




'''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   lowercase__(			):

      """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   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






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

      SCREAMING_SNAKE_CASE   :      Tuple    					=						image_tensor.unsqueeze(0    )
      SCREAMING_SNAKE_CASE   :      str    					=						torch.nn.functional.unfold(lowerCamelCase_      ,(patch_height, patch_width)      ,stride=(patch_height, patch_width)    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						patches.reshape(image_tensor.size(0    )      ,image_tensor.size(1    )      ,lowerCamelCase_      ,lowerCamelCase_      ,-1    )
      SCREAMING_SNAKE_CASE   :      Optional[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   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					str = 36      ,__UpperCamelCase:					Union[str, Any] = "black"      ,__UpperCamelCase:					Dict = "white"      ,__UpperCamelCase:					str = 5      ,__UpperCamelCase:					Optional[Any] = 5      ,__UpperCamelCase:					List[str] = 5      ,__UpperCamelCase:					Tuple = 5      ,__UpperCamelCase:					str = None      ,__UpperCamelCase:					List[Any] = None      ,):

      """simple docstring"""






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

      SCREAMING_SNAKE_CASE   :      List[str]    					=						textwrap.TextWrapper(width=80    )
      SCREAMING_SNAKE_CASE   :      str    					=						wrapper.wrap(text=lowerCamelCase_    )
      SCREAMING_SNAKE_CASE   :      int    					=						'\n'.join(lowerCamelCase_    )

      if font_bytes is not None and font_path is None:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						io.BytesIO(lowerCamelCase_    )
      elif font_path is not None:
            SCREAMING_SNAKE_CASE   :      str    					=						font_path
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						hf_hub_download(lowerCamelCase_      ,'Arial.TTF'    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						ImageFont.truetype(lowerCamelCase_      ,encoding='UTF-8'      ,size=lowerCamelCase_    )

      # Use a temporary canvas to determine the width and height in pixels when
      # rendering the text.
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						ImageDraw.Draw(Image.new('RGB'      ,(1, 1)      ,lowerCamelCase_    )    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						temp_draw.textbbox((0, 0)      ,lowerCamelCase_      ,lowerCamelCase_    )

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




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Optional[Any]      ,**__UpperCamelCase:					Optional[int]    ):

      """simple docstring"""






      requires_backends(lowerCamelCase_      ,'vision'    )

      # Convert to PIL image if necessary
      SCREAMING_SNAKE_CASE   :      str    					=						to_pil_image(lowerCamelCase_    )

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						render_text(lowerCamelCase_      ,**lowerCamelCase_    )
      SCREAMING_SNAKE_CASE   :      str    					=						max(header_image.width      ,image.width    )

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

      SCREAMING_SNAKE_CASE   :      List[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
      SCREAMING_SNAKE_CASE   :      str    					=						to_numpy_array(lowerCamelCase_    )

      if infer_channel_dimension_format(lowerCamelCase_    ) == ChannelDimension.LAST:
            SCREAMING_SNAKE_CASE   :      int    					=						to_channel_dimension_format(lowerCamelCase_      ,ChannelDimension.LAST    )

      return new_image






class 							_a				(			_a ):





       '''simple docstring'''






       A						:				List[Any]        =						["""flattened_patches"""]







       def __init__(      self,							A = True,							A = True,							A = None,							A = 2_048,							A = False,							**A,							):







             '''simple docstring'''







             super().__init__(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						patch_size if patch_size is not None else {'height': 16, 'width': 16}
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						do_normalize
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						do_convert_rgb
             SCREAMING_SNAKE_CASE   :      int    					=						max_patches
             SCREAMING_SNAKE_CASE   :      List[str]    					=						is_vqa







       def        UpperCamelCase_							(      self,							A,							A,							A,							**A  ):







             '''simple docstring'''







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

             # convert to torch
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						to_channel_dimension_format(A,							ChannelDimension.FIRST  )
             SCREAMING_SNAKE_CASE   :      int    					=						torch.from_numpy(A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						patch_size['height'], patch_size['width']
             SCREAMING_SNAKE_CASE   :      List[str]    					=						get_image_size(A  )

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

             SCREAMING_SNAKE_CASE   :      Tuple    					=						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]
             SCREAMING_SNAKE_CASE   :      Dict    					=						torch_extract_patches(A,							A,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						patches.shape
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patches_shape[1]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						patches_shape[2]
             SCREAMING_SNAKE_CASE   :      Any    					=						patches_shape[3]

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

             # [rows * columns, 1]
             SCREAMING_SNAKE_CASE   :      str    					=						torch.arange(A  ).reshape([rows, 1]  ).repeat(1,							A  ).reshape([rows * columns, 1]  )
             SCREAMING_SNAKE_CASE   :      str    					=						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]
             SCREAMING_SNAKE_CASE   :      Any    					=						row_ids.to(torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						col_ids.to(torch.floataa  )

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

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

             SCREAMING_SNAKE_CASE   :      Tuple    					=						to_numpy_array(A  )

             return result







       def        UpperCamelCase_							(      self,							A,							A = None,							**A  ):







             '''simple docstring'''







             if image.dtype == np.uinta:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						image.astype(np.floataa  )

             # take mean across the whole `image`
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.mean(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.std(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						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,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						do_normalize if do_normalize is not None else self.do_normalize
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size if patch_size is not None else self.patch_size
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max_patches if max_patches is not None else self.max_patches
             SCREAMING_SNAKE_CASE   :      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 '  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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:
                   SCREAMING_SNAKE_CASE   :      int    					=						[convert_to_rgb(A  ) for image in images]

             # All transformations expect numpy arrays.
             SCREAMING_SNAKE_CASE   :      Dict    					=						[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.'  )
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						kwargs.pop('font_bytes',							A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						kwargs.pop('font_path',							A  )

                   if isinstance(A,							A  ):
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						[header_text] * len(A  )

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

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

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

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

             SCREAMING_SNAKE_CASE   :      List[str]    					=						BatchFeature(
                 data={'flattened_patches': images, 'attention_mask': attention_masks},							tensor_type=A  )

             return encoded_outputs






from __future__ import annotations



def 							UpperCamelCase_( lowerCamelCase_					)       ->		float:
    if not nums:
        raise ValueError('List is empty'					)
    return sum(lowerCamelCase_					) / len(lowerCamelCase_					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


