



"""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						:		Tuple			= False


UpperCAmelCase						:		int			= logging.get_logger(__name__)
UpperCAmelCase						:		Optional[int]			= "ybelkada/fonts"


def 			_SCREAMING_SNAKE_CASE						() ->						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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


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

  lowercase_								= image_tensor.unsqueeze(0							)
  lowercase_								= torch.nn.functional.unfold(__lowerCAmelCase ,  (patch_height, patch_width) ,  stride=(patch_height, patch_width)							)
  lowercase_								= patches.reshape(image_tensor.size(0							) ,  image_tensor.size(1							) ,  __lowerCAmelCase ,  __lowerCAmelCase ,  -1							)
  lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase = 36 ,  __lowerCAmelCase = "black" ,  __lowerCAmelCase = "white" ,  __lowerCAmelCase = 5 ,  __lowerCAmelCase = 5 ,  __lowerCAmelCase = 5 ,  __lowerCAmelCase = 5 ,  __lowerCAmelCase = None ,  __lowerCAmelCase = None ,  ) ->						Image.Image:







  '''simple docstring'''


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

  lowercase_								= textwrap.TextWrapper(width=80							)
  lowercase_								= wrapper.wrap(text=__lowerCAmelCase							)
  lowercase_								= """\n""".join(__lowerCAmelCase							)

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

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

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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  **__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  requires_backends(__lowerCAmelCase ,  """vision"""							)

  # Convert to PIL image if necessary
  lowercase_								= to_pil_image(__lowerCAmelCase							)

  lowercase_								= render_text(__lowerCAmelCase ,  **__lowerCAmelCase							)
  lowercase_								= max(header_image.width ,  image.width							)

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

  lowercase_								= 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
  lowercase_								= to_numpy_array(__lowerCAmelCase							)

  if infer_channel_dimension_format(__lowerCAmelCase							) == ChannelDimension.LAST:
    lowercase_								= to_channel_dimension_format(__lowerCAmelCase ,  ChannelDimension.LAST							)

  return new_image


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   ["flattened_patches"]

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







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				dict ,  **lowerCAmelCase_			:				int):







      """simple docstring"""







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

      # convert to torch
      lowercase_								= to_channel_dimension_format(lowerCAmelCase_ ,  ChannelDimension.FIRST)
      lowercase_								= torch.from_numpy(lowerCAmelCase_)

      lowercase_					,    lowercase_								= patch_size["""height"""], patch_size["""width"""]
      lowercase_					,    lowercase_								= get_image_size(lowerCAmelCase_)

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

      lowercase_								= 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]
      lowercase_								= torch_extract_patches(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)

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

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

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

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

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

      lowercase_								= to_numpy_array(lowerCAmelCase_)

      return result

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				Tuple):







      """simple docstring"""







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

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

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




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







      """simple docstring"""







      lowercase_								= do_normalize if do_normalize is not None else self.do_normalize
      lowercase_								= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
      lowercase_								= patch_size if patch_size is not None else self.patch_size
      lowercase_								= max_patches if max_patches is not None else self.max_patches
      lowercase_								= 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 """)

      lowercase_								= 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:
        lowercase_								= [convert_to_rgb(lowerCAmelCase_) for image in images]

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

        if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_):
          lowercase_								= [header_text] * len(lowerCAmelCase_)

        lowercase_								= [
            render_header(lowerCAmelCase_ ,  header_text[i] ,  font_bytes=lowerCAmelCase_ ,  font_path=lowerCAmelCase_)
            for i, image in enumerate(lowerCAmelCase_)
        ]

      if do_normalize:
        lowercase_								= [self.normalize(image=lowerCAmelCase_) for image in images]

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

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

      lowercase_								= BatchFeature(
          data={"""flattened_patches""": images, """attention_mask""": attention_masks} ,  tensor_type=lowerCAmelCase_)

      return encoded_outputs






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase						:		List[Any]			= logging.get_logger(__name__)

UpperCAmelCase						:		Optional[Any]			= {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   "mgp-str"

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]=[3_2, 1_2_8] ,  lowerCAmelCase_			:				Union[str, Any]=4 ,  lowerCAmelCase_			:				Dict=3 ,  lowerCAmelCase_			:				List[str]=2_7 ,  lowerCAmelCase_			:				str=3_8 ,  lowerCAmelCase_			:				Optional[int]=5_0_2_5_7 ,  lowerCAmelCase_			:				Any=3_0_5_2_2 ,  lowerCAmelCase_			:				str=7_6_8 ,  lowerCAmelCase_			:				Any=1_2 ,  lowerCAmelCase_			:				Optional[Any]=1_2 ,  lowerCAmelCase_			:				Tuple=4.0 ,  lowerCAmelCase_			:				str=True ,  lowerCAmelCase_			:				List[str]=False ,  lowerCAmelCase_			:				List[Any]=1E-5 ,  lowerCAmelCase_			:				Any=0.0 ,  lowerCAmelCase_			:				int=0.0 ,  lowerCAmelCase_			:				str=0.0 ,  lowerCAmelCase_			:				Dict=False ,  lowerCAmelCase_			:				List[str]=0.02 ,  **lowerCAmelCase_			:				str ,  ):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)

      lowercase_								= image_size
      lowercase_								= patch_size
      lowercase_								= num_channels
      lowercase_								= max_token_length
      lowercase_								= num_character_labels
      lowercase_								= num_bpe_labels
      lowercase_								= num_wordpiece_labels
      lowercase_								= hidden_size
      lowercase_								= num_hidden_layers
      lowercase_								= num_attention_heads
      lowercase_								= mlp_ratio
      lowercase_								= distilled
      lowercase_								= layer_norm_eps
      lowercase_								= drop_rate
      lowercase_								= qkv_bias
      lowercase_								= attn_drop_rate
      lowercase_								= drop_path_rate
      lowercase_								= output_aa_attentions
      lowercase_								= initializer_range

