

'''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 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 with open(A__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''   ) as f:
  __lowercase													=				f.read()
 __lowercase						,			__lowercase													=				REPLACE_PATTERNS[pattern]
 __lowercase													=				replace.replace('''VERSION'''  ,       A__   )
 __lowercase													=				re_pattern.sub(A__  ,       A__   )
 with open(A__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''   ) as f:
  f.write(A__   )


def 	_A  (					A__   ):






 """simple docstring"""



 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 	_A  (					A__  ,       A__=False   ):






 """simple docstring"""



 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(A__  ,       A__  ,       A__   )
 if not patch:
  update_version_in_examples(A__   )


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				'''🤗 Transformers currently provides the following architectures'''
 __lowercase													=				'''1. Want to contribute a new model?'''
 with open(A__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''   ) as f:
  __lowercase													=				f.readlines()

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

 __lowercase													=				start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt   ):
  if lines[index].startswith('''1.'''   ):
   __lowercase													=				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 	_A  (					):






 """simple docstring"""



 with open(REPLACE_FILES['''init''']  ,       '''r'''   ) as f:
  __lowercase													=				f.read()
 __lowercase													=				REPLACE_PATTERNS['''init'''][0].search(A__   ).groups()[0]
 return packaging.version.parse(A__   )


def 	_A  (					A__=False   ):






 """simple docstring"""



 __lowercase													=				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:
  __lowercase													=				default_version.base_version
 elif patch:
  __lowercase													=				F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  __lowercase													=				F"{default_version.major}.{default_version.minor + 1}.0"

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






 """simple docstring"""



 __lowercase													=				get_version()
 __lowercase													=				F"{current_version.major}.{current_version.minor + 1}.0.dev0"
 __lowercase													=				current_version.base_version

 # Check with the user we got that right.
 __lowercase													=				input(F"Which version are we developing now? [{dev_version}]"   )
 if len(A__   ) == 0:
  __lowercase													=				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 argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
lowerCAmelCase__      		=			logging.getLogger(__name__)


lowerCAmelCase__      		=			'''Hello world! cécé herlolip'''


lowerCAmelCase__      		=			namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				BertAbsConfig(
     temp_dir='''.'''  ,       finetune_bert=A__  ,       large=A__  ,       share_emb=A__  ,       use_bert_emb=A__  ,       encoder='''bert'''  ,       max_pos=512  ,       enc_layers=6  ,       enc_hidden_size=512  ,       enc_heads=8  ,       enc_ff_size=512  ,       enc_dropout=0.2  ,       dec_layers=6  ,       dec_hidden_size=768  ,       dec_heads=8  ,       dec_ff_size=2048  ,       dec_dropout=0.2  ,       )
 __lowercase													=				torch.load(A__  ,       lambda A__  ,       A__   : storage   )
 __lowercase													=				AbsSummarizer(A__  ,       torch.device('''cpu'''   )  ,       A__   )
 original.eval()

 __lowercase													=				BertAbsSummarizer(A__  ,       torch.device('''cpu'''   )   )
 new_model.eval()

 # -------------------
 # Convert the weights
 # -------------------

 logging.info('''convert the model'''   )
 new_model.bert.load_state_dict(original.bert.state_dict()   )
 new_model.decoder.load_state_dict(original.decoder.state_dict()   )
 new_model.generator.load_state_dict(original.generator.state_dict()   )

 # ----------------------------------
 # Make sure the outpus are identical
 # ----------------------------------

 logging.info('''Make sure that the models\' outputs are identical'''   )
 __lowercase													=				BertTokenizer.from_pretrained('''bert-base-uncased'''   )

 # prepare the model inputs
 __lowercase													=				tokenizer.encode('''This is sample éàalj\'-.'''   )
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).unsqueeze(0   )
 __lowercase													=				tokenizer.encode('''This is sample 3 éàalj\'-.'''   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).unsqueeze(0   )

 # failsafe to make sure the weights reset does not affect the
 # loaded weights.
 assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight   )   ) == 0

 # forward pass
 __lowercase													=				encoder_input_ids
 __lowercase													=				decoder_input_ids
 __lowercase													=				__lowercase													=				None
 __lowercase													=				None
 __lowercase													=				__lowercase													=				None
 __lowercase													=				__lowercase													=				None
 __lowercase													=				None

 # The original model does not apply the geneator layer immediatly but rather in
 # the beam search (where it combines softmax + linear layer). Since we already
 # apply the softmax in our generation process we only apply the linear layer here.
 # We make sure that the outputs of the full stack are identical
 __lowercase													=				original(A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				original.generator(A__   )

 __lowercase													=				new_model(
     A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				new_model.generator(A__   )

 __lowercase													=				torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
 print('''Maximum absolute difference beween weights: {:.2f}'''.format(A__   )   )
 __lowercase													=				torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
 print('''Maximum absolute difference beween weights: {:.2f}'''.format(A__   )   )

 __lowercase													=				torch.allclose(A__  ,       A__  ,       atol=1e-3   )
 if are_identical:
  logging.info('''all weights are equal up to 1e-3'''   )
 else:
  raise ValueError('''the weights are different. The new model is likely different from the original one.'''   )

 # The model has been saved with torch.save(model) and this is bound to the exact
 # directory structure. We save the state_dict instead.
 logging.info('''saving the model\'s state dictionary'''   )
 torch.save(
     new_model.state_dict()  ,       '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'''   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 parser.add_argument(
     '''--bertabs_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the output PyTorch model.''',
 )
 lowerCAmelCase__      		=			parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )

