

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:
			a_     =			False


a_     =			logging.get_logger(__name__)
a_     =			'ybelkada/fonts'

def        lowerCamelCase__       (    ):
	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):
	requires_backends(__lowerCAmelCase							,	["torch"])
	_check_torch_version()

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				image_tensor.unsqueeze(0)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.nn.functional.unfold(__lowerCAmelCase							,	(patch_height, patch_width)							,	stride=(patch_height, patch_width))
	SCREAMING_SNAKE_CASE      :					List[Any]    =				patches.reshape(image_tensor.size(0)							,	image_tensor.size(1)							,	__lowerCAmelCase							,	__lowerCAmelCase							,	-1)
	SCREAMING_SNAKE_CASE      :					str    =				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							,	):
	requires_backends(__lowerCAmelCase							,	"vision")
	# Add new lines so that each line is no more than 80 characters.

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				textwrap.TextWrapper(width=80)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				wrapper.wrap(text=__lowerCAmelCase)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				"\n".join(__lowerCAmelCase)

	if font_bytes is not None and font_path is None:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				io.BytesIO(__lowerCAmelCase)
	elif font_path is not None:
		SCREAMING_SNAKE_CASE      :					Dict    =				font_path
	else:
		SCREAMING_SNAKE_CASE      :					Any    =				hf_hub_download(__lowerCAmelCase							,	"Arial.TTF")
	SCREAMING_SNAKE_CASE      :					Any    =				ImageFont.truetype(__lowerCAmelCase							,	encoding="UTF-8"							,	size=__lowerCAmelCase)

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

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

def        lowerCamelCase__       (    _a							,	_a							,	**_a):
	requires_backends(__lowerCAmelCase							,	"vision")

	# Convert to PIL image if necessary
	SCREAMING_SNAKE_CASE      :					Any    =				to_pil_image(__lowerCAmelCase)

	SCREAMING_SNAKE_CASE      :					Any    =				render_text(__lowerCAmelCase							,	**__lowerCAmelCase)
	SCREAMING_SNAKE_CASE      :					List[str]    =				max(header_image.width							,	image.width)

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

	SCREAMING_SNAKE_CASE      :					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
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				to_numpy_array(__lowerCAmelCase)

	if infer_channel_dimension_format(__lowerCAmelCase) == ChannelDimension.LAST:
		SCREAMING_SNAKE_CASE      :					str    =				to_channel_dimension_format(__lowerCAmelCase							,	ChannelDimension.LAST)

	return new_image






class _UpperCamelCase							(							UpperCAmelCase__      ):
			'''simple docstring'''







			lowerCamelCase__							=["flattened_patches"]
			def __init__(	self							:       Union[str, Any]					,					a							:       bool = True					,					a							:       bool = True					,					a							:       Dict[str, int] = None					,					a							:       int = 2048					,					a							:       bool = False					,					**a							:       str					,					)							->				None:

				"""simple docstring"""

				super().__init__(**lowerCamelCase__		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				patch_size if patch_size is not None else {"height": 16, "width": 16}
				SCREAMING_SNAKE_CASE      :					List[Any]    =				do_normalize
				SCREAMING_SNAKE_CASE      :					int    =				do_convert_rgb
				SCREAMING_SNAKE_CASE      :					Tuple    =				max_patches
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				is_vqa
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       np.ndarray					,					a							:       int					,					a							:       dict					,					**a							:       str		)							->				np.ndarray:

				"""simple docstring"""

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

				# convert to torch
				SCREAMING_SNAKE_CASE      :					List[Any]    =				to_channel_dimension_format(lowerCamelCase__					,					ChannelDimension.FIRST		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.from_numpy(lowerCamelCase__		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				patch_size["height"], patch_size["width"]
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				get_image_size(lowerCamelCase__		)

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

				SCREAMING_SNAKE_CASE      :					List[str]    =				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]
				SCREAMING_SNAKE_CASE      :					int    =				torch_extract_patches(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__		)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				patches.shape
				SCREAMING_SNAKE_CASE      :					List[Any]    =				patches_shape[1]
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				patches_shape[2]
				SCREAMING_SNAKE_CASE      :					Any    =				patches_shape[3]

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

				# [rows * columns, 1]
				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.arange(lowerCamelCase__		).reshape([rows, 1]		).repeat(1					,					lowerCamelCase__		).reshape([rows * columns, 1]		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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]
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				row_ids.to(torch.floataa		)
				SCREAMING_SNAKE_CASE      :					Dict    =				col_ids.to(torch.floataa		)

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

				# [max_patches, 2 + patch_height * patch_width * image_channels]
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.nn.functional.pad(lowerCamelCase__					,					[0, 0, 0, max_patches - (rows * columns)]		).float()

