




'''simple docstring'''



import argparse
import os
import re

import packaging.version


lowerCamelCase_			=						'examples/'
lowerCamelCase_			=						{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowerCamelCase_			=						{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowerCamelCase_			=						'README.md'
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			Optional[int]	,					__A    :			Tuple  )			->			Any:
   with open(__A	,					"r"	,					encoding="utf-8"	,					newline="\n"  ) as f:
      _SCREAMING_SNAKE_CASE	=				f.read()
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				REPLACE_PATTERNS[pattern]
   _SCREAMING_SNAKE_CASE	=				replace.replace("VERSION"	,					__A  )
   _SCREAMING_SNAKE_CASE	=				re_pattern.sub(__A	,					__A  )
   with open(__A	,					"w"	,					encoding="utf-8"	,					newline="\n"  ) as f:
      f.write(__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Optional[int]:
   for folder, directories, fnames in os.walk(__A  ):
      # Removing some of the folders with non-actively maintained examples from the walk
      if "research_projects" in directories:
         directories.remove("research_projects"  )
      if "legacy" in directories:
         directories.remove("legacy"  )
      for fname in fnames:
         if fname.endswith(".py"  ):
            update_version_in_file(os.path.join(__A	,					__A  )	,					__A	,					pattern="examples"  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Tuple	,					__A    :			str=False  )			->			Any:
   for pattern, fname in REPLACE_FILES.items():
      update_version_in_file(__A	,					__A	,					__A  )
   if not patch:
      update_version_in_examples(__A  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				"🤗 Transformers currently provides the following architectures"
   _SCREAMING_SNAKE_CASE	=				"1. Want to contribute a new model?"
   with open(__A	,					"r"	,					encoding="utf-8"	,					newline="\n"  ) as f:
      _SCREAMING_SNAKE_CASE	=				f.readlines()

   # Find the start of the list.
   _SCREAMING_SNAKE_CASE	=				0
   while not lines[start_index].startswith(_start_prompt  ):
      start_index += 1
   start_index += 1

   _SCREAMING_SNAKE_CASE	=				start_index
   # Update the lines in the model list.
   while not lines[index].startswith(_end_prompt  ):
      if lines[index].startswith("1."  ):
         _SCREAMING_SNAKE_CASE	=				lines[index].replace(
             "https://huggingface.co/docs/transformers/main/model_doc"	,					"https://huggingface.co/docs/transformers/model_doc"	,					)
      index += 1

   with open(__A	,					"w"	,					encoding="utf-8"	,					newline="\n"  ) as f:
      f.writelines(__A  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			str:
   with open(REPLACE_FILES["init"]	,					"r"  ) as f:
      _SCREAMING_SNAKE_CASE	=				f.read()
   _SCREAMING_SNAKE_CASE	=				REPLACE_PATTERNS["init"][0].search(__A  ).groups()[0]
   return packaging.version.parse(__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]=False  )			->			str:
   _SCREAMING_SNAKE_CASE	=				get_version()
   if patch and default_version.is_devrelease:
      raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"  )
   if default_version.is_devrelease:
      _SCREAMING_SNAKE_CASE	=				default_version.base_version
   elif patch:
      _SCREAMING_SNAKE_CASE	=				f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
   else:
      _SCREAMING_SNAKE_CASE	=				f"""{default_version.major}.{default_version.minor + 1}.0"""

   # Now let's ask nicely if that's the right one.
   _SCREAMING_SNAKE_CASE	=				input(f"""Which version are you releasing? [{default_version}]"""  )
   if len(__A  ) == 0:
      _SCREAMING_SNAKE_CASE	=				default_version

   print(f"""Updating version to {version}."""  )
   global_version_update(__A	,					patch=__A  )
   if not patch:
      print("Cleaning main README, don't forget to run `make fix-copies`."  )
      clean_main_ref_in_model_list()
def 			SCREAMING_SNAKE_CASE_			(							)			->			List[Any]:
   _SCREAMING_SNAKE_CASE	=				get_version()
   _SCREAMING_SNAKE_CASE	=				f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
   _SCREAMING_SNAKE_CASE	=				current_version.base_version

   # Check with the user we got that right.
   _SCREAMING_SNAKE_CASE	=				input(f"""Which version are we developing now? [{dev_version}]"""  )
   if len(__A  ) == 0:
      _SCREAMING_SNAKE_CASE	=				dev_version

   print(f"""Updating version to {version}."""  )
   global_version_update(__A  )
   print("Cleaning main README, don't forget to run `make fix-copies`."  )
   clean_main_ref_in_model_list()


if __name__ == "__main__":
 lowerCamelCase_			=						argparse.ArgumentParser()
 parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
 parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
 lowerCamelCase_			=						parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print('Nothing to do after a patch :-)')
 else:
  post_release_work()







'''simple docstring'''



import torch

from transformers import AutoModel







class 		lowercase_   (			torch.nn.Module       ):


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]="sayef/fsner-bert-base-uncased"   ):

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

      _SCREAMING_SNAKE_CASE	=				AutoModel.from_pretrained(__lowerCamelCase  ,				return_dict=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.CosineSimilarity(3  ,				1e-08   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.Softmax(dim=1   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return self.bert(**__lowerCamelCase   ).last_hidden_state



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return token_embeddings.sum(2  ,				keepdim=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple=1   ):

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



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

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

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

      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == start_token_id
      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == end_token_id

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

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

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

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

      return p_starts, p_ends

