


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


SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     logging.get_logger(__name__)


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					**A_    ):
       requires_backends(self							,					["""bs4"""]    )
       super().__init__(**A_    )


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


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Tuple							=     BeautifulSoup(A_							,					"""html.parser"""    )

       _UpperCAmelCase		:							Dict							=     []
       _UpperCAmelCase		:							Optional[int]							=     []
       _UpperCAmelCase		:							str							=     []

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

           _UpperCAmelCase		:							Optional[Any]							=     html.unescape(A_    ).strip()
           if not text_in_this_tag:
             continue

           all_doc_strings.append(A_    )

           _UpperCAmelCase,_UpperCAmelCase		:							Tuple							=     self.xpath_soup(A_    )
           stringaxtag_seq.append(A_    )
           stringaxsubs_seq.append(A_    )

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

       return all_doc_strings, stringaxtag_seq, stringaxsubs_seq


     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							str							=     """"""
       for tagname, subs in zip(A_							,					A_    ):
         xpath += F'''/{tagname}'''
         if subs != 0:
           xpath += F'''[{subs}]'''
       return xpath




     def __call__(  self							,					A_    ):
       _UpperCAmelCase		:							Optional[int]							=     False

       # Check that strings has a valid type
       if isinstance(A_							,					A_    ):
         _UpperCAmelCase		:							Optional[int]							=     True
       elif isinstance(A_							,					(list, tuple)    ):
         if len(A_    ) == 0 or isinstance(html_strings[0]							,					A_    ):
           _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(A_    )}.'''    )

       _UpperCAmelCase		:							Dict							=     bool(isinstance(A_							,					(list, tuple)    ) and (isinstance(html_strings[0]							,					A_    ))    )

       if not is_batched:
         _UpperCAmelCase		:							Any							=     [html_strings]

       # Get nodes + xpaths
       _UpperCAmelCase		:							List[str]							=     []
       _UpperCAmelCase		:							Tuple							=     []
       for html_string in html_strings:
         _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							Dict							=     self.get_three_from_single(A_    )
         nodes.append(A_    )
         _UpperCAmelCase		:							Any							=     []
         for node, tag_list, sub_list in zip(A_							,					A_							,					A_    ):
           _UpperCAmelCase		:							List[Any]							=     self.construct_xpath(A_							,					A_    )
           xpath_strings.append(A_    )
         xpaths.append(A_    )

       # return as Dict
       _UpperCAmelCase		:							List[str]							=     {"""nodes""": nodes, """xpaths""": xpaths}
       _UpperCAmelCase		:							str							=     BatchFeature(data=A_							,					tensor_type=A_    )

       return encoded_inputs









import copy
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


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''conditional_detr'''
     __SCREAMING_SNAKE_CASE     					=    ['''past_key_values''']
     __SCREAMING_SNAKE_CASE     					=    {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(  self							,					A_=True							,					A_=None							,					A_=3							,					A_=3_00							,					A_=6							,					A_=20_48							,					A_=8							,					A_=6							,					A_=20_48							,					A_=8							,					A_=0.0							,					A_=0.0							,					A_=True							,					A_="relu"							,					A_=2_56							,					A_=0.1							,					A_=0.0							,					A_=0.0							,					A_=0.0_2							,					A_=1.0							,					A_=False							,					A_="sine"							,					A_="resnet50"							,					A_=True							,					A_=False							,					A_=2							,					A_=5							,					A_=2							,					A_=1							,					A_=1							,					A_=2							,					A_=5							,					A_=2							,					A_=0.2_5							,					**A_							,					):
       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."""    )
           _UpperCAmelCase		:							int							=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
         elif isinstance(A_							,					A_    ):
           _UpperCAmelCase		:							Tuple							=     backbone_config.get("""model_type"""    )
           _UpperCAmelCase		:							Union[str, Any]							=     CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase		:							Optional[Any]							=     config_class.from_dict(A_    )

       _UpperCAmelCase		:							Tuple							=     use_timm_backbone
       _UpperCAmelCase		:							List[str]							=     backbone_config
       _UpperCAmelCase		:							Any							=     num_channels
       _UpperCAmelCase		:							Tuple							=     num_queries
       _UpperCAmelCase		:							Dict							=     d_model
       _UpperCAmelCase		:							Dict							=     encoder_ffn_dim
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							List[Any]							=     encoder_attention_heads
       _UpperCAmelCase		:							Union[str, Any]							=     decoder_ffn_dim
       _UpperCAmelCase		:							Optional[int]							=     decoder_layers
       _UpperCAmelCase		:							List[str]							=     decoder_attention_heads
       _UpperCAmelCase		:							Optional[Any]							=     dropout
       _UpperCAmelCase		:							str							=     attention_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_function
       _UpperCAmelCase		:							int							=     init_std
       _UpperCAmelCase		:							Union[str, Any]							=     init_xavier_std
       _UpperCAmelCase		:							Tuple							=     encoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     decoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							Any							=     auxiliary_loss
       _UpperCAmelCase		:							List[str]							=     position_embedding_type
       _UpperCAmelCase		:							int							=     backbone
       _UpperCAmelCase		:							Any							=     use_pretrained_backbone
       _UpperCAmelCase		:							int							=     dilation
       # Hungarian matcher
       _UpperCAmelCase		:							Dict							=     class_cost
       _UpperCAmelCase		:							Optional[Any]							=     bbox_cost
       _UpperCAmelCase		:							Tuple							=     giou_cost
       # Loss coefficients
       _UpperCAmelCase		:							Union[str, Any]							=     mask_loss_coefficient
       _UpperCAmelCase		:							int							=     dice_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     cls_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     bbox_loss_coefficient
       _UpperCAmelCase		:							int							=     giou_loss_coefficient
       _UpperCAmelCase		:							Optional[int]							=     focal_alpha
       super().__init__(is_encoder_decoder=A_							,					**A_    )


     @property
     def        __snake_case(  self    ):
       return self.encoder_attention_heads


     @property
     def        __snake_case(  self    ):
       return self.d_model




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
         _UpperCAmelCase		:							Optional[int]							=     self.backbone_config.to_dict()
       _UpperCAmelCase		:							Dict							=     self.__class__.model_type
       return output


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    version.parse('''1.11'''     )


     @property
     def        __snake_case(  self    ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]    )


     @property
     def        __snake_case(  self    ):
       return 1e-5




     @property
     def        __snake_case(  self    ):
       return 12





