




'''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_      :  Optional[Any]				     =  False


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



def  _SCREAMING_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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[Any]				):




     """simple docstring"""


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

     a_     :							Optional[Any]     						=  image_tensor.unsqueeze(0				)
     a_     :							Union[str, Any]     						=  torch.nn.functional.unfold(UpperCamelCase__ ,    (patch_height, patch_width) ,    stride=(patch_height, patch_width)				)
     a_     :							List[str]     						=  patches.reshape(image_tensor.size(0				) ,    image_tensor.size(1				) ,    UpperCamelCase__ ,    UpperCamelCase__ ,    -1				)
     a_     :							List[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  _SCREAMING_SNAKE_CASE     (      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 ,    ):




     """simple docstring"""


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

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

     if font_bytes is not None and font_path is None:
          a_     :							Optional[int]     						=  io.BytesIO(UpperCamelCase__				)
     elif font_path is not None:
          a_     :							int     						=  font_path
     else:
          a_     :							Tuple     						=  hf_hub_download(UpperCamelCase__ ,    """Arial.TTF"""				)
     a_     :							Any     						=  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_     :							List[Any]     						=  ImageDraw.Draw(Image.new("""RGB""" ,    (1, 1) ,    UpperCamelCase__				)				)
     a_						,     a_						,     a_						,     a_     :							List[Any]     						=  temp_draw.textbbox((0, 0) ,    UpperCamelCase__ ,    UpperCamelCase__				)

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					np.ndarray ,    UpperCamelCase__    :					str ,    **UpperCamelCase__    :					int				):




     """simple docstring"""


     requires_backends(UpperCamelCase__ ,    """vision"""				)

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

     a_     :							List[str]     						=  render_text(UpperCamelCase__ ,    **UpperCamelCase__				)
     a_     :							Optional[int]     						=  max(header_image.width ,    image.width				)

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

     a_     :							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
     a_     :							str     						=  to_numpy_array(UpperCamelCase__				)

     if infer_channel_dimension_format(UpperCamelCase__				) == ChannelDimension.LAST:
          a_     :							List[Any]     						=  to_channel_dimension_format(UpperCamelCase__ ,    ChannelDimension.LAST				)

     return new_image







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       List[str]   							=					['''flattened_patches''']






      def __init__( self   :  Optional[int]						,						lowercase__   :  bool = True						,						lowercase__   :  bool = True						,						lowercase__   :  Dict[str, int] = None						,						lowercase__   :  int = 2048						,						lowercase__   :  bool = False						,						**lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  np.ndarray						,						lowercase__   :  int						,						lowercase__   :  dict						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

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

           # convert to torch
           a_     :							List[Any]     						=  to_channel_dimension_format(lowercase__						,						ChannelDimension.FIRST       )
           a_     :							Tuple     						=  torch.from_numpy(lowercase__       )

           a_						,     a_     :							Optional[int]     						=  patch_size["""height"""], patch_size["""width"""]
           a_						,     a_     :							int     						=  get_image_size(lowercase__       )

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

           a_     :							Dict     						=  torch.nn.functional.interpolate(
               image.unsqueeze(0       )						,						size=(resized_height, resized_width)						,						mode="""bilinear"""						,						align_corners=lowercase__						,						antialias=lowercase__						,						).squeeze(0       )

           # [1, rows, columns, patch_height * patch_width * image_channels]
           a_     :							Tuple     						=  torch_extract_patches(lowercase__						,						lowercase__						,						lowercase__       )

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

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

           # [rows * columns, 1]
           a_     :							Dict     						=  torch.arange(lowercase__       ).reshape([rows, 1]       ).repeat(1						,						lowercase__       ).reshape([rows * columns, 1]       )
           a_     :							List[Any]     						=  torch.arange(lowercase__       ).reshape([1, columns]       ).repeat(lowercase__						,						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_     :							List[Any]     						=  row_ids.to(torch.floataa       )
           a_     :							List[Any]     						=  col_ids.to(torch.floataa       )

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

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

           a_     :							List[str]     						=  to_numpy_array(lowercase__       )

           return result






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  np.ndarray						,						lowercase__   :  Optional[Union[str, ChannelDimension]] = None						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           if image.dtype == np.uinta:
                a_     :							List[str]     						=  image.astype(np.floataa       )

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

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






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  ImageInput						,						lowercase__   :  Optional[str] = None						,						lowercase__   :  bool = None						,						lowercase__   :  Optional[bool] = None						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  Optional[Dict[str, int]] = None						,						lowercase__   :  Optional[Union[str, TensorType]] = None						,						lowercase__   :  ChannelDimension = ChannelDimension.FIRST						,						**lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

           a_     :							Dict     						=  do_normalize if do_normalize is not None else self.do_normalize
           a_     :							Optional[int]     						=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
           a_     :							str     						=  patch_size if patch_size is not None else self.patch_size
           a_     :							Union[str, Any]     						=  max_patches if max_patches is not None else self.max_patches
           a_     :							List[Any]     						=  self.is_vqa

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

           a_     :							Optional[Any]     						=  make_list_of_images(lowercase__       )

           if not valid_images(lowercase__       ):
                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_     :							Optional[int]     						=  [convert_to_rgb(lowercase__       ) for image in images]

           # All transformations expect numpy arrays.
           a_     :							List[Any]     						=  [to_numpy_array(lowercase__       ) 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"""						,						lowercase__       )
                a_     :							int     						=  kwargs.pop("""font_path"""						,						lowercase__       )

                if isinstance(lowercase__						,						lowercase__       ):
                     a_     :							List[str]     						=  [header_text] * len(lowercase__       )

                a_     :							List[str]     						=  [
                    render_header(lowercase__						,						header_text[i]						,						font_bytes=lowercase__						,						font_path=lowercase__       )
                    for i, image in enumerate(lowercase__       )
                ]

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

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

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

           a_     :							Optional[int]     						=  BatchFeature(
               data={"""flattened_patches""": images, """attention_mask""": attention_masks}						,						tensor_type=lowercase__       )

           return encoded_outputs






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_      :  Optional[Any]				     =  {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Any				     =  ['GPTSw3Tokenizer']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 lowerCAmelCase_      :  int				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
