




'''simple docstring'''







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


A     =subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
A     =subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()

A     ='|'.join(sys.argv[1:])
A     =re.compile(rf"""^({joined_dirs}).*?\.py$""")

A     =[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')










'''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)
A     =logging.getLogger(__name__)


A     ='Hello world! cécé herlolip'


A     =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 						snake_case_							(_a	:							List[Any]    ,   _a	:							Any     ):
    UpperCAmelCase			       =		BertAbsConfig(
        temp_dir='''.'''    ,   finetune_bert=_a    ,   large=_a    ,   share_emb=_a    ,   use_bert_emb=_a    ,   encoder='''bert'''    ,   max_pos=5_1_2    ,   enc_layers=6    ,   enc_hidden_size=5_1_2    ,   enc_heads=8    ,   enc_ff_size=5_1_2    ,   enc_dropout=0.2    ,   dec_layers=6    ,   dec_hidden_size=7_6_8    ,   dec_heads=8    ,   dec_ff_size=2_0_4_8    ,   dec_dropout=0.2    ,   )
    UpperCAmelCase			       =		torch.load(_a    ,   lambda _a    ,   _a     : storage     )
    UpperCAmelCase			       =		AbsSummarizer(_a    ,   torch.device('''cpu'''     )    ,   _a     )
    original.eval()

    UpperCAmelCase			       =		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'''     )
    UpperCAmelCase			       =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )

    # prepare the model inputs
    UpperCAmelCase			       =		tokenizer.encode('''This is sample éàalj\'-.'''     )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).unsqueeze(0     )
    UpperCAmelCase			       =		tokenizer.encode('''This is sample 3 éàalj\'-.'''     )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		encoder_input_ids
    UpperCAmelCase			       =		decoder_input_ids
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		original(_a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		original.generator(_a     )

    UpperCAmelCase			       =		new_model(
        _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		new_model.generator(_a     )

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

    UpperCAmelCase			       =		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__":
   A     =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.',
   )
   A     =parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




