





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:
    _UpperCAmelCase       : Union[str, Any]			     =       False


_UpperCAmelCase       : Tuple			     =       logging.get_logger(__name__)
_UpperCAmelCase       : Tuple			     =       """ybelkada/fonts"""


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[int]:
    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		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:
    requires_backends(_UpperCAmelCase		,			['torch']				)
    _check_torch_version()

    lowerCamelCase__					:   Tuple      				=	image_tensor.unsqueeze(0				)
    lowerCamelCase__					:   int      				=	torch.nn.functional.unfold(_UpperCAmelCase		,			(patch_height, patch_width)		,			stride=(patch_height, patch_width)				)
    lowerCamelCase__					:   Dict      				=	patches.reshape(image_tensor.size(0				)		,			image_tensor.size(1				)		,			_UpperCAmelCase		,			_UpperCAmelCase		,			-1				)
    lowerCamelCase__					:   Tuple      				=	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		,			_UpperCAmelCase = 36		,			_UpperCAmelCase = "black"		,			_UpperCAmelCase = "white"		,			_UpperCAmelCase = 5		,			_UpperCAmelCase = 5		,			_UpperCAmelCase = 5		,			_UpperCAmelCase = 5		,			_UpperCAmelCase = None		,			_UpperCAmelCase = None		,			)    ->  Image.Image:
    requires_backends(_UpperCAmelCase		,			'vision'				)
    # Add new lines so that each line is no more than 80 characters.

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

    if font_bytes is not None and font_path is None:
        lowerCamelCase__					:   Dict      				=	io.BytesIO(_UpperCAmelCase				)
    elif font_path is not None:
        lowerCamelCase__					:   Optional[int]      				=	font_path
    else:
        lowerCamelCase__					:   Tuple      				=	hf_hub_download(_UpperCAmelCase		,			'Arial.TTF'				)
    lowerCamelCase__					:   Optional[int]      				=	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__					:   str      				=	ImageDraw.Draw(Image.new('RGB'		,			(1, 1)		,			_UpperCAmelCase				)				)
    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   str      				=	temp_draw.textbbox((0, 0)		,			_UpperCAmelCase		,			_UpperCAmelCase				)

    # 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__					:   str      				=	Image.new('RGB'		,			(image_width, image_height)		,			_UpperCAmelCase				)
    lowerCamelCase__					:   List[Any]      				=	ImageDraw.Draw(_UpperCAmelCase				)
    draw.text(xy=(left_padding, top_padding)		,			text=_UpperCAmelCase		,			fill=_UpperCAmelCase		,			font=_UpperCAmelCase				)
    return image


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			**_UpperCAmelCase				)    ->  int:
    requires_backends(_UpperCAmelCase		,			'vision'				)

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

    lowerCamelCase__					:   Any      				=	render_text(_UpperCAmelCase		,			**_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[int]      				=	max(header_image.width		,			image.width				)

    lowerCamelCase__					:   List[str]      				=	int(image.height * (new_width / image.width)				)
    lowerCamelCase__					:   List[str]      				=	int(header_image.height * (new_width / header_image.width)				)

    lowerCamelCase__					:   Union[str, 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__					:   List[str]      				=	to_numpy_array(_UpperCAmelCase				)

    if infer_channel_dimension_format(_UpperCAmelCase				) == ChannelDimension.LAST:
        lowerCamelCase__					:   List[Any]      				=	to_channel_dimension_format(_UpperCAmelCase		,			ChannelDimension.LAST				)

    return new_image


class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""flattened_patches"""]
 def __init__(   self						:   Dict    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   int = 2048    ,  UpperCAmelCase						:   bool = False    ,  **UpperCAmelCase						:   List[Any]    ,  )    ->			None:
     super().__init__(**UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	patch_size if patch_size is not None else {'height': 16, 'width': 16}
     lowerCamelCase__					:   Optional[int]      				=	do_normalize
     lowerCamelCase__					:   List[str]      				=	do_convert_rgb
     lowerCamelCase__					:   Any      				=	max_patches
     lowerCamelCase__					:   int      				=	is_vqa
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   dict    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			np.ndarray:
     requires_backends(self.extract_flattened_patches    ,  'torch'			)
     _check_torch_version()

     # convert to torch
     lowerCamelCase__					:   List[str]      				=	to_channel_dimension_format(UpperCAmelCase    ,  ChannelDimension.FIRST			)
     lowerCamelCase__					:   Any      				=	torch.from_numpy(UpperCAmelCase			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	patch_size['height'], patch_size['width']
     lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	get_image_size(UpperCAmelCase			)

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

     lowerCamelCase__					:   Union[str, Any]      				=	torch.nn.functional.interpolate(
         image.unsqueeze(0			)    ,  size=(resized_height, resized_width)    ,  mode='bilinear'    ,  align_corners=UpperCAmelCase    ,  antialias=UpperCAmelCase    ,  ).squeeze(0			)

