


'''simple docstring'''






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 						__UpperCamelCase      (						lowercase__						,  unittest.TestCase							):
							lowercase			:		str   		=  MgpstrTokenizer
							lowercase			:		List[Any]   		=  False
							lowercase			:		Union[str, Any]   		=  {}
							lowercase			:		Dict   		=  False





							def 		a__   (  self		:Tuple			):
									super().setUp()

									# fmt: off
									snake_case_		:							Union[str, Any]			       =						["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
									# fmt: on
									snake_case_		:							List[Any]			       =						dict(zip(_UpperCamelCase  ,range(len(_UpperCamelCase			)			)			)			)

									snake_case_		:							List[Any]			       =						os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["""vocab_file"""]			)
									with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""			) as fp:
											fp.write(json.dumps(_UpperCamelCase			) + """\n"""			)





							def 		a__   (  self		:Optional[int]  ,**_UpperCamelCase		:Optional[int]			):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Dict			):
									snake_case_		:							int			       =						"""tester"""
									snake_case_		:							str			       =						"""tester"""
									return input_text, output_text





							@unittest.skip("""MGP-STR always lower cases letters."""			)
							def 		a__   (  self		:int			):
									pass





							def 		a__   (  self		:Dict			):
									snake_case_		:							Tuple			       =						self.get_tokenizers(do_lower_case=_UpperCamelCase			)
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													snake_case_		:							List[Any]			       =						"""[SPECIAL_TOKEN]"""

													tokenizer.add_special_tokens({"""cls_token""": special_token}			)
													snake_case_		:							Dict			       =						tokenizer.encode([special_token]  ,add_special_tokens=_UpperCamelCase			)
													self.assertEqual(len(_UpperCamelCase			)  ,1			)

													snake_case_		:							Optional[Any]			       =						tokenizer.decode(_UpperCamelCase  ,skip_special_tokens=_UpperCamelCase			)
													self.assertTrue(special_token not in decoded			)





							def 		a__   (  self		:str			):
									snake_case_		:							Optional[Any]			       =						self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													snake_case_		:							Tuple			       =						self.get_input_output_texts(_UpperCamelCase			)

													snake_case_		:							Any			       =						tokenizer.tokenize(_UpperCamelCase			)
													snake_case_		:							Tuple			       =						tokenizer.convert_tokens_to_ids(_UpperCamelCase			)
													snake_case_		:							Dict			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													self.assertListEqual(_UpperCamelCase  ,_UpperCamelCase			)

													snake_case_		:							Tuple			       =						tokenizer.convert_ids_to_tokens(_UpperCamelCase			)
													self.assertNotEqual(len(_UpperCamelCase			)  ,0			)
													snake_case_		:							List[Any]			       =						tokenizer.decode(_UpperCamelCase			)
													self.assertIsInstance(_UpperCamelCase  ,_UpperCamelCase			)

													self.assertEqual(text_a.replace(""" """  ,""""""			)  ,_UpperCamelCase			)





							@unittest.skip("""MGP-STR tokenizer only handles one sequence."""			)
							def 		a__   (  self		:Optional[int]			):
									pass





							@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""			)
							def 		a__   (  self		:int			):
									pass



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

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

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

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

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

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

									return p_starts, p_ends