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				to_numpy_array(lowerCamelCase__		)

				return result
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       np.ndarray					,					a							:       Optional[Union[str, ChannelDimension]] = None					,					**a							:       Union[str, Any]		)							->				np.ndarray:

				"""simple docstring"""

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

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

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






			def 							__UpperCamelCase       (	self							:       List[str]					,					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							:       Any					,					)							->				ImageInput:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE      :					List[Any]    =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
				SCREAMING_SNAKE_CASE      :					Dict    =				patch_size if patch_size is not None else self.patch_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				max_patches if max_patches is not None else self.max_patches
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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 "		)

				SCREAMING_SNAKE_CASE      :					str    =				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:
					SCREAMING_SNAKE_CASE      :					Dict    =				[convert_to_rgb(lowerCamelCase__		) for image in images]

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE      :					List[str]    =				[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."		)
					SCREAMING_SNAKE_CASE      :					List[str]    =				kwargs.pop("font_bytes"					,					lowerCamelCase__		)
					SCREAMING_SNAKE_CASE      :					List[Any]    =				kwargs.pop("font_path"					,					lowerCamelCase__		)

					if isinstance(lowerCamelCase__					,					lowerCamelCase__		):
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[header_text] * len(lowerCamelCase__		)

					SCREAMING_SNAKE_CASE      :					Tuple    =				[
					    render_header(lowerCamelCase__					,					header_text[i]					,					font_bytes=lowerCamelCase__					,					font_path=lowerCamelCase__		)
					    for i, image in enumerate(lowerCamelCase__		)
					]

				if do_normalize:
					SCREAMING_SNAKE_CASE      :					str    =				[self.normalize(image=lowerCamelCase__		) for image in images]

				# convert to torch tensor and permute
				SCREAMING_SNAKE_CASE      :					str    =				[
				    self.extract_flattened_patches(image=lowerCamelCase__					,					max_patches=lowerCamelCase__					,					patch_size=lowerCamelCase__		)
				    for image in images
				]

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

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				BatchFeature(
				    data={"flattened_patches": images, "attention_mask": attention_masks}					,					tensor_type=lowerCamelCase__		)

				return encoded_outputs



'''simple docstring'''


from __future__ import annotations

lowerCamelCase__					=   'Muhammad Umer Farooq'
lowerCamelCase__					=   'MIT'
lowerCamelCase__					=   '1.0.0'
lowerCamelCase__					=   'Muhammad Umer Farooq'
lowerCamelCase__					=   'contact@muhammadumerfarooq.me'
lowerCamelCase__					=   'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Any		,     lowerCamelCase__     :   str       )      ->None:



					'''simple docstring'''







					super().__init__()
					_UpperCAmelCase						:				list[str]	    =					[]
					_UpperCAmelCase						:				List[str]	    =					domain






			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   list[tuple[str, str | None]]       )      ->None:



					'''simple docstring'''







					if tag == "a":
							# Check the list of defined attributes.
							for name, value in attrs:
									# If href is defined, and not empty nor # print it.
									if name == "href" and value != "#" and value != "":
											# If not already in urls.
											if value not in self.urls:
													_UpperCAmelCase						:				Dict	    =					parse.urljoin(self.domain		,     lowerCamelCase__       )
													self.urls.append(lowerCamelCase__       )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return ".".join(get_sub_domain_name(__lowerCAmelCase      ).split("."      )[-2:]      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return parse.urlparse(__lowerCAmelCase      ).netloc






def        __lowerCAmelCase       (__lowerCAmelCase = "https://github.com"      ):
		_UpperCAmelCase						:				List[str]	    =					get_domain_name(__lowerCAmelCase      )

		# Initialize the parser
		_UpperCAmelCase						:				int	    =					Parser(__lowerCAmelCase      )

		try:
				# Open URL
				_UpperCAmelCase						:				Any	    =					requests.get(__lowerCAmelCase      )

				# pass the raw HTML to the parser to get links
				parser.feed(r.text      )

				# Get links and loop through
				_UpperCAmelCase						:				Any	    =					set()
				for link in parser.urls:
						# open URL.
						# read = requests.get(link)
						try:
								_UpperCAmelCase						:				List[str]	    =					requests.get(__lowerCAmelCase      )
								# Get the valid email.
								_UpperCAmelCase						:				Optional[int]	    =					re.findall("[a-zA-Z0-9]+@" + domain					,       read.text      )
								# If not in list then append it.
								for email in emails:
										valid_emails.add(__lowerCAmelCase      )
						except ValueError:
								pass
		except ValueError:
				raise SystemExit(1      )

		# Finally return a sorted list of email addresses with no duplicates.
		return sorted(__lowerCAmelCase      )


if __name__ == "__main__":
						lowerCamelCase__					=   emails_from_url('https://github.com')
						print(F'''{len(emails)} emails found:''')
						print('\n'.join(sorted(emails)))



