


'''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 (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





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

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




     '''simple docstring'''





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

     __A	:       Optional[Any]      =							textwrap.TextWrapper(width=80							)
     __A	:       str      =							wrapper.wrap(text=SCREAMING_SNAKE_CASE							)
     __A	:       Optional[int]      =							"\n".join(SCREAMING_SNAKE_CASE							)

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

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

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


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					**SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     requires_backends(SCREAMING_SNAKE_CASE       ,					"vision"							)

     # Convert to PIL image if necessary
     __A	:       Any      =							to_pil_image(SCREAMING_SNAKE_CASE							)

     __A	:       int      =							render_text(SCREAMING_SNAKE_CASE       ,					**SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							max(header_image.width       ,					image.width							)

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

     __A	:       Tuple      =							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
     __A	:       Union[str, Any]      =							to_numpy_array(SCREAMING_SNAKE_CASE							)

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

     return new_image







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['flattened_patches']


     def __init__(   self			,     lowerCamelCase = True			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = 2048			,     lowerCamelCase = False			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       str      =							patch_size if patch_size is not None else {"height": 16, "width": 16}
          __A	:       Optional[int]      =							do_normalize
          __A	:       str      =							do_convert_rgb
          __A	:       Dict      =							max_patches
          __A	:       List[str]      =							is_vqa


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		):


          '''simple docstring'''





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

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

          __A  ,__A	:       Tuple      =							patch_size["height"], patch_size["width"]
          __A  ,__A	:       List[Any]      =							get_image_size(lowerCamelCase		)

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

          __A	:       str      =							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]
          __A	:       Any      =							torch_extract_patches(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)

          __A	:       int      =							patches.shape
          __A	:       Any      =							patches_shape[1]
          __A	:       Optional[Any]      =							patches_shape[2]
          __A	:       int      =							patches_shape[3]

          # [rows * columns, patch_height * patch_width * image_channels]
          __A	:       List[Any]      =							patches.reshape([rows * columns, depth]		)

          # [rows * columns, 1]
          __A	:       Tuple      =							torch.arange(lowerCamelCase		).reshape([rows, 1]		).repeat(1			,     lowerCamelCase		).reshape([rows * columns, 1]		)
          __A	:       Union[str, Any]      =							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]
          __A	:       Dict      =							row_ids.to(torch.floataa		)
          __A	:       Dict      =							col_ids.to(torch.floataa		)

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

          # [max_patches, 2 + patch_height * patch_width * image_channels]
          __A	:       List[str]      =							torch.nn.functional.pad(lowerCamelCase			,     [0, 0, 0, max_patches - (rows * columns)]		).float()

          __A	:       Optional[Any]      =							to_numpy_array(lowerCamelCase		)

          return result


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     **lowerCamelCase		):


          '''simple docstring'''





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

          # take mean across the whole `image`
          __A	:       Any      =							np.mean(lowerCamelCase		)
          __A	:       Optional[int]      =							np.std(lowerCamelCase		)
          __A	:       Any      =							max(lowerCamelCase			,     1.0 / math.sqrt(np.prod(image.shape		)		)		)

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


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       str      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Optional[Any]      =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
          __A	:       Any      =							patch_size if patch_size is not None else self.patch_size
          __A	:       Optional[Any]      =							max_patches if max_patches is not None else self.max_patches
          __A	:       Any      =							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 "		)

          __A	:       Any      =							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:
               __A	:       Union[str, Any]      =							[convert_to_rgb(lowerCamelCase		) for image in images]

          # All transformations expect numpy arrays.
          __A	:       Optional[int]      =							[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."		)
               __A	:       Optional[Any]      =							kwargs.pop("font_bytes"			,     lowerCamelCase		)
               __A	:       Union[str, Any]      =							kwargs.pop("font_path"			,     lowerCamelCase		)

               if isinstance(lowerCamelCase			,     lowerCamelCase		):
                    __A	:       Dict      =							[header_text] * len(lowerCamelCase		)

