
'''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:
					lowercase					:							Tuple   =      False


lowercase					:							Tuple   =      logging.get_logger(__name__)
lowercase					:							Optional[int]   =      "ybelkada/fonts"







def        SCREAMING_SNAKE_CASE__			(     ) ->			List[str]:
	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__			(     __A    ,       __A    ,       __A			) ->			str:
	requires_backends(__A    ,       ['torch']			)
	_check_torch_version()

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

	_snake_case     		=	textwrap.TextWrapper(width=80			)
	_snake_case     		=	wrapper.wrap(text=__A			)
	_snake_case     		=	'\n'.join(__A			)

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

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

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       **__A			) ->			Tuple:
	requires_backends(__A    ,       'vision'			)

	# Convert to PIL image if necessary
	_snake_case     		=	to_pil_image(__A			)

	_snake_case     		=	render_text(__A    ,       **__A			)
	_snake_case     		=	max(header_image.width    ,       image.width			)

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

	_snake_case     		=	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
	_snake_case     		=	to_numpy_array(__A			)

	if infer_channel_dimension_format(__A			) == ChannelDimension.LAST:
		_snake_case     		=	to_channel_dimension_format(__A    ,       ChannelDimension.LAST			)

	return new_image





class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= ["""flattened_patches"""]


							def __init__( self					,     lowerCAmelCase_ = True					,     lowerCAmelCase_ = True					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = 20_48					,     lowerCAmelCase_ = False					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_						)
								_snake_case     		=	patch_size if patch_size is not None else {'height': 16, 'width': 16}
								_snake_case     		=	do_normalize
								_snake_case     		=	do_convert_rgb
								_snake_case     		=	max_patches
								_snake_case     		=	is_vqa


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






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

								# convert to torch
								_snake_case     		=	to_channel_dimension_format(lowerCAmelCase_					,     ChannelDimension.FIRST						)
								_snake_case     		=	torch.from_numpy(lowerCAmelCase_						)

								_snake_case		,				_snake_case     		=	patch_size['height'], patch_size['width']
								_snake_case		,				_snake_case     		=	get_image_size(lowerCAmelCase_						)

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

								_snake_case     		=	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]
								_snake_case     		=	torch_extract_patches(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						)

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

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

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

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

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

								_snake_case     		=	to_numpy_array(lowerCAmelCase_						)

								return result


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None					,     **lowerCAmelCase_						):





								"""simple docstring"""






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

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

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




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = ChannelDimension.FIRST					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	do_normalize if do_normalize is not None else self.do_normalize
								_snake_case     		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
								_snake_case     		=	patch_size if patch_size is not None else self.patch_size
								_snake_case     		=	max_patches if max_patches is not None else self.max_patches
								_snake_case     		=	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 '						)

								_snake_case     		=	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:
									_snake_case     		=	[convert_to_rgb(lowerCAmelCase_						) for image in images]

								# All transformations expect numpy arrays.
								_snake_case     		=	[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.'						)
									_snake_case     		=	kwargs.pop('font_bytes'					,     lowerCAmelCase_						)
									_snake_case     		=	kwargs.pop('font_path'					,     lowerCAmelCase_						)

									if isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
										_snake_case     		=	[header_text] * len(lowerCAmelCase_						)

									_snake_case     		=	[
									    render_header(lowerCAmelCase_					,     header_text[i]					,     font_bytes=lowerCAmelCase_					,     font_path=lowerCAmelCase_						)
									    for i, image in enumerate(lowerCAmelCase_						)
									]

								if do_normalize:
									_snake_case     		=	[self.normalize(image=lowerCAmelCase_						) for image in images]

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

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

								_snake_case     		=	BatchFeature(
								    data={'flattened_patches': images, 'attention_mask': attention_masks}					,     tensor_type=lowerCAmelCase_						)

								return encoded_outputs



'''simple docstring'''







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=sys.maxsize						):





								"""simple docstring"""






