



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:
				snake_case    :						Optional[int]  =						False


snake_case    :						Optional[Any]  =						logging.get_logger(__name__)
snake_case    :						Optional[int]  =						'ybelkada/fonts'
def 	snake_case__       (					)		->		Any:







						"""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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Dict:







						"""simple docstring"""

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

						A__	:							List[Any]						    = image_tensor.unsqueeze(0       )
						A__	:							Union[str, Any]						    = torch.nn.functional.unfold(__lowercase							,     (patch_height, patch_width)							,     stride=(patch_height, patch_width)       )
						A__	:							Dict						    = patches.reshape(image_tensor.size(0       )							,     image_tensor.size(1       )							,     __lowercase							,     __lowercase							,     -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 	snake_case__       (					__lowercase							,     __lowercase = 3_6							,     __lowercase = "black"							,     __lowercase = "white"							,     __lowercase = 5							,     __lowercase = 5							,     __lowercase = 5							,     __lowercase = 5							,     __lowercase = None							,     __lowercase = None							,     )		->		Image.Image:







						"""simple docstring"""

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

						A__	:							Union[str, Any]						    = textwrap.TextWrapper(width=8_0       )
						A__	:							List[str]						    = wrapper.wrap(text=__lowercase       )
						A__	:							List[Any]						    = "\n".join(__lowercase       )

						if font_bytes is not None and font_path is None:
												A__	:							Any						    = io.BytesIO(__lowercase       )
						elif font_path is not None:
												A__	:							Union[str, Any]						    = font_path
						else:
												A__	:							Union[str, Any]						    = hf_hub_download(__lowercase							,     "Arial.TTF"       )
						A__	:							str						    = ImageFont.truetype(__lowercase							,     encoding="UTF-8"							,     size=__lowercase       )

						# Use a temporary canvas to determine the width and height in pixels when
						# rendering the text.
						A__	:							Any						    = ImageDraw.Draw(Image.new("RGB"							,     (1, 1)							,     __lowercase       )       )
						A__	:							List[str]						    = temp_draw.textbbox((0, 0)							,     __lowercase							,     __lowercase       )

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







						"""simple docstring"""

						requires_backends(__lowercase							,     "vision"       )

						# Convert to PIL image if necessary
						A__	:							List[Any]						    = to_pil_image(__lowercase       )

						A__	:							Union[str, Any]						    = render_text(__lowercase							,     **__lowercase       )
						A__	:							str						    = max(header_image.width							,     image.width       )

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

						A__	:							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
						A__	:							List[str]						    = to_numpy_array(__lowercase       )

						if infer_channel_dimension_format(__lowercase       ) == ChannelDimension.LAST:
												A__	:							Optional[int]						    = to_channel_dimension_format(__lowercase							,     ChannelDimension.LAST       )

						return new_image




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Union[str, Any]           =						['flattened_patches']







					def __init__(       self      :     Optional[int]			,		_A      :     bool = True			,		_A      :     bool = True			,		_A      :     Dict[str, int] = None			,		_A      :     int = 2048			,		_A      :     bool = False			,		**_A      :     Any			,		):
											super().__init__(**_A)
											A__	:							str						    = patch_size if patch_size is not None else {"height": 16, "width": 16}
											A__	:							Tuple						    = do_normalize
											A__	:							List[Any]						    = do_convert_rgb
											A__	:							Tuple						    = max_patches
											A__	:							Tuple						    = is_vqa







					def  _lowercase			(       self      :     Tuple			,		_A      :     np.ndarray			,		_A      :     int			,		_A      :     dict			,		**_A      :     List[Any]):
											requires_backends(self.extract_flattened_patches			,		"torch")
											_check_torch_version()

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

											A__	:							Any						    = patch_size["height"], patch_size["width"]
											A__	:							List[Any]						    = get_image_size(_A)

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

											A__	:							Optional[Any]						    = torch.nn.functional.interpolate(
											    image.unsqueeze(0)			,		size=(resized_height, resized_width)			,		mode="bilinear"			,		align_corners=_A			,		antialias=_A			,		).squeeze(0)

											# [1, rows, columns, patch_height * patch_width * image_channels]
											A__	:							int						    = torch_extract_patches(_A			,		_A			,		_A)

											A__	:							Dict						    = patches.shape
											A__	:							Optional[Any]						    = patches_shape[1]
											A__	:							Tuple						    = patches_shape[2]
											A__	:							Tuple						    = patches_shape[3]

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

											# [rows * columns, 1]
											A__	:							Optional[int]						    = torch.arange(_A).reshape([rows, 1]).repeat(1			,		_A).reshape([rows * columns, 1])
											A__	:							Union[str, Any]						    = torch.arange(_A).reshape([1, columns]).repeat(_A			,		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__	:							Union[str, Any]						    = row_ids.to(torch.floataa)
											A__	:							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__	:							Optional[Any]						    = torch.nn.functional.pad(_A			,		[0, 0, 0, max_patches - (rows * columns)]).float()

											A__	:							str						    = to_numpy_array(_A)

											return result







					def  _lowercase			(       self      :     Tuple			,		_A      :     np.ndarray			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     Tuple):
											if image.dtype == np.uinta:
																	A__	:							Dict						    = image.astype(np.floataa)

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

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







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     ImageInput			,		_A      :     Optional[str] = None			,		_A      :     bool = None			,		_A      :     Optional[bool] = None			,		_A      :     Optional[int] = None			,		_A      :     Optional[Dict[str, int]] = None			,		_A      :     Optional[Union[str, TensorType]] = None			,		_A      :     ChannelDimension = ChannelDimension.FIRST			,		**_A      :     List[Any]			,		):
											A__	:							List[Any]						    = do_normalize if do_normalize is not None else self.do_normalize
											A__	:							List[Any]						    = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
											A__	:							Tuple						    = patch_size if patch_size is not None else self.patch_size
											A__	:							Any						    = max_patches if max_patches is not None else self.max_patches
											A__	:							Dict						    = self.is_vqa

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

											A__	:							str						    = make_list_of_images(_A)

											if not valid_images(_A):
																	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__	:							List[str]						    = [convert_to_rgb(_A) for image in images]

											# All transformations expect numpy arrays.
											A__	:							List[str]						    = [to_numpy_array(_A) for image in images]

											if is_vqa:
																	if header_text is None:
																							raise ValueError("A header text must be provided for VQA models.")
																	A__	:							Any						    = kwargs.pop("font_bytes"			,		_A)
																	A__	:							Dict						    = kwargs.pop("font_path"			,		_A)

																	if isinstance(_A			,		_A):
																							A__	:							List[str]						    = [header_text] * len(_A)

																	A__	:							Optional[int]						    = [
																	    render_header(_A			,		header_text[i]			,		font_bytes=_A			,		font_path=_A)
																	    for i, image in enumerate(_A)
																	]

											if do_normalize:
																	A__	:							List[Any]						    = [self.normalize(image=_A) for image in images]

											# convert to torch tensor and permute
											A__	:							Tuple						    = [
											    self.extract_flattened_patches(image=_A			,		max_patches=_A			,		patch_size=_A)
											    for image in images
											]

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

											A__	:							str						    = BatchFeature(
											    data={"flattened_patches": images, "attention_mask": attention_masks}			,		tensor_type=_A)

											return encoded_outputs




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()