str, Any]    						=      torch.nn.functional.unfold(_lowerCAmelCase     ,   (patch_height, patch_width)     ,   stride=(patch_height, patch_width)		)
      snake_case__  :    Dict    						=      patches.reshape(image_tensor.size(0		)     ,   image_tensor.size(1		)     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   -1		)
      snake_case__  :    str    						=      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 						__snake_case(				_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.

      snake_case__  :    List[Any]    						=      textwrap.TextWrapper(width=80		)
      snake_case__  :    Optional[int]    						=      wrapper.wrap(text=_lowerCAmelCase		)
      snake_case__  :    List[Any]    						=      """\n""".join(_lowerCAmelCase		)

      if font_bytes is not None and font_path is None:
            snake_case__  :    Union[str, Any]    						=      io.BytesIO(_lowerCAmelCase		)
      elif font_path is not None:
            snake_case__  :    List[str]    						=      font_path
      else:
            snake_case__  :    Tuple    						=      hf_hub_download(_lowerCAmelCase     ,   """Arial.TTF"""		)
      snake_case__  :    Any    						=      ImageFont.truetype(_lowerCAmelCase     ,   encoding="""UTF-8"""     ,   size=_lowerCAmelCase		)

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

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   **_lowerCAmelCase		)		-> List[str]:
      requires_backends(_lowerCAmelCase     ,   """vision"""		)

      # Convert to PIL image if necessary
      snake_case__  :    Tuple    						=      to_pil_image(_lowerCAmelCase		)

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

      snake_case__  :    Any    						=      int(image.height * (new_width / image.width)		)
      snake_case__  :    Optional[int]    						=      int(header_image.height * (new_width / header_image.width)		)

      snake_case__  :    Optional[Any]    						=      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
      snake_case__  :    Union[str, Any]    						=      to_numpy_array(_lowerCAmelCase		)

      if infer_channel_dimension_format(_lowerCAmelCase		) == ChannelDimension.LAST:
            snake_case__  :    Optional[Any]    						=      to_channel_dimension_format(_lowerCAmelCase     ,   ChannelDimension.LAST		)

      return new_image







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		["flattened_patches"]




    def __init__(    self					:     int ,     snake_case_					:     bool = True ,     snake_case_					:     bool = True ,     snake_case_					:     Dict[str, int] = None ,     snake_case_					:     int = 2_048 ,     snake_case_					:     bool = False ,     **snake_case_					:     int ,     ):
          super().__init__(**snake_case_						)
          snake_case__  :    List[str]    						=      patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
          snake_case__  :    List[str]    						=      do_normalize
          snake_case__  :    Tuple    						=      do_convert_rgb
          snake_case__  :    str    						=      max_patches
          snake_case__  :    Dict    						=      is_vqa




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     np.ndarray ,     snake_case_					:     int ,     snake_case_					:     dict ,     **snake_case_					:     List[str]						):
          requires_backends(self.extract_flattened_patches ,     """torch"""						)
          _check_torch_version()

          # convert to torch
          snake_case__  :    Tuple    						=      to_channel_dimension_format(snake_case_ ,     ChannelDimension.FIRST						)
          snake_case__  :    Optional[int]    						=      torch.from_numpy(snake_case_						)

          snake_case__						,				snake_case__  :    List[Any]    						=      patch_size["""height"""], patch_size["""width"""]
          snake_case__						,				snake_case__  :    Tuple    						=      get_image_size(snake_case_						)

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

          snake_case__  :    List[str]    						=      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]
          snake_case__  :    str    						=      torch_extract_patches(snake_case_ ,     snake_case_ ,     snake_case_						)

          snake_case__  :    Tuple    						=      patches.shape
          snake_case__  :    List[Any]    						=      patches_shape[1]
          snake_case__  :    Dict    						=      patches_shape[2]
          snake_case__  :    List[str]    						=      patches_shape[3]

          # [rows * columns, patch_height * patch_width * image_channels]
          snake_case__  :    Optional[int]    						=      patches.reshape([rows * columns, depth]						)

          # [rows * columns, 1]
          snake_case__  :    Optional[int]    						=      torch.arange(snake_case_						).reshape([rows, 1]						).repeat(1 ,     snake_case_						).reshape([rows * columns, 1]						)
          snake_case__  :    Optional[int]    						=      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]
          snake_case__  :    Optional[int]    						=      row_ids.to(torch.floataa						)
          snake_case__  :    Tuple    						=      col_ids.to(torch.floataa						)

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

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

          snake_case__  :    int    						=      to_numpy_array(snake_case_						)

          return result




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     np.ndarray ,     snake_case_					:     Optional[Union[str, ChannelDimension]] = None ,     **snake_case_					:     str						):
          if image.dtype == np.uinta:
                snake_case__  :    int    						=      image.astype(np.floataa						)

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

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




    def lowerCamelCase       (    self					:     Any ,     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 ,     ):
          snake_case__  :    Any    						=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case__  :    Dict    						=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
          snake_case__  :    str    						=      patch_size if patch_size is not None else self.patch_size
          snake_case__  :    List[Any]    						=      max_patches if max_patches is not None else self.max_patches
          snake_case__  :    int    						=      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 """						)

          snake_case__  :    Tuple    						=      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:
                snake_case__  :    Union[str, Any]    						=      [convert_to_rgb(snake_case_						) for image in images]

          # All transformations expect numpy arrays.
          snake_case__  :    Tuple    						=      [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."""						)
                snake_case__  :    Any    						=      kwargs.pop("""font_bytes""" ,     snake_case_						)
                snake_case__  :    str    						=      kwargs.pop("""font_path""" ,     snake_case_						)

                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    List[str]    						=      [header_text] * len(snake_case_						)

                snake_case__  :    int    						=      [
                    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:
                snake_case__  :    Union[str, Any]    						=      [self.normalize(image=snake_case_						) for image in images]

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

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

          snake_case__  :    List[Any]    						=      BatchFeature(
              data={"""flattened_patches""": images, """attention_mask""": attention_masks} ,     tensor_type=snake_case_						)

          return encoded_outputs





'''simple docstring'''





from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		CustomTokenizer
    pass
