
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Any:
			if isinstance(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     ):
						lowerCAmelCase__      :      List[Any]         =		np.full((len(_SCREAMING_SNAKE_CASE     ), sequence_length, 2)       ,							_SCREAMING_SNAKE_CASE     )
			else:
						lowerCAmelCase__      :      List[str]         =		np.full((len(_SCREAMING_SNAKE_CASE     ), sequence_length)       ,							_SCREAMING_SNAKE_CASE     )

			for i, tensor in enumerate(_SCREAMING_SNAKE_CASE     ):
						if padding_side == "right":
									if isinstance(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     ):
												lowerCAmelCase__      :      Union[str, Any]         =		tensor[:sequence_length]
									else:
												lowerCAmelCase__      :      str         =		tensor[:sequence_length]
						else:
									if isinstance(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     ):
												lowerCAmelCase__      :      Dict         =		tensor[:sequence_length]
									else:
												lowerCAmelCase__      :      int         =		tensor[:sequence_length]

			return out_tensor.tolist()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      Any         =		ord(_SCREAMING_SNAKE_CASE     )
			if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
						return True
			lowerCAmelCase__      :      Any         =		unicodedata.category(_SCREAMING_SNAKE_CASE     )
			if cat.startswith('P'     ):
						return True
			return False







@dataclass
class      A__							(     lowerCAmelCase_	):
				lowercase			    =  42
				lowercase			    =  True
				lowercase			    =  None
				lowercase			    =  None
				lowercase			    =  -100
				lowercase			    =  'pt'






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      str   ):






							'''simple docstring'''







							import torch

							lowerCAmelCase__      :      List[str]         =		'label' if 'label' in features[0].keys() else 'labels'
							lowerCAmelCase__      :      str         =		[feature[label_name] for feature in features] if label_name in features[0].keys() else None
							lowerCAmelCase__      :      List[str]         =		self.tokenizer.pad(
							    __a    ,     padding=self.padding    ,     max_length=self.max_length    ,     pad_to_multiple_of=self.pad_to_multiple_of    ,     return_tensors='pt' if labels is None else None    ,     )

							if labels is None:
										return batch

							lowerCAmelCase__      :      Optional[Any]         =		torch.tensor(batch['entity_ids']   ).shape[1]
							lowerCAmelCase__      :      Dict         =		self.tokenizer.padding_side
							if padding_side == "right":
										lowerCAmelCase__      :      Any         =		[
										    list(__a   ) + [self.label_pad_token_id] * (sequence_length - len(__a   )) for label in labels
										]
							else:
										lowerCAmelCase__      :      str         =		[
										    [self.label_pad_token_id] * (sequence_length - len(__a   )) + list(__a   ) for label in labels
										]

							lowerCAmelCase__      :      int         =		[feature['ner_tags'] for feature in features]
							lowerCAmelCase__      :      str         =		padding_tensor(__a    ,     -1    ,     __a    ,     __a   )
							lowerCAmelCase__      :      List[Any]         =		[feature['original_entity_spans'] for feature in features]
							lowerCAmelCase__      :      Optional[Any]         =		padding_tensor(__a    ,     (-1, -1)    ,     __a    ,     __a   )
							lowerCAmelCase__      :      Optional[Any]         =		{k: torch.tensor(__a    ,     dtype=torch.intaa   ) for k, v in batch.items()}

							return batch



'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




	def __init__(   self						,				__a="sayef/fsner-bert-base-uncased"							):

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  torch.nn.Softmax(dim=1							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return self.bert(**__a							).last_hidden_state




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return token_embeddings.sum(2						,				keepdim=__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=1							):

						'''simple docstring'''





						return self.softmax(T * self.cos(__a						,				__a							)							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(__a							):
											if i == 0:
																__a			:       Optional[int]					    =  0
											else:
																__a			:       Union[str, Any]					    =  support_sizes[i - 1]

											__a			:       int					    =  S[s : s + size][start_token_masks[s : s + size]]
											__a			:       Union[str, Any]					    =  S[s : s + size][end_token_masks[s : s + size]]

											__a			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  torch.matmul(q[i]						,				s_end.T							).sum(1							).softmax(0							)

											if p_starts is not None:
																__a			:       str					    =  torch.vstack((p_starts, p_start)							)
																__a			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



