




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__				:     int 		=						logging.get_logger(__name__)
lowercase__				:     List[Any] 		=						"ybelkada/fonts"


def 						lowerCamelCase__    (		):







	'''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 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	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 						lowerCamelCase__    (		_A     ,	_A = 36     ,	_A = "black"     ,	_A = "white"     ,	_A = 5     ,	_A = 5     ,	_A = 5     ,	_A = 5     ,	_A = None     ,	_A = None     ,	):







	'''simple docstring'''





	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 						lowerCamelCase__    (		_A     ,	_A     ,	**_A							):







	'''simple docstring'''





	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   (				UpperCAmelCase__			):







						'''simple docstring'''





						lowerCAmelCase_     		=   ['''flattened_patches''']






						def __init__(						self							:     Optional[Any]       ,	__lowercase							:     bool = True       ,	__lowercase							:     bool = True       ,	__lowercase							:     Dict[str, int] = None       ,	__lowercase							:     int = 20_48       ,	__lowercase							:     bool = False       ,	**__lowercase							:     Dict       ,	):






							"""simple docstring"""






							super().__init__(**__lowercase						)
							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        snake_case__						(						self							:     str       ,	__lowercase							:     np.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     dict       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






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

							# convert to torch
							snake_case_		    =     to_channel_dimension_format(__lowercase       ,	ChannelDimension.FIRST						)
							snake_case_		    =     torch.from_numpy(__lowercase						)

							snake_case_    ,      snake_case_		    =     patch_size["height"], patch_size["width"]
							snake_case_    ,      snake_case_		    =     get_image_size(__lowercase						)

							# 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						)       ,	__lowercase						)       ,	1						)
							snake_case_		    =     max(min(math.floor(scale * image_width / patch_width						)       ,	__lowercase						)       ,	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=__lowercase       ,	antialias=__lowercase       ,	).squeeze(0						)

							# [1, rows, columns, patch_height * patch_width * image_channels]
							snake_case_		    =     torch_extract_patches(__lowercase       ,	__lowercase       ,	__lowercase						)

							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(__lowercase						).reshape([rows, 1]						).repeat(1       ,	__lowercase						).reshape([rows * columns, 1]						)
							snake_case_		    =     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]
							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(__lowercase       ,	[0, 0, 0, max_patches - (rows * columns)]						).float()

							snake_case_		    =     to_numpy_array(__lowercase						)

							return result






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






							"""simple docstring"""






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

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

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






						def        snake_case__						(						self							:     Optional[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							:     str       ,	):






							"""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"       ,	__lowercase						) is not None:
								raise ValueError("data_format is not an accepted input as the outputs are "						)

							snake_case_		    =     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:
								snake_case_		    =     [convert_to_rgb(__lowercase						) for image in images]

							# All transformations expect numpy arrays.
							snake_case_		    =     [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."						)
								snake_case_		    =     kwargs.pop("font_bytes"       ,	__lowercase						)
								snake_case_		    =     kwargs.pop("font_path"       ,	__lowercase						)

								if isinstance(__lowercase       ,	__lowercase						):
									snake_case_		    =     [header_text] * len(__lowercase						)

								snake_case_		    =     [
								    render_header(__lowercase       ,	header_text[i]       ,	font_bytes=__lowercase       ,	font_path=__lowercase						)
								    for i, image in enumerate(__lowercase						)
								]

							if do_normalize:
								snake_case_		    =     [self.normalize(image=__lowercase						) for image in images]

							# convert to torch tensor and permute
							snake_case_		    =     [
							    self.extract_flattened_patches(image=__lowercase       ,	max_patches=__lowercase       ,	patch_size=__lowercase						)
							    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=__lowercase						)

							return encoded_outputs






from torch import nn


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f"Unsupported activation function: {act_fn}"							)
