






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    :   str        =			False


_a    :   Any        =			logging.get_logger(__name__)
_a    :   int        =			"""ybelkada/fonts"""


def 				snake_case__							(							):
    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__							(							UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  Any       ):
    requires_backends(UpperCAmelCase    ,			["torch"]       )
    _check_torch_version()

    lowerCAmelCase__   :List[str]      							=				image_tensor.unsqueeze(0       )
    lowerCAmelCase__   :Optional[int]      							=				torch.nn.functional.unfold(UpperCAmelCase    ,			(patch_height, patch_width)    ,			stride=(patch_height, patch_width)       )
    lowerCAmelCase__   :Optional[Any]      							=				patches.reshape(image_tensor.size(0       )    ,			image_tensor.size(1       )    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			-1       )
    lowerCAmelCase__   :Dict      							=				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__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  int = 3_6    ,			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    ,			):
    requires_backends(UpperCAmelCase    ,			"vision"       )
    # Add new lines so that each line is no more than 80 characters.

    lowerCAmelCase__   :Union[str, Any]      							=				textwrap.TextWrapper(width=8_0       )
    lowerCAmelCase__   :str      							=				wrapper.wrap(text=UpperCAmelCase       )
    lowerCAmelCase__   :Tuple      							=				"\n".join(UpperCAmelCase       )

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

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


def 				snake_case__							(							UpperCAmelCase							:  np.ndarray    ,			UpperCAmelCase							:  str    ,			**UpperCAmelCase							:  List[Any]       ):
    requires_backends(UpperCAmelCase    ,			"vision"       )

    # Convert to PIL image if necessary
    lowerCAmelCase__   :Optional[Any]      							=				to_pil_image(UpperCAmelCase       )

    lowerCAmelCase__   :List[Any]      							=				render_text(UpperCAmelCase    ,			**UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				max(header_image.width    ,			image.width       )

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

    lowerCAmelCase__   :str      							=				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__   :str      							=				to_numpy_array(UpperCAmelCase       )

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

    return new_image







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''flattened_patches''']
      def __init__(   self    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 2_048    ,	_lowerCAmelCase = False    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				patch_size if patch_size is not None else {"height": 16, "width": 16}
          lowerCAmelCase__   :Tuple      							=				do_normalize
          lowerCAmelCase__   :Union[str, Any]      							=				do_convert_rgb
          lowerCAmelCase__   :Dict      							=				max_patches
          lowerCAmelCase__   :Optional[Any]      							=				is_vqa
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          requires_backends(self.extract_flattened_patches    ,	"torch"		)
          _check_torch_version()

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

          lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				patch_size["height"], patch_size["width"]
          lowerCAmelCase__							,lowerCAmelCase__   :int      							=				get_image_size(_lowerCAmelCase		)

          # maximize scale s.t.
          lowerCAmelCase__   :List[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		)    ,	_lowerCAmelCase		)    ,	1		)
          lowerCAmelCase__   :int      							=				max(min(math.floor(scale * image_width / patch_width		)    ,	_lowerCAmelCase		)    ,	1		)
          lowerCAmelCase__   :Dict      							=				max(num_feasible_rows * patch_height    ,	1		)
          lowerCAmelCase__   :int      							=				max(num_feasible_cols * patch_width    ,	1		)

          lowerCAmelCase__   :Optional[int]      							=				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__   :List[str]      							=				patches_shape[1]
          lowerCAmelCase__   :Tuple      							=				patches_shape[2]
          lowerCAmelCase__   :Dict      							=				patches_shape[3]

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

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

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

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

          lowerCAmelCase__   :Optional[int]      							=				to_numpy_array(_lowerCAmelCase		)

          return result
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          if image.dtype == np.uinta:
              lowerCAmelCase__   :List[Any]      							=				image.astype(np.floataa		)

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

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



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = ChannelDimension.FIRST    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				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__   :Optional[Any]      							=				patch_size if patch_size is not None else self.patch_size
          lowerCAmelCase__   :Optional[int]      							=				max_patches if max_patches is not None else self.max_patches
          lowerCAmelCase__   :Tuple      							=				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__   :Union[str, 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:
              lowerCAmelCase__   :Optional[int]      							=				[convert_to_rgb(_lowerCAmelCase		) for image in images]

          # All transformations expect numpy arrays.
          lowerCAmelCase__   :str      							=				[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__   :Tuple      							=				kwargs.pop("font_bytes"    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :int      							=				kwargs.pop("font_path"    ,	_lowerCAmelCase		)

              if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Optional[Any]      							=				[header_text] * len(_lowerCAmelCase		)

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

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

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

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

          lowerCAmelCase__   :int      							=				BatchFeature(
              data={"flattened_patches": images, "attention_mask": attention_masks}    ,	tensor_type=_lowerCAmelCase		)

          return encoded_outputs











from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info


def 				snake_case__							(							UpperCAmelCase							:  Dict       ):
    return {key.lstrip("-"       ): value for key, value in zip(unknown_args[::2]    ,			unknown_args[1::2]       )}


def 				snake_case__							(							):
    lowerCAmelCase__   :str      							=				ArgumentParser(
        "HuggingFace Datasets CLI tool"    ,			usage="datasets-cli <command> [<args>]"    ,			allow_abbrev=UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				parser.add_subparsers(help="datasets-cli command helpers"       )
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(UpperCAmelCase       )
    EnvironmentCommand.register_subcommand(UpperCAmelCase       )
    TestCommand.register_subcommand(UpperCAmelCase       )
    RunBeamCommand.register_subcommand(UpperCAmelCase       )
    DummyDataCommand.register_subcommand(UpperCAmelCase       )

    # Parse args
    lowerCAmelCase__							,lowerCAmelCase__   :int      							=				parser.parse_known_args()
    if not hasattr(UpperCAmelCase    ,			"func"       ):
        parser.print_help()
        exit(1       )
    lowerCAmelCase__   :Optional[int]      							=				parse_unknown_args(UpperCAmelCase       )

    # Run
    lowerCAmelCase__   :str      							=				args.func(UpperCAmelCase    ,			**UpperCAmelCase       )
    service.run()


if __name__ == "__main__":
 main()



