
'''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:
     snake_case									=    False


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


def        UpperCAmelCase_ (  ):







 """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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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

 lowerCAmelCase__     :   int     		=	image_tensor.unsqueeze(0			)
 lowerCAmelCase__     :   Any     		=	torch.nn.functional.unfold(lowerCamelCase_      ,			(patch_height, patch_width)      ,			stride=(patch_height, patch_width)			)
 lowerCAmelCase__     :   str     		=	patches.reshape(image_tensor.size(0			)      ,			image_tensor.size(1			)      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			-1			)
 lowerCAmelCase__     :   List[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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_ = 3_6      ,			lowerCamelCase_ = "black"      ,			lowerCamelCase_ = "white"      ,			lowerCamelCase_ = 5      ,			lowerCamelCase_ = 5      ,			lowerCamelCase_ = 5      ,			lowerCamelCase_ = 5      ,			lowerCamelCase_ = None      ,			lowerCamelCase_ = None      ,			):







 """simple docstring"""


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

 lowerCAmelCase__     :   Optional[int]     		=	textwrap.TextWrapper(width=8_0			)
 lowerCAmelCase__     :   int     		=	wrapper.wrap(text=lowerCamelCase_			)
 lowerCAmelCase__     :   Tuple     		=	"\n".join(lowerCamelCase_			)

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

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

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			**lowerCamelCase_			):







 """simple docstring"""


 requires_backends(lowerCamelCase_      ,			"vision"			)

 # Convert to PIL image if necessary
 lowerCAmelCase__     :   Dict     		=	to_pil_image(lowerCamelCase_			)

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

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

 lowerCAmelCase__     :   Dict     		=	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
 lowerCAmelCase__     :   Any     		=	to_numpy_array(lowerCamelCase_			)

 if infer_channel_dimension_format(lowerCamelCase_			) == ChannelDimension.LAST:
  lowerCAmelCase__     :   Any     		=	to_channel_dimension_format(lowerCamelCase_      ,			ChannelDimension.LAST			)

 return new_image







class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Union[str, Any]	=			["""flattened_patches"""]


 def __init__(    self  : Any			,							a__  : bool = True			,							a__  : bool = True			,							a__  : Dict[str, int] = None			,							a__  : int = 2048			,							a__  : bool = False			,							**a__  : Optional[Any]			,							):
  '''simple docstring'''

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


 def    _A	(    self  : List[str]			,							a__  : np.ndarray			,							a__  : int			,							a__  : dict			,							**a__  : Any    ):
  '''simple docstring'''

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

  # convert to torch
  lowerCAmelCase__     :   List[Any]     		=	to_channel_dimension_format(a__			,							ChannelDimension.FIRST    )
  lowerCAmelCase__     :   List[Any]     		=	torch.from_numpy(a__    )

  lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	patch_size["height"], patch_size["width"]
  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	get_image_size(a__    )

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

  lowerCAmelCase__     :   Dict     		=	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]
  lowerCAmelCase__     :   str     		=	torch_extract_patches(a__			,							a__			,							a__    )

  lowerCAmelCase__     :   Tuple     		=	patches.shape
  lowerCAmelCase__     :   int     		=	patches_shape[1]
  lowerCAmelCase__     :   str     		=	patches_shape[2]
  lowerCAmelCase__     :   str     		=	patches_shape[3]

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

  # [rows * columns, 1]
  lowerCAmelCase__     :   str     		=	torch.arange(a__    ).reshape([rows, 1]    ).repeat(1			,							a__    ).reshape([rows * columns, 1]    )
  lowerCAmelCase__     :   Union[str, Any]     		=	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]
  lowerCAmelCase__     :   List[str]     		=	row_ids.to(torch.floataa    )
  lowerCAmelCase__     :   Optional[int]     		=	col_ids.to(torch.floataa    )

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

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

  lowerCAmelCase__     :   Optional[int]     		=	to_numpy_array(a__    )

  return result


 def    _A	(    self  : Optional[int]			,							a__  : np.ndarray			,							a__  : Optional[Union[str, ChannelDimension]] = None			,							**a__  : Dict    ):
  '''simple docstring'''

  if image.dtype == np.uinta:
   lowerCAmelCase__     :   Optional[Any]     		=	image.astype(np.floataa    )

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

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


 def    _A	(    self  : Any			,							a__  : ImageInput			,							a__  : Optional[str] = None			,							a__  : bool = None			,							a__  : Optional[bool] = None			,							a__  : Optional[int] = None			,							a__  : Optional[Dict[str, int]] = None			,							a__  : Optional[Union[str, TensorType]] = None			,							a__  : ChannelDimension = ChannelDimension.FIRST			,							**a__  : Dict			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	do_normalize if do_normalize is not None else self.do_normalize
  lowerCAmelCase__     :   List[str]     		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  lowerCAmelCase__     :   str     		=	patch_size if patch_size is not None else self.patch_size
  lowerCAmelCase__     :   List[Any]     		=	max_patches if max_patches is not None else self.max_patches
  lowerCAmelCase__     :   Any     		=	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 "    )

  lowerCAmelCase__     :   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:
   lowerCAmelCase__     :   Dict     		=	[convert_to_rgb(a__    ) for image in images]

  # All transformations expect numpy arrays.
  lowerCAmelCase__     :   List[Any]     		=	[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."    )
   lowerCAmelCase__     :   Optional[int]     		=	kwargs.pop("font_bytes"			,							a__    )
   lowerCAmelCase__     :   Optional[int]     		=	kwargs.pop("font_path"			,							a__    )

   if isinstance(a__			,							a__    ):
    lowerCAmelCase__     :   str     		=	[header_text] * len(a__    )

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

  if do_normalize:
   lowerCAmelCase__     :   Optional[int]     		=	[self.normalize(image=a__    ) for image in images]

  # convert to torch tensor and permute
  lowerCAmelCase__     :   int     		=	[
      self.extract_flattened_patches(image=a__			,							max_patches=a__			,							patch_size=a__    )
      for image in images
  ]

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

  lowerCAmelCase__     :   Optional[Any]     		=	BatchFeature(
      data={"flattened_patches": images, "attention_mask": attention_masks}			,							tensor_type=a__    )

  return encoded_outputs







'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 return number & 1 == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()