               __A	:       Union[str, Any]      =							[
                   render_header(lowerCamelCase			,     header_text[i]			,     font_bytes=lowerCamelCase			,     font_path=lowerCamelCase		)
                   for i, image in enumerate(lowerCamelCase		)
               ]

          if do_normalize:
               __A	:       Tuple      =							[self.normalize(image=lowerCamelCase		) for image in images]

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

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

          __A	:       Union[str, Any]      =							BatchFeature(
              data={"flattened_patches": images, "attention_mask": attention_masks}			,     tensor_type=lowerCamelCase		)

          return encoded_outputs





'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_UpperCamelCase 							=  logging.get_logger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							b.T
     __A	:       Optional[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=1							)
     __A	:       List[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=0							)
     __A	:       List[Any]      =							np.matmul(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       str      =							aa[:, None] - 2 * ab + ba[None, :]
     return d


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							x.reshape(-1       ,					3							)
     __A	:       Any      =							squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return np.argmin(SCREAMING_SNAKE_CASE       ,					axis=1							)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['pixel_values']


     def __init__(   self			,     lowerCamelCase = None			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = True			,     lowerCamelCase = True			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
          __A	:       int      =							get_size_dict(lowerCamelCase		)
          __A	:       str      =							np.array(lowerCamelCase		) if clusters is not None else None
          __A	:       List[Any]      =							do_resize
          __A	:       Dict      =							size
          __A	:       str      =							resample
          __A	:       Optional[Any]      =							do_normalize
          __A	:       Dict      =							do_color_quantize


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							get_size_dict(lowerCamelCase		)
          if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
          return resize(
              lowerCamelCase			,     size=(size["height"], size["width"])			,     resample=lowerCamelCase			,     data_format=lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       List[str]      =							rescale(image=lowerCamelCase			,     scale=1 / 127.5			,     data_format=lowerCamelCase		)
          __A	:       List[Any]      =							image - 1
          return image


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							do_resize if do_resize is not None else self.do_resize
          __A	:       int      =							size if size is not None else self.size
          __A	:       Tuple      =							get_size_dict(lowerCamelCase		)
          __A	:       Tuple      =							resample if resample is not None else self.resample
          __A	:       Union[str, Any]      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Tuple      =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
          __A	:       Any      =							clusters if clusters is not None else self.clusters
          __A	:       Dict      =							np.array(lowerCamelCase		)

          __A	:       Optional[int]      =							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."		)

          if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

          if do_color_quantize and clusters is None:
               raise ValueError("Clusters must be specified if do_color_quantize is True."		)

          # All transformations expect numpy arrays.
          __A	:       Optional[Any]      =							[to_numpy_array(lowerCamelCase		) for image in images]

          if do_resize:
               __A	:       Dict      =							[self.resize(image=lowerCamelCase			,     size=lowerCamelCase			,     resample=lowerCamelCase		) for image in images]

          if do_normalize:
               __A	:       List[Any]      =							[self.normalize(image=lowerCamelCase		) for image in images]

          if do_color_quantize:
               __A	:       Union[str, Any]      =							[to_channel_dimension_format(lowerCamelCase			,     ChannelDimension.LAST		) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __A	:       int      =							np.array(lowerCamelCase		)
               __A	:       Any      =							color_quantize(lowerCamelCase			,     lowerCamelCase		).reshape(images.shape[:-1]		)

               # flatten to (batch_size, height*width)
               __A	:       Tuple      =							images.shape[0]
               __A	:       List[Any]      =							images.reshape(lowerCamelCase			,     -1		)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __A	:       Optional[int]      =							list(lowerCamelCase		)
          else:
               __A	:       Tuple      =							[to_channel_dimension_format(lowerCamelCase			,     lowerCamelCase		) for image in images]

          __A	:       Optional[int]      =							{"input_ids": images}
          return BatchFeature(data=lowerCamelCase			,     tensor_type=lowerCamelCase		)

