
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A     :	Tuple    = logging.get_logger(__name__)

A     :	int    = {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}







class 			__A( a			):
				snake_case_						   =    '''table-transformer'''
				snake_case_						   =    ['''past_key_values''']
				snake_case_						   =    {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}




				def __init__(     self		,     _snake_case=True		,     _snake_case=None		,     _snake_case=3		,     _snake_case=100		,     _snake_case=6		,     _snake_case=2_048		,     _snake_case=8		,     _snake_case=6		,     _snake_case=2_048		,     _snake_case=8		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case="relu"		,     _snake_case=256		,     _snake_case=0.1		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=1.0		,     _snake_case=False		,     _snake_case="sine"		,     _snake_case="resnet50"		,     _snake_case=True		,     _snake_case=False		,     _snake_case=1		,     _snake_case=5		,     _snake_case=2		,     _snake_case=1		,     _snake_case=1		,     _snake_case=5		,     _snake_case=2		,     _snake_case=0.1		,     **_snake_case		,     )    ->      List[str]:


								'''simple docstring'''




								if backbone_config is not None and use_timm_backbone:
												raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''				)

								if not use_timm_backbone:
												if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
																__a										=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']				)
												elif isinstance(_snake_case		,     _snake_case				):
																__a										=    backbone_config.get('''model_type'''				)
																__a										=    CONFIG_MAPPING[backbone_model_type]
																__a										=    config_class.from_dict(_snake_case				)
												# set timm attributes to None
												__a							,     __a							,     __a										=    None, None, None

								__a										=    use_timm_backbone
								__a										=    backbone_config
								__a										=    num_channels
								__a										=    num_queries
								__a										=    d_model
								__a										=    encoder_ffn_dim
								__a										=    encoder_layers
								__a										=    encoder_attention_heads
								__a										=    decoder_ffn_dim
								__a										=    decoder_layers
								__a										=    decoder_attention_heads
								__a										=    dropout
								__a										=    attention_dropout
								__a										=    activation_dropout
								__a										=    activation_function
								__a										=    init_std
								__a										=    init_xavier_std
								__a										=    encoder_layerdrop
								__a										=    decoder_layerdrop
								__a										=    encoder_layers
								__a										=    auxiliary_loss
								__a										=    position_embedding_type
								__a										=    backbone
								__a										=    use_pretrained_backbone
								__a										=    dilation
								# Hungarian matcher
								__a										=    class_cost
								__a										=    bbox_cost
								__a										=    giou_cost
								# Loss coefficients
								__a										=    mask_loss_coefficient
								__a										=    dice_loss_coefficient
								__a										=    bbox_loss_coefficient
								__a										=    giou_loss_coefficient
								__a										=    eos_coefficient
								super().__init__(is_encoder_decoder=_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.encoder_attention_heads




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.d_model







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								        ('''pixel_mask''', {0: '''batch'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-5




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return 12

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


A     :	str    = logging.get_logger(__name__)







class 			__A( a			):




				def __init__(     self		,     **_snake_case				)    ->      List[Any]:


								'''simple docstring'''




								requires_backends(self		,     ['''bs4''']				)
								super().__init__(**_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								__a										=    []
								__a										=    []
								__a										=    element if element.name else element.parent
								for parent in child.parents:  # type: bs4.element.Tag
												__a										=    parent.find_all(child.name		,     recursive=_snake_case				)
												xpath_tags.append(child.name				)
												xpath_subscripts.append(
												    0 if 1 == len(_snake_case				) else next(i for i, s in enumerate(_snake_case		,     1				) if s is child				)				)
												__a										=    parent
								xpath_tags.reverse()
								xpath_subscripts.reverse()
								return xpath_tags, xpath_subscripts




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    BeautifulSoup(_snake_case		,     '''html.parser'''				)

								__a										=    []
								__a										=    []
								__a										=    []

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

																__a										=    html.unescape(_snake_case				).strip()
																if not text_in_this_tag:
																				continue

																all_doc_strings.append(_snake_case				)

																__a							,     __a										=    self.xpath_soup(_snake_case				)
																stringaxtag_seq.append(_snake_case				)
																stringaxsubs_seq.append(_snake_case				)

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

								return all_doc_strings, stringaxtag_seq, stringaxsubs_seq




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    ''''''
								for tagname, subs in zip(_snake_case		,     _snake_case				):
												xpath += F"""/{tagname}"""
												if subs != 0:
																xpath += F"""[{subs}]"""
								return xpath




				def __call__(     self		,     _snake_case				)    ->      BatchFeature:


								'''simple docstring'''




								__a										=    False

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

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

								__a										=    bool(isinstance(_snake_case		,     (list, tuple)				) and (isinstance(html_strings[0]		,     _snake_case				))				)

								if not is_batched:
												__a										=    [html_strings]

								# Get nodes + xpaths
								__a										=    []
								__a										=    []
								for html_string in html_strings:
												__a							,     __a							,     __a										=    self.get_three_from_single(_snake_case				)
												nodes.append(_snake_case				)
												__a										=    []
												for node, tag_list, sub_list in zip(_snake_case		,     _snake_case		,     _snake_case				):
																__a										=    self.construct_xpath(_snake_case		,     _snake_case				)
																xpath_strings.append(_snake_case				)
												xpaths.append(_snake_case				)

								# return as Dict
								__a										=    {'''nodes''': nodes, '''xpaths''': xpaths}
								__a										=    BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)

								return encoded_inputs