from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
      from ...onnx.config import PatchingSpec
      from ...tokenization_utils_base import PreTrainedTokenizerBase


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class   snake_case_       (		__A       ):
   __A	:		List[str]         =				"longformer"






   def __init__(      self				:     Any	,						lowercase_				:     Union[List[int], int] = 5_12	,						lowercase_				:     int = 2	,						lowercase_				:     int = 1	,						lowercase_				:     int = 0	,						lowercase_				:     int = 2	,						lowercase_				:     int = 3_05_22	,						lowercase_				:     int = 7_68	,						lowercase_				:     int = 12	,						lowercase_				:     int = 12	,						lowercase_				:     int = 30_72	,						lowercase_				:     str = "gelu"	,						lowercase_				:     float = 0.1	,						lowercase_				:     float = 0.1	,						lowercase_				:     int = 5_12	,						lowercase_				:     int = 2	,						lowercase_				:     float = 0.02	,						lowercase_				:     float = 1E-12	,						lowercase_				:     bool = False	,						**lowercase_				:     Dict	,						)					->						str:
       super().__init__(pad_token_id=lowercase_	,						**lowercase_  )

       lowercase__ :   List[Any]    					=				attention_window
       lowercase__ :   Any    					=				sep_token_id
       lowercase__ :   Dict    					=				bos_token_id
       lowercase__ :   Optional[Any]    					=				eos_token_id
       lowercase__ :   str    					=				vocab_size
       lowercase__ :   Optional[Any]    					=				hidden_size
       lowercase__ :   Optional[int]    					=				num_hidden_layers
       lowercase__ :   List[str]    					=				num_attention_heads
       lowercase__ :   Tuple    					=				hidden_act
       lowercase__ :   int    					=				intermediate_size
       lowercase__ :   str    					=				hidden_dropout_prob
       lowercase__ :   Any    					=				attention_probs_dropout_prob
       lowercase__ :   Tuple    					=				max_position_embeddings
       lowercase__ :   List[Any]    					=				type_vocab_size
       lowercase__ :   Tuple    					=				initializer_range
       lowercase__ :   List[Any]    					=				layer_norm_eps
       lowercase__ :   Dict    					=				onnx_export



class   snake_case_       (		__A       ):






   def __init__(      self				:     Optional[int]	,						lowercase_				:     "PretrainedConfig"	,						lowercase_				:     str = "default"	,						lowercase_				:     "List[PatchingSpec]" = None  )					->						int:
       super().__init__(lowercase_	,						lowercase_	,						lowercase_  )
       lowercase__ :   Any    					=				True






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           lowercase__ :   Union[str, Any]    					=				{0: "batch", 1: "choice", 2: "sequence"}
       else:
           lowercase__ :   str    					=				{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("global_attention_mask", dynamic_axis),
           ]  )






   @property
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Mapping[str, Mapping[int, str]]:
       lowercase__ :   Optional[int]    					=				super().outputs
       if self.task == "default":
           lowercase__ :   Optional[Any]    					=				{0: "batch"}
       return outputs






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						float:
       return 1E-4






   @property
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset	,						14  )







   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     "PreTrainedTokenizerBase"	,						lowercase_				:     int = -1	,						lowercase_				:     int = -1	,						lowercase_				:     bool = False	,						lowercase_				:     Optional[TensorType] = None	,						)					->						Mapping[str, Any]:
       lowercase__ :   Tuple    					=				super().generate_dummy_inputs(
           preprocessor=lowercase_	,						batch_size=lowercase_	,						seq_length=lowercase_	,						is_pair=lowercase_	,						framework=lowercase_  )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       lowercase__ :   Dict    					=				torch.zeros_like(inputs["input_ids"]  )
       # make every second token global
       lowercase__ :   str    					=				1

       return inputs





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