     # [1, rows, columns, patch_height * patch_width * image_channels]
     lowerCamelCase__					:   str      				=	torch_extract_patches(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	patches.shape
     lowerCamelCase__					:   Tuple      				=	patches_shape[1]
     lowerCamelCase__					:   int      				=	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__					:   Optional[Any]      				=	torch.arange(UpperCAmelCase			).reshape([rows, 1]			).repeat(1    ,  UpperCAmelCase			).reshape([rows * columns, 1]			)
     lowerCamelCase__					:   List[str]      				=	torch.arange(UpperCAmelCase			).reshape([1, columns]			).repeat(UpperCAmelCase    ,  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__					:   str      				=	row_ids.to(torch.floataa			)
     lowerCamelCase__					:   Any      				=	col_ids.to(torch.floataa			)

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

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

     lowerCamelCase__					:   Any      				=	to_numpy_array(UpperCAmelCase			)

     return result
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   Tuple			)    ->			np.ndarray:
     if image.dtype == np.uinta:
         lowerCamelCase__					:   Union[str, Any]      				=	image.astype(np.floataa			)

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

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







 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   ImageInput    ,  UpperCAmelCase						:   Optional[str] = None    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Optional[bool] = None    ,  UpperCAmelCase						:   Optional[int] = None    ,  UpperCAmelCase						:   Optional[Dict[str, int]] = None    ,  UpperCAmelCase						:   Optional[Union[str, TensorType]] = None    ,  UpperCAmelCase						:   ChannelDimension = ChannelDimension.FIRST    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			ImageInput:
     lowerCamelCase__					:   Any      				=	do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__					:   int      				=	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__					:   Tuple      				=	max_patches if max_patches is not None else self.max_patches
     lowerCamelCase__					:   Optional[int]      				=	self.is_vqa

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

     lowerCamelCase__					:   Optional[Any]      				=	make_list_of_images(UpperCAmelCase			)

     if not valid_images(UpperCAmelCase			):
         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(UpperCAmelCase			) for image in images]

     # All transformations expect numpy arrays.
     lowerCamelCase__					:   List[str]      				=	[to_numpy_array(UpperCAmelCase			) 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'    ,  UpperCAmelCase			)
         lowerCamelCase__					:   Dict      				=	kwargs.pop('font_path'    ,  UpperCAmelCase			)

         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
             lowerCamelCase__					:   Tuple      				=	[header_text] * len(UpperCAmelCase			)

         lowerCamelCase__					:   Tuple      				=	[
             render_header(UpperCAmelCase    ,  header_text[i]    ,  font_bytes=UpperCAmelCase    ,  font_path=UpperCAmelCase			)
             for i, image in enumerate(UpperCAmelCase			)
         ]

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

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

     # create attention mask in numpy
     lowerCamelCase__					:   Optional[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=UpperCAmelCase			)

     return encoded_outputs












import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_UpperCAmelCase       : Union[str, Any]			     =       logging.get_logger(__name__)

class      lowerCAmelCase							:
 UpperCAmelCase__									=	None


@experimental
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

    return _map_with_joblib(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   int      				=	num_proc if num_proc <= len(_UpperCAmelCase				) else len(_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[int]      				=	[]  # We organize the splits ourselve (contiguous splits)
    for index in range(_UpperCAmelCase				):
        lowerCamelCase__					:   str      				=	len(_UpperCAmelCase				) // num_proc
        lowerCamelCase__					:   List[str]      				=	len(_UpperCAmelCase				) % num_proc
        lowerCamelCase__					:   Tuple      				=	div * index + min(_UpperCAmelCase		,			_UpperCAmelCase				)
        lowerCamelCase__					:   int      				=	start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)				)

    if len(_UpperCAmelCase				) != sum(len(i[1]				) for i in split_kwds				):
        raise ValueError(
            F"""Error dividing inputs iterable among processes. """
            F"""Total number of objects {len(_UpperCAmelCase				)}, """
            F"""length: {sum(len(i[1]				) for i in split_kwds				)}"""				)

    logger.info(
        F"""Spawning {num_proc} processes for {len(_UpperCAmelCase				)} objects in slices of {[len(i[1]				) for i in split_kwds]}"""				)
    lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	None, None
    if not disable_tqdm:
        lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	(RLock(),), tqdm.set_lock
    with Pool(_UpperCAmelCase		,			initargs=_UpperCAmelCase		,			initializer=_UpperCAmelCase				) as pool:
        lowerCamelCase__					:   Any      				=	pool.map(_UpperCAmelCase		,			_UpperCAmelCase				)
    logger.info(F"""Finished {num_proc} processes"""				)
    lowerCamelCase__					:   Union[str, Any]      				=	[obj for proc_res in mapped for obj in proc_res]
    logger.info(F"""Unpacked {len(_UpperCAmelCase				)} objects"""				)

    return mapped


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  int:
    # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
    # and it requires monkey-patching joblib internal classes which is subject to change
    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name		,			n_jobs=_UpperCAmelCase				):
        return joblib.Parallel()(
            joblib.delayed(_UpperCAmelCase				)((function, obj, types, None, True, None)				) for obj in iterable				)


@experimental
@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   List[str]      				=	backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        lowerCamelCase__					:   str      				=	None





