

import torch

from transformers import AutoModel


class 				SCREAMING_SNAKE_CASE__ (torch.nn.Module					):





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

         lowercase__   :       List[str]				=	AutoModel.from_pretrained(a						,       return_dict=a)
         lowercase__   :       Dict				=	torch.nn.CosineSimilarity(3						,       1e-08)
         lowercase__   :       Tuple				=	torch.nn.Softmax(dim=1)





    def 				snake_case_    (				self						,       **a):
         return self.bert(**a).last_hidden_state





    def 				snake_case_    (				self						,       a):
         return token_embeddings.sum(2						,       keepdim=a)





    def 				snake_case_    (				self						,       a						,       a						,       a=1):
         return self.softmax(T * self.cos(a						,       a))





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       Optional[int]				=	W_supports['sizes'].tolist()
         lowercase__   :       List[str]				=	W_supports['start_token_id'].item()
         lowercase__   :       List[str]				=	W_supports['end_token_id'].item()

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

         lowercase__   :       Union[str, Any]				=	self.BERT(**a)
         lowercase__   :       List[Any]				=	self.BERT(**a)

         lowercase__   :       Dict				=	None
         lowercase__   :       Dict				=	None

         lowercase__   :       Tuple				=	W_supports['input_ids'] == start_token_id
         lowercase__   :       str				=	W_supports['input_ids'] == end_token_id

         for i, size in enumerate(a):
              if i == 0:
                   lowercase__   :       List[Any]				=	0
              else:
                   lowercase__   :       List[str]				=	support_sizes[i - 1]

              lowercase__   :       Optional[int]				=	S[s : s + size][start_token_masks[s : s + size]]
              lowercase__   :       Dict				=	S[s : s + size][end_token_masks[s : s + size]]

              lowercase__   :       Optional[int]				=	torch.matmul(q[i]						,       s_start.T).sum(1).softmax(0)
              lowercase__   :       Union[str, Any]				=	torch.matmul(q[i]						,       s_end.T).sum(1).softmax(0)

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

         return p_starts, p_ends




import os

try:
       from .build_directory_md import good_file_paths
except ImportError:
       from build_directory_md import good_file_paths  # type: ignore

snake_case_        =			list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

snake_case_        =			[file for file in filepaths if file != file.lower()]
if upper_files:
       print(F'''{len(upper_files)} files contain uppercase characters:''')
       print('''\n'''.join(upper_files) + '''\n''')

snake_case_        =			[file for file in filepaths if ''' ''' in file]
if space_files:
       print(F'''{len(space_files)} files contain space characters:''')
       print('''\n'''.join(space_files) + '''\n''')

snake_case_        =			[file for file in filepaths if '''-''' in file]
if hyphen_files:
       print(F'''{len(hyphen_files)} files contain hyphen characters:''')
       print('''\n'''.join(hyphen_files) + '''\n''')

snake_case_        =			[file for file in filepaths if os.sep not in file]
if nodir_files:
       print(F'''{len(nodir_files)} files are not in a directory:''')
       print('''\n'''.join(nodir_files) + '''\n''')

snake_case_        =			len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
       import sys

       sys.exit(bad_files)

