






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_ :						List[Any]      =      logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     str     	=  ["""pixel_values"""]





	def __init__(		self				:					str ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					int = 32 ,  __lowerCamelCase				:					List[str]=PILImageResampling.BILINEAR ,  __lowerCamelCase				:					bool = True ,  **__lowerCamelCase				:					Optional[Any] ,  ):
		UpperCamelCase     :List[str]          =	do_resize
		UpperCamelCase     :Tuple          =	do_rescale
		UpperCamelCase     :str          =	size_divisor
		UpperCamelCase     :Any          =	resample
		super().__init__(**__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					np.ndarray ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[ChannelDimension] = None ,  **__lowerCamelCase				:					List[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Tuple          =	get_image_size(__lowerCamelCase	)
		# Rounds the height and width down to the closest multiple of size_divisor
		UpperCamelCase     :Union[str, Any]          =	height // size_divisor * size_divisor
		UpperCamelCase     :int          =	width // size_divisor * size_divisor
		UpperCamelCase     :List[str]          =	resize(__lowerCamelCase ,  (new_h, new_w) ,  resample=__lowerCamelCase ,  data_format=__lowerCamelCase ,  **__lowerCamelCase	)
		return image





	def 						_A (		self				:					str ,  __lowerCamelCase				:					np.ndarray ,  __lowerCamelCase				:					float ,  __lowerCamelCase				:					Optional[ChannelDimension] = None ,  **__lowerCamelCase				:					List[str]	):
		return rescale(image=__lowerCamelCase ,  scale=__lowerCamelCase ,  data_format=__lowerCamelCase ,  **__lowerCamelCase	)





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					Optional[Any]=None ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					Optional[Union[TensorType, str]] = None ,  __lowerCamelCase				:					ChannelDimension = ChannelDimension.FIRST ,  **__lowerCamelCase				:					Optional[int] ,  ):
		UpperCamelCase     :int          =	do_resize if do_resize is not None else self.do_resize
		UpperCamelCase     :Optional[Any]          =	do_rescale if do_rescale is not None else self.do_rescale
		UpperCamelCase     :Union[str, Any]          =	size_divisor if size_divisor is not None else self.size_divisor
		UpperCamelCase     :Optional[int]          =	resample if resample is not None else self.resample

		if do_resize and size_divisor is None:
			raise ValueError("""size_divisor is required for resizing"""	)

		UpperCamelCase     :str          =	make_list_of_images(__lowerCamelCase	)

		if not valid_images(__lowerCamelCase	):
			raise ValueError("""Invalid image(s)"""	)

		# All transformations expect numpy arrays.
		UpperCamelCase     :Dict          =	[to_numpy_array(__lowerCamelCase	) for img in images]

		if do_resize:
			UpperCamelCase     :Any          =	[self.resize(__lowerCamelCase ,  size_divisor=__lowerCamelCase ,  resample=__lowerCamelCase	) for image in images]

		if do_rescale:
			UpperCamelCase     :Dict          =	[self.rescale(__lowerCamelCase ,  scale=1 / 255	) for image in images]

		UpperCamelCase     :Tuple          =	[to_channel_dimension_format(__lowerCamelCase ,  __lowerCamelCase	) for image in images]

		UpperCamelCase     :List[str]          =	{"""pixel_values""": images}
		return BatchFeature(data=__lowerCamelCase ,  tensor_type=__lowerCamelCase	)








import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
	import bsa
	from bsa import BeautifulSoup


UpperCAmelCase_ :						Any      =      logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  **__lowerCamelCase				:					Optional[int]	):
		requires_backends(self ,  ["""bs4"""]	)
		super().__init__(**__lowerCamelCase	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Any	):
		UpperCamelCase     :Optional[int]          =	[]
		UpperCamelCase     :List[str]          =	[]
		UpperCamelCase     :Union[str, Any]          =	element if element.name else element.parent
		for parent in child.parents:  # type: bs4.element.Tag
			UpperCamelCase     :Optional[Any]          =	parent.find_all(child.name ,  recursive=__lowerCamelCase	)
			xpath_tags.append(child.name	)
			xpath_subscripts.append(
			    0 if 1 == len(__lowerCamelCase	) else next(i for i, s in enumerate(__lowerCamelCase ,  1	) if s is child	)	)
			UpperCamelCase     :Any          =	parent
		xpath_tags.reverse()
		xpath_subscripts.reverse()
		return xpath_tags, xpath_subscripts





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Tuple	):
		UpperCamelCase     :Any          =	BeautifulSoup(__lowerCamelCase ,  """html.parser"""	)

		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :Tuple          =	[]
		UpperCamelCase     :Tuple          =	[]

		for element in html_code.descendants:
			if type(__lowerCamelCase	) == bsa.element.NavigableString:
				if type(element.parent	) != bsa.element.Tag:
					continue

				UpperCamelCase     :Any          =	html.unescape(__lowerCamelCase	).strip()
				if not text_in_this_tag:
					continue

				all_doc_strings.append(__lowerCamelCase	)

				UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.xpath_soup(__lowerCamelCase	)
				stringaxtag_seq.append(__lowerCamelCase	)
				stringaxsubs_seq.append(__lowerCamelCase	)

		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
			raise ValueError("""Number of doc strings and xtags does not correspond"""	)
		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
			raise ValueError("""Number of doc strings and xsubs does not correspond"""	)

		return all_doc_strings, stringaxtag_seq, stringaxsubs_seq





	def 						_A (		self				:					int ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :Tuple          =	""""""
		for tagname, subs in zip(__lowerCamelCase ,  __lowerCamelCase	):
			xpath += F"""/{tagname}"""
			if subs != 0:
				xpath += F"""[{subs}]"""
		return xpath





	def __call__(		self				:					Any ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Any          =	False

		# Check that strings has a valid type
		if isinstance(__lowerCamelCase ,  __lowerCamelCase	):
			UpperCamelCase     :List[Any]          =	True
		elif isinstance(__lowerCamelCase ,  (list, tuple)	):
			if len(__lowerCamelCase	) == 0 or isinstance(html_strings[0] ,  __lowerCamelCase	):
				UpperCamelCase     :Any          =	True

		if not valid_strings:
			raise ValueError(
			    """HTML strings must of type `str`, `List[str]` (batch of examples), """
			    F"""but is of type {type(__lowerCamelCase	)}."""	)

		UpperCamelCase     :str          =	bool(isinstance(__lowerCamelCase ,  (list, tuple)	) and (isinstance(html_strings[0] ,  __lowerCamelCase	))	)

		if not is_batched:
			UpperCamelCase     :Any          =	[html_strings]

		# Get nodes + xpaths
		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :str          =	[]
		for html_string in html_strings:
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :int          =	self.get_three_from_single(__lowerCamelCase	)
			nodes.append(__lowerCamelCase	)
			UpperCamelCase     :int          =	[]
			for node, tag_list, sub_list in zip(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	):
				UpperCamelCase     :str          =	self.construct_xpath(__lowerCamelCase ,  __lowerCamelCase	)
				xpath_strings.append(__lowerCamelCase	)
			xpaths.append(__lowerCamelCase	)

		# return as Dict
		UpperCamelCase     :Optional[int]          =	{"""nodes""": nodes, """xpaths""": xpaths}
		UpperCamelCase     :Any          =	BatchFeature(data=__lowerCamelCase ,  tensor_type=__lowerCamelCase	)

		return encoded_inputs
