



"""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:
   lowerCAmelCase__              =							False


lowerCAmelCase__              =							logging.get_logger(__name__)
lowerCAmelCase__              =							'''ybelkada/fonts'''






def 			snake_case_    ( ):


       '''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 			snake_case_    ( A_   :						Tuple,							A_   :						List[Any],							A_   :						Union[str, Any]		):


       '''simple docstring'''





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

       _lowerCamelCase		:							Optional[Any]            =					image_tensor.unsqueeze(0		)
       _lowerCamelCase		:							Optional[Any]            =					torch.nn.functional.unfold(A_,							(patch_height, patch_width),							stride=(patch_height, patch_width)		)
       _lowerCamelCase		:							Tuple            =					patches.reshape(image_tensor.size(0		),							image_tensor.size(1		),							A_,							A_,							-1		)
       _lowerCamelCase		:							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_    ( A_   :						str,							A_   :						int = 36,							A_   :						str = "black",							A_   :						str = "white",							A_   :						int = 5,							A_   :						int = 5,							A_   :						int = 5,							A_   :						int = 5,							A_   :						Optional[bytes] = None,							A_   :						Optional[str] = None,							):


       '''simple docstring'''





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

       _lowerCamelCase		:							Optional[Any]            =					textwrap.TextWrapper(width=80		)
       _lowerCamelCase		:							int            =					wrapper.wrap(text=A_		)
       _lowerCamelCase		:							List[Any]            =					'''\n'''.join(A_		)

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

       # 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),							A_		)		)
       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							List[Any]            =					temp_draw.textbbox((0, 0),							A_,							A_		)

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






def 			snake_case_    ( A_   :						np.ndarray,							A_   :						str,							**A_   :						Optional[int]		):


       '''simple docstring'''





       requires_backends(A_,							'''vision'''		)

       # Convert to PIL image if necessary
       _lowerCamelCase		:							List[str]            =					to_pil_image(A_		)

       _lowerCamelCase		:							Union[str, Any]            =					render_text(A_,							**A_		)
       _lowerCamelCase		:							int            =					max(header_image.width,							image.width		)

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

       _lowerCamelCase		:							List[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
       _lowerCamelCase		:							Optional[int]            =					to_numpy_array(A_		)

       if infer_channel_dimension_format(A_		) == ChannelDimension.LAST:
              _lowerCamelCase		:							int            =					to_channel_dimension_format(A_,							ChannelDimension.LAST		)

       return new_image







class 			__snake_case			(      _lowercase):
  snake_case__							:  str         =		["flattened_patches"]




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






         """simple docstring"""






         super().__init__(**__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
         _lowerCamelCase		:							List[str]            =					do_normalize
         _lowerCamelCase		:							Dict            =					do_convert_rgb
         _lowerCamelCase		:							Union[str, Any]            =					max_patches
         _lowerCamelCase		:							Any            =					is_vqa




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		dict		,   **__lowerCAmelCase	:		Union[str, Any]						):






         """simple docstring"""






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

         # convert to torch
         _lowerCamelCase		:							Any            =					to_channel_dimension_format(__lowerCAmelCase		,   ChannelDimension.FIRST						)
         _lowerCamelCase		:							int            =					torch.from_numpy(__lowerCAmelCase						)

         _lowerCamelCase     ,       _lowerCamelCase		:							Union[str, Any]            =					patch_size['''height'''], patch_size['''width''']
         _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					get_image_size(__lowerCAmelCase						)

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

         _lowerCamelCase		:							Optional[Any]            =					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]
         _lowerCamelCase		:							int            =					torch_extract_patches(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Optional[int]            =					patches.shape
         _lowerCamelCase		:							int            =					patches_shape[1]
         _lowerCamelCase		:							Union[str, Any]            =					patches_shape[2]
         _lowerCamelCase		:							Optional[int]            =					patches_shape[3]

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

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

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

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

         _lowerCamelCase		:							Any            =					to_numpy_array(__lowerCAmelCase						)

         return result




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		Optional[Union[str, ChannelDimension]] = None		,   **__lowerCAmelCase	:		Dict						):






         """simple docstring"""






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

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

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




  def 		SCREAMING_SNAKE_CASE							(					self	:		str		,   __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	:		Any		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					do_normalize if do_normalize is not None else self.do_normalize
         _lowerCamelCase		:							Tuple            =					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'''		,   __lowerCAmelCase						) is not None:
                raise ValueError('''data_format is not an accepted input as the outputs are '''						)

         _lowerCamelCase		:							Tuple            =					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:
                _lowerCamelCase		:							Optional[Any]            =					[convert_to_rgb(__lowerCAmelCase						) for image in images]

         # All transformations expect numpy arrays.
         _lowerCamelCase		:							Optional[Any]            =					[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.'''						)
                _lowerCamelCase		:							str            =					kwargs.pop('''font_bytes'''		,   __lowerCAmelCase						)
                _lowerCamelCase		:							int            =					kwargs.pop('''font_path'''		,   __lowerCAmelCase						)

                if isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                       _lowerCamelCase		:							Optional[Any]            =					[header_text] * len(__lowerCAmelCase						)

                _lowerCamelCase		:							Dict            =					[
                    render_header(__lowerCAmelCase		,   header_text[i]		,   font_bytes=__lowerCAmelCase		,   font_path=__lowerCAmelCase						)
                    for i, image in enumerate(__lowerCAmelCase						)
                ]

         if do_normalize:
                _lowerCamelCase		:							Dict            =					[self.normalize(image=__lowerCAmelCase						) for image in images]

         # convert to torch tensor and permute
         _lowerCamelCase		:							Optional[int]            =					[
             self.extract_flattened_patches(image=__lowerCAmelCase		,   max_patches=__lowerCAmelCase		,   patch_size=__lowerCAmelCase						)
             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		:							str            =					BatchFeature(
             data={'''flattened_patches''': images, '''attention_mask''': attention_masks}		,   tensor_type=__lowerCAmelCase						)

         return encoded_outputs





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)





