





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:
      a__      :     Tuple         =		False


a__      :     Tuple         =		logging.get_logger(__name__)
a__      :     Union[str, Any]         =		'ybelkada/fonts'

def      UpperCAmelCase_    (    )	->				List[str]:
     '''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_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:Optional[int]				)	->				Dict:
     '''simple docstring'''


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

     A_          =       image_tensor.unsqueeze(0				)
     A_          =       torch.nn.functional.unfold(_UpperCAmelCase  ,						(patch_height, patch_width)  ,						stride=(patch_height, patch_width)				)
     A_          =       patches.reshape(image_tensor.size(0				)  ,						image_tensor.size(1				)  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						-1				)
     A_          =       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_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:int = 36  ,						_UpperCAmelCase							:str = "black"  ,						_UpperCAmelCase							:str = "white"  ,						_UpperCAmelCase							:int = 5  ,						_UpperCAmelCase							:int = 5  ,						_UpperCAmelCase							:int = 5  ,						_UpperCAmelCase							:int = 5  ,						_UpperCAmelCase							:Optional[bytes] = None  ,						_UpperCAmelCase							:Optional[str] = None  ,						)	->				Image.Image:
     '''simple docstring'''


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

     A_          =       textwrap.TextWrapper(width=80				)
     A_          =       wrapper.wrap(text=_UpperCAmelCase				)
     A_          =       '''\n'''.join(_UpperCAmelCase				)

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

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

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:np.ndarray  ,						_UpperCAmelCase							:str  ,						**_UpperCAmelCase							:str				)	->				int:
     '''simple docstring'''


     requires_backends(_UpperCAmelCase  ,						'''vision'''				)

     # Convert to PIL image if necessary
     A_          =       to_pil_image(_UpperCAmelCase				)

     A_          =       render_text(_UpperCAmelCase  ,						**_UpperCAmelCase				)
     A_          =       max(header_image.width  ,						image.width				)

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

     A_          =       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_          =       to_numpy_array(_UpperCAmelCase				)

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

     return new_image





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						str							=   ["""flattened_patches"""]


  def __init__(							self ,__snake_case = True ,__snake_case = True ,__snake_case = None ,__snake_case = 2_0_4_8 ,__snake_case = False ,**__snake_case ,):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,**__snake_case ):



       """simple docstring"""





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

       # convert to torch
       A_          =       to_channel_dimension_format(__snake_case ,ChannelDimension.FIRST )
       A_          =       torch.from_numpy(__snake_case )

       A_   ,    A_          =       patch_size['''height'''], patch_size['''width''']
       A_   ,    A_          =       get_image_size(__snake_case )

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

       A_          =       torch.nn.functional.interpolate(
           image.unsqueeze(0 ) ,size=(resized_height, resized_width) ,mode='''bilinear''' ,align_corners=__snake_case ,antialias=__snake_case ,).squeeze(0 )

       # [1, rows, columns, patch_height * patch_width * image_channels]
       A_          =       torch_extract_patches(__snake_case ,__snake_case ,__snake_case )

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

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

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

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

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

       A_          =       to_numpy_array(__snake_case )

       return result


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,**__snake_case ):



       """simple docstring"""





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

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

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




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = ChannelDimension.FIRST ,**__snake_case ,):



       """simple docstring"""





       A_          =       do_normalize if do_normalize is not None else self.do_normalize
       A_          =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
       A_          =       patch_size if patch_size is not None else self.patch_size
       A_          =       max_patches if max_patches is not None else self.max_patches
       A_          =       self.is_vqa

       if kwargs.get('''data_format''' ,__snake_case ) is not None:
            raise ValueError('''data_format is not an accepted input as the outputs are ''' )

       A_          =       make_list_of_images(__snake_case )

       if not valid_images(__snake_case ):
            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_          =       [convert_to_rgb(__snake_case ) for image in images]

