

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__		      =       False


lowerCAmelCase__		      =       logging.get_logger(__name__)
lowerCAmelCase__		      =       """ybelkada/fonts"""



def     lowerCAmelCase__							(		)	->					Tuple:




				'''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__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							int					)	->					Tuple:




				'''simple docstring'''





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

				A__						=	image_tensor.unsqueeze(0					)
				A__						=	torch.nn.functional.unfold(SCREAMING_SNAKE_CASE_		,				(patch_height, patch_width)		,				stride=(patch_height, patch_width)					)
				A__						=	patches.reshape(image_tensor.size(0					)		,				image_tensor.size(1					)		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				-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__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							int = 3_6		,				SCREAMING_SNAKE_CASE_:							str = "black"		,				SCREAMING_SNAKE_CASE_:							str = "white"		,				SCREAMING_SNAKE_CASE_:							int = 5		,				SCREAMING_SNAKE_CASE_:							int = 5		,				SCREAMING_SNAKE_CASE_:							int = 5		,				SCREAMING_SNAKE_CASE_:							int = 5		,				SCREAMING_SNAKE_CASE_:							Optional[bytes] = None		,				SCREAMING_SNAKE_CASE_:							Optional[str] = None		,				)	->					Image.Image:




				'''simple docstring'''





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

				A__						=	textwrap.TextWrapper(width=8_0					)
				A__						=	wrapper.wrap(text=SCREAMING_SNAKE_CASE_					)
				A__						=	"\n".join(SCREAMING_SNAKE_CASE_					)

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

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

				# 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)		,				SCREAMING_SNAKE_CASE_					)
				A__						=	ImageDraw.Draw(SCREAMING_SNAKE_CASE_					)
				draw.text(xy=(left_padding, top_padding)		,				text=SCREAMING_SNAKE_CASE_		,				fill=SCREAMING_SNAKE_CASE_		,				font=SCREAMING_SNAKE_CASE_					)
				return image



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							np.ndarray		,				SCREAMING_SNAKE_CASE_:							str		,				**SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					Dict:




				'''simple docstring'''





				requires_backends(SCREAMING_SNAKE_CASE_		,				"vision"					)

				# Convert to PIL image if necessary
				A__						=	to_pil_image(SCREAMING_SNAKE_CASE_					)

				A__						=	render_text(SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_					)
				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(SCREAMING_SNAKE_CASE_					)

				if infer_channel_dimension_format(SCREAMING_SNAKE_CASE_					) == ChannelDimension.LAST:
								A__						=	to_channel_dimension_format(SCREAMING_SNAKE_CASE_		,				ChannelDimension.LAST					)

				return new_image





class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['flattened_patches']


			def __init__(			self					,					lowercase = True					,					lowercase = True					,					lowercase = None					,					lowercase = 2048					,					lowercase = False					,					**lowercase					,					)     ->  None:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					**lowercase				)     ->  np.ndarray:
							'''simple docstring'''






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

							# convert to torch
							A__						=	to_channel_dimension_format(lowercase					,					ChannelDimension.FIRST				)
							A__						=	torch.from_numpy(lowercase				)

							A__			, A__						=	patch_size["height"], patch_size["width"]
							A__			, A__						=	get_image_size(lowercase				)

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

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

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

							A__						=	to_numpy_array(lowercase				)

							return result


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None					,					**lowercase				)     ->  np.ndarray:
							'''simple docstring'''






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

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

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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = ChannelDimension.FIRST					,					**lowercase					,					)     ->  ImageInput:
							'''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"					,					lowercase				) is not None:
											raise ValueError("data_format is not an accepted input as the outputs are "				)

							A__						=	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:
											A__						=	[convert_to_rgb(lowercase				) for image in images]

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

											if isinstance(lowercase					,					lowercase				):
															A__						=	[header_text] * len(lowercase				)

											A__						=	[
											    render_header(lowercase					,					header_text[i]					,					font_bytes=lowercase					,					font_path=lowercase				)
											    for i, image in enumerate(lowercase				)
											]

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

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

							return encoded_outputs


'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

def 	UpperCamelCase_							(					_UpperCAmelCase  :      dict    )    ->    tuple:


       """simple docstring"""







       return (data["data"], data["target"])

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      np.ndarray    )    ->    XGBClassifier:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    XGBClassifier()
       classifier.fit(_UpperCAmelCase			,	_UpperCAmelCase    )
       return classifier

def 	UpperCamelCase_							(					)    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    load_iris()
       _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    data_handling(_UpperCAmelCase    )
       _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    train_test_split(
           _UpperCAmelCase			,	_UpperCAmelCase			,	test_size=0.2_5    )

       _UpperCAmelCase     :  Optional[Any]							      =    iris["target_names"]

       # Create an XGBoost Classifier from the training data
       _UpperCAmelCase     :  Tuple							      =    xgboost(_UpperCAmelCase			,	_UpperCAmelCase    )

       # Display the confusion matrix of the classifier with both training and test sets
       ConfusionMatrixDisplay.from_estimator(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	display_labels=_UpperCAmelCase			,	cmap="Blues"			,	normalize="true"			,	)
       plt.title("Normalized Confusion Matrix - IRIS Dataset"    )
       plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()

