





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)
UpperCAmelCase__  :     int   			=logging.getLogger(__name__)


UpperCAmelCase__  :     List[Any]   			="Hello world! cécé herlolip"


UpperCAmelCase__  :     Tuple   			=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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					List[str]:
				lowerCamelCase			=BertAbsConfig(
				    temp_dir="""."""     , finetune_bert=_UpperCAmelCase     , large=_UpperCAmelCase     , share_emb=_UpperCAmelCase     , use_bert_emb=_UpperCAmelCase     , encoder="""bert"""     , max_pos=5_12     , enc_layers=6     , enc_hidden_size=5_12     , enc_heads=8     , enc_ff_size=5_12     , enc_dropout=0.2     , dec_layers=6     , dec_hidden_size=7_68     , dec_heads=8     , dec_ff_size=20_48     , dec_dropout=0.2     , )
				lowerCamelCase			=torch.load(_UpperCAmelCase     , lambda _UpperCAmelCase     , _UpperCAmelCase			: storage			)
				lowerCamelCase			=AbsSummarizer(_UpperCAmelCase     , torch.device("""cpu"""			)     , _UpperCAmelCase			)
				original.eval()

				lowerCamelCase			=BertAbsSummarizer(_UpperCAmelCase     , 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"""			)
				lowerCamelCase			=BertTokenizer.from_pretrained("""bert-base-uncased"""			)

				# prepare the model inputs
				lowerCamelCase			=tokenizer.encode("""This is sample éàalj'-."""			)
				encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_UpperCAmelCase			))			)
				lowerCamelCase			=torch.tensor(_UpperCAmelCase			).unsqueeze(0			)
				lowerCamelCase			=tokenizer.encode("""This is sample 3 éàalj'-."""			)
				decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_UpperCAmelCase			))			)
				lowerCamelCase			=torch.tensor(_UpperCAmelCase			).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
				lowerCamelCase			=encoder_input_ids
				lowerCamelCase			=decoder_input_ids
				lowerCamelCase			=None
				lowerCamelCase			=None
				lowerCamelCase			=None
				lowerCamelCase			=None
				lowerCamelCase			=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
				lowerCamelCase			=original(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)[0]
				lowerCamelCase			=original.generator(_UpperCAmelCase			)

				lowerCamelCase			=new_model(
				    _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)[0]
				lowerCamelCase			=new_model.generator(_UpperCAmelCase			)

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

				lowerCamelCase			=torch.allclose(_UpperCAmelCase     , _UpperCAmelCase     , 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__":
						UpperCAmelCase__  :     List[Any]   			=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.''',
						)
						UpperCAmelCase__  :     Optional[int]   			=parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)












import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def        _lowercase   (  _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=[]
				for line in lines:
								lowerCamelCase			=re.sub(r"""#.*"""     , """"""     , _UpperCAmelCase			)  # remove comments
								if line:
												filtered_lines.append(_UpperCAmelCase			)
				lowerCamelCase			="""\n""".join(_UpperCAmelCase			)

				# Make a hash from all this code
				lowerCamelCase			=full_str.encode("""utf-8"""			)
				return shaaaa(_UpperCAmelCase			).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase__  :     str   			={
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase__  :     Tuple   			={
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase__  :     Optional[Any]   			={'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
UpperCAmelCase__  :     Dict[str, List[str]]   			={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
							_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')




_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')