								_snake_case     		=	'bilinear'
								_snake_case     		=	max_size
								_snake_case     		=	short_edge_length




							def __call__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	[]
								for img in imgs:
									_snake_case		,				_snake_case     		=	img.shape[:2]
									# later: provide list and randomly choose index for resize
									_snake_case     		=	np.random.randint(self.short_edge_length[0]					,     self.short_edge_length[1] + 1						)
									if size == 0:
										return img
									_snake_case     		=	size * 1.0 / min(lowerCAmelCase_					,     lowerCAmelCase_						)
									if h < w:
										_snake_case		,				_snake_case     		=	size, scale * w
									else:
										_snake_case		,				_snake_case     		=	scale * h, size
									if max(lowerCAmelCase_					,     lowerCAmelCase_						) > self.max_size:
										_snake_case     		=	self.max_size * 1.0 / max(lowerCAmelCase_					,     lowerCAmelCase_						)
										_snake_case     		=	newh * scale
										_snake_case     		=	neww * scale
									_snake_case     		=	int(neww + 0.5						)
									_snake_case     		=	int(newh + 0.5						)

									if img.dtype == np.uinta:
										_snake_case     		=	Image.fromarray(lowerCAmelCase_						)
										_snake_case     		=	pil_image.resize((neww, newh)					,     PILImageResampling.BILINEAR						)
										_snake_case     		=	np.asarray(lowerCAmelCase_						)
									else:
										_snake_case     		=	img.permute(2					,     0					,     1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
										_snake_case     		=	nn.functional.interpolate(
										    lowerCAmelCase_					,     (newh, neww)					,     mode=self.interp_method					,     align_corners=lowerCAmelCase_						).squeeze(0						)
									img_augs.append(lowerCAmelCase_						)

								return img_augs


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					,     cfg.INPUT.MAX_SIZE_TEST						)
								_snake_case     		=	cfg.INPUT.FORMAT
								_snake_case     		=	cfg.SIZE_DIVISIBILITY
								_snake_case     		=	cfg.PAD_VALUE
								_snake_case     		=	cfg.INPUT.MAX_SIZE_TEST
								_snake_case     		=	cfg.MODEL.DEVICE
								_snake_case     		=	torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)					,     1					,     1						)
								_snake_case     		=	torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)					,     1					,     1						)
								_snake_case     		=	lambda lowerCAmelCase_						: (x - self.pixel_mean) / self.pixel_std


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	tuple(max(lowerCAmelCase_						) for s in zip(*[img.shape for img in images]						)						)
								_snake_case     		=	[im.shape[-2:] for im in images]
								_snake_case     		=	[
								    nn.functional.pad(
								        lowerCAmelCase_					,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					,     value=self.pad_value					,     )
								    for size, im in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
								]

								return torch.stack(lowerCAmelCase_						), torch.tensor(lowerCAmelCase_						)




							def __call__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								with torch.no_grad():
									if not isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
										_snake_case     		=	[images]
									if single_image:
										assert len(lowerCAmelCase_						) == 1
									for i in range(len(lowerCAmelCase_						)						):
										if isinstance(images[i]					,     torch.Tensor						):
											images.insert(lowerCAmelCase_					,     images.pop(lowerCAmelCase_						).to(self.device						).float()						)
										elif not isinstance(images[i]					,     torch.Tensor						):
											images.insert(
											    lowerCAmelCase_					,     torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_						)					,     input_format=self.input_format						)						)
											    .to(self.device						)
											    .float()					,     )
            # resize smallest edge
									_snake_case     		=	torch.tensor([im.shape[:2] for im in images]						)
									_snake_case     		=	self.aug(lowerCAmelCase_						)
									# transpose images and convert to torch tensors
									# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
									# now normalize before pad to avoid useless arithmetic
									_snake_case     		=	[self.normalizer(lowerCAmelCase_						) for x in images]
									# now pad them to do the following operations
									_snake_case		,				_snake_case     		=	self.pad(lowerCAmelCase_						)
									# Normalize

									if self.size_divisibility > 0:
										raise NotImplementedError()
									# pad
									_snake_case     		=	torch.true_divide(lowerCAmelCase_					,     lowerCAmelCase_						)
									if single_image:
										return images[0], sizes[0], scales_yx[0]
									else:
										return images, sizes, scales_yx







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	assert torch.isfinite(__A			).all(), "Box tensor contains infinite or NaN!"
	_snake_case		,				_snake_case     		=	box_size
	tensor[:, 0].clamp_(min=0    ,       max=__A			)
	tensor[:, 1].clamp_(min=0    ,       max=__A			)
	tensor[:, 2].clamp_(min=0    ,       max=__A			)
	tensor[:, 3].clamp_(min=0    ,       max=__A			)

