



import torch

from transformers import AutoModel


class 	__snake_case   ( torch.nn.Module ):
		def __init__(							self    ,a_="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""
						super(SCREAMING_SNAKE_CASE__    ,self						).__init__()

						lowerCAmelCase__		  =  AutoModel.from_pretrained(SCREAMING_SNAKE_CASE__    ,return_dict=SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  torch.nn.CosineSimilarity(3    ,1e-0_8						)
						lowerCAmelCase__		  =  torch.nn.Softmax(dim=1						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return self.bert(**SCREAMING_SNAKE_CASE__						).last_hidden_state
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						return token_embeddings.sum(2    ,keepdim=SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_=1						):







						"""simple docstring"""
						return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  W_supports['sizes'].tolist()
						lowerCAmelCase__		  =  W_supports['start_token_id'].item()
						lowerCAmelCase__		  =  W_supports['end_token_id'].item()

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

						lowerCAmelCase__		  =  self.BERT(**SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  self.BERT(**SCREAMING_SNAKE_CASE__						)

						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  None

						lowerCAmelCase__		  =  W_supports['input_ids'] == start_token_id
						lowerCAmelCase__		  =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(SCREAMING_SNAKE_CASE__						):
										if i == 0:
														lowerCAmelCase__		  =  0
										else:
														lowerCAmelCase__		  =  support_sizes[i - 1]

										lowerCAmelCase__		  =  S[s : s + size][start_token_masks[s : s + size]]
										lowerCAmelCase__		  =  S[s : s + size][end_token_masks[s : s + size]]

										lowerCAmelCase__		  =  torch.matmul(q[i]    ,s_start.T						).sum(1						).softmax(0						)
										lowerCAmelCase__		  =  torch.matmul(q[i]    ,s_end.T						).sum(1						).softmax(0						)

										if p_starts is not None:
														lowerCAmelCase__		  =  torch.vstack((p_starts, p_start)						)
														lowerCAmelCase__		  =  torch.vstack((p_ends, p_end)						)
										else:
														lowerCAmelCase__		  =  p_start
														lowerCAmelCase__		  =  p_end

						return p_starts, p_ends


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=1024			)       ->      Union[str, Any]:
     UpperCamelCase    ,		UpperCamelCase						    =					[], []

     UpperCamelCase						    =					list(zip(_lowercase						,     _lowercase			)			)
     UpperCamelCase    ,		UpperCamelCase						    =					sorted_examples[0]

     def is_too_big(_lowercase			):
          return tok(_lowercase						,     return_tensors='pt'			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          UpperCamelCase						    =					new_src + ' ' + src
          UpperCamelCase						    =					new_tgt + ' ' + tgt
          if is_too_big(_lowercase			) or is_too_big(_lowercase			):  # cant fit, finalize example
               finished_src.append(_lowercase			)
               finished_tgt.append(_lowercase			)
               UpperCamelCase    ,		UpperCamelCase						    =					src, tgt
          else:  # can fit, keep adding
               UpperCamelCase    ,		UpperCamelCase						    =					cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(_lowercase			)
          finished_tgt.append(_lowercase			)
     return finished_src, finished_tgt

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     UpperCamelCase						    =					Path(_lowercase			)
     save_path.mkdir(exist_ok=_lowercase			)
     for split in ["train"]:
          UpperCamelCase    ,		UpperCamelCase						    =					data_dir / F'{split}.source', data_dir / F'{split}.target'
          UpperCamelCase						    =					[x.rstrip() for x in Path(_lowercase			).open().readlines()]
          UpperCamelCase						    =					[x.rstrip() for x in Path(_lowercase			).open().readlines()]
          UpperCamelCase    ,		UpperCamelCase						    =					pack_examples(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)
          print(F'packed {split} split from {len(_lowercase			)} examples -> {len(_lowercase			)}.'			)
          Path(save_path / F'{split}.source'			).open('w'			).write('\n'.join(_lowercase			)			)
          Path(save_path / F'{split}.target'			).open('w'			).write('\n'.join(_lowercase			)			)
     for split in ["val", "test"]:
          UpperCamelCase    ,		UpperCamelCase						    =					data_dir / F'{split}.source', data_dir / F'{split}.target'
          shutil.copyfile(_lowercase						,     save_path / F'{split}.source'			)
          shutil.copyfile(_lowercase						,     save_path / F'{split}.target'			)

def  __lowerCamelCase					( )       ->      Union[str, Any]:
     UpperCamelCase						    =					argparse.ArgumentParser()
     parser.add_argument('--tok_name'						,     type=_lowercase						,     help='like facebook/bart-large-cnn,t5-base, etc.'			)
     parser.add_argument('--max_seq_len'						,     type=_lowercase						,     default=128			)
     parser.add_argument('--data_dir'						,     type=_lowercase			)
     parser.add_argument('--save_path'						,     type=_lowercase			)
     UpperCamelCase						    =					parser.parse_args()
     UpperCamelCase						    =					AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(_lowercase						,     Path(args.data_dir			)						,     args.max_seq_len						,     args.save_path			)


if __name__ == "__main__":
 packer_cli()


