
'''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:
		lowerCAmelCase_   :   Dict       =					False


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







def    _lowerCamelCase  ()   ->    Tuple:







			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  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Optional[int]     )   ->    Optional[Any]:
			requires_backends(lowercase__					,  ["torch"]     )
			_check_torch_version()

			a__     = image_tensor.unsqueeze(0     )
			a__     = torch.nn.functional.unfold(lowercase__					,  (patch_height, patch_width)					,  stride=(patch_height, patch_width)     )
			a__     = patches.reshape(image_tensor.size(0     )					,  image_tensor.size(1     )					,  lowercase__					,  lowercase__					,  -1     )
			a__     = 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  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple = 36					,  __lowerCamelCase  :     Tuple = "black"					,  __lowerCamelCase  :     int = "white"					,  __lowerCamelCase  :     Optional[int] = 5					,  __lowerCamelCase  :     Dict = 5					,  __lowerCamelCase  :     Optional[int] = 5					,  __lowerCamelCase  :     Union[str, Any] = 5					,  __lowerCamelCase  :     List[str] = None					,  __lowerCamelCase  :     List[str] = None					,  )   ->    str:
			requires_backends(lowercase__					,  "vision"     )
			# Add new lines so that each line is no more than 80 characters.

			a__     = textwrap.TextWrapper(width=80     )
			a__     = wrapper.wrap(text=lowercase__     )
			a__     = "\n".join(lowercase__     )

			if font_bytes is not None and font_path is None:
						a__     = io.BytesIO(lowercase__     )
			elif font_path is not None:
						a__     = font_path
			else:
						a__     = hf_hub_download(lowercase__					,  "Arial.TTF"     )
			a__     = 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__     = ImageDraw.Draw(Image.new("RGB"					,  (1, 1)					,  lowercase__     )     )
			a__   ,				a__   ,				a__   ,				a__     = temp_draw.textbbox((0, 0)					,  lowercase__					,  lowercase__     )

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







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Dict					,  **__lowerCamelCase  :     int     )   ->    Dict:
			requires_backends(lowercase__					,  "vision"     )

			# Convert to PIL image if necessary
			a__     = to_pil_image(lowercase__     )

			a__     = render_text(lowercase__					,  **lowercase__     )
			a__     = max(header_image.width					,  image.width     )

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

			a__     = 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__     = to_numpy_array(lowercase__     )

			if infer_channel_dimension_format(lowercase__     ) == ChannelDimension.LAST:
						a__     = to_channel_dimension_format(lowercase__					,  ChannelDimension.LAST     )

			return new_image






class       UpperCamelCase__  (		__a     ):
	lowerCAmelCase__					:			int								=						["flattened_patches"]







	def __init__(				self			:   int , lowerCamelCase			:   Union[str, Any] = True , lowerCamelCase			:   Optional[int] = True , lowerCamelCase			:   List[Any] = None , lowerCamelCase			:   int = 2_0_4_8 , lowerCamelCase			:   int = False , **lowerCamelCase			:   Optional[int] , ):




				'''simple docstring'''
				super().__init__(**lowerCAmelCase_		)
				a__     = patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
				a__     = do_normalize
				a__     = do_convert_rgb
				a__     = max_patches
				a__     = is_vqa







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Union[str, Any] , **lowerCamelCase			:   Dict		):




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

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

				a__   ,				a__     = patch_size["height"], patch_size["width"]
				a__   ,				a__     = get_image_size(lowerCAmelCase_		)

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

				a__     = 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]
				a__     = torch_extract_patches(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_		)

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

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

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

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

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

				a__     = to_numpy_array(lowerCAmelCase_		)

				return result







	def 						__a   (				self			:   str , lowerCamelCase			:   Dict , lowerCamelCase			:   List[Any] = None , **lowerCamelCase			:   List[Any]		):




				'''simple docstring'''







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

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

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







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   Dict = None , lowerCamelCase			:   Tuple = None , lowerCamelCase			:   List[Any] = None , lowerCamelCase			:   Union[str, Any] = None , lowerCamelCase			:   List[Any] = None , lowerCamelCase			:   Optional[Any] = None , lowerCamelCase			:   List[str] = ChannelDimension.FIRST , **lowerCamelCase			:   Optional[Any] , ):




				'''simple docstring'''
				a__     = do_normalize if do_normalize is not None else self.do_normalize
				a__     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
				a__     = patch_size if patch_size is not None else self.patch_size
				a__     = max_patches if max_patches is not None else self.max_patches
				a__     = 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 "		)

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

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

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

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

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

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

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

				a__     = BatchFeature(
				    data={"flattened_patches": images, "attention_mask": attention_masks} , tensor_type=lowerCAmelCase_		)

				return encoded_outputs










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