       # All transformations expect numpy arrays.
       A_          =       [to_numpy_array(__snake_case ) for image in images]

       if is_vqa:
            if header_text is None:
                 raise ValueError('''A header text must be provided for VQA models.''' )
            A_          =       kwargs.pop('''font_bytes''' ,__snake_case )
            A_          =       kwargs.pop('''font_path''' ,__snake_case )

            if isinstance(__snake_case ,__snake_case ):
                 A_          =       [header_text] * len(__snake_case )

            A_          =       [
                render_header(__snake_case ,header_text[i] ,font_bytes=__snake_case ,font_path=__snake_case )
                for i, image in enumerate(__snake_case )
            ]

       if do_normalize:
            A_          =       [self.normalize(image=__snake_case ) for image in images]

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

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

       A_          =       BatchFeature(
           data={'''flattened_patches''': images, '''attention_mask''': attention_masks} ,tensor_type=__snake_case )

       return encoded_outputs










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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


a__      :     Dict         =		logging.get_logger(__name__)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Optional[int]							=   ["""pixel_values"""]


  def __init__(							self ,__snake_case = True ,__snake_case = None ,__snake_case = None ,__snake_case = PILImageResampling.BILINEAR ,__snake_case = True ,__snake_case = 1 / 2_5_5 ,__snake_case = True ,__snake_case = None ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       super().__init__(**__snake_case )
       A_          =       size if size is not None else {'''shortest_edge''': 3_8_4}
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       do_resize
       A_          =       size
       # Default value set here for backwards compatibility where the value in config is None
       A_          =       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       A_          =       resample
       A_          =       do_rescale
       A_          =       rescale_factor
       A_          =       do_normalize
       A_          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A_          =       image_std if image_std is not None else IMAGENET_STANDARD_STD


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = PILImageResampling.BICUBIC ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )
       if "shortest_edge" not in size:
            raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
       A_          =       size['''shortest_edge''']

       if shortest_edge < 3_8_4:
            # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
            A_          =       int(shortest_edge / crop_pct )
            A_          =       get_resize_output_image_size(__snake_case ,size=__snake_case ,default_to_square=__snake_case )
            A_          =       resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
            # then crop to (shortest_edge, shortest_edge)
            return center_crop(image=__snake_case ,size=(shortest_edge, shortest_edge) ,data_format=__snake_case ,**__snake_case )
       else:
            # warping (no cropping) when evaluated at 384 or larger
            return resize(
                __snake_case ,size=(shortest_edge, shortest_edge) ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       return rescale(__snake_case ,scale=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       return normalize(__snake_case ,mean=__snake_case ,std=__snake_case ,data_format=__snake_case ,**__snake_case )




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = ChannelDimension.FIRST ,**__snake_case ,):



       """simple docstring"""





       A_          =       do_resize if do_resize is not None else self.do_resize
       A_          =       crop_pct if crop_pct is not None else self.crop_pct
       A_          =       resample if resample is not None else self.resample
       A_          =       do_rescale if do_rescale is not None else self.do_rescale
       A_          =       rescale_factor if rescale_factor is not None else self.rescale_factor
       A_          =       do_normalize if do_normalize is not None else self.do_normalize
       A_          =       image_mean if image_mean is not None else self.image_mean
       A_          =       image_std if image_std is not None else self.image_std

       A_          =       size if size is not None else self.size
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       make_list_of_images(__snake_case )

       if not valid_images(__snake_case ):
            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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
            raise ValueError('''crop_pct must be specified if size < 384.''' )

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       A_          =       [to_numpy_array(__snake_case ) for image in images]

       if do_resize:
            A_          =       [self.resize(image=__snake_case ,size=__snake_case ,crop_pct=__snake_case ,resample=__snake_case ) for image in images]

       if do_rescale:
            A_          =       [self.rescale(image=__snake_case ,scale=__snake_case ) for image in images]

       if do_normalize:
            A_          =       [self.normalize(image=__snake_case ,mean=__snake_case ,std=__snake_case ) for image in images]

       A_          =       [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]

       A_          =       {'''pixel_values''': images}
       return BatchFeature(data=__snake_case ,tensor_type=__snake_case )



