





'''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_					:    Optional[Any]      	=			logging.getLogger(__name__)


lowerCAmelCase_					:    Union[str, Any]      	=			'Hello world! cécé herlolip'


lowerCAmelCase_					:    Any      	=			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  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	List[Any]:
				_a								= 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    ,							)
				_a								= torch.load(_a    ,							lambda lowercase    ,							lowercase						: storage						)
				_a								= AbsSummarizer(_a    ,							torch.device("cpu"						)    ,							_a						)
				original.eval()

				_a								= 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"						)
				_a								= BertTokenizer.from_pretrained("bert-base-uncased"						)

				# prepare the model inputs
				_a								= tokenizer.encode("This is sample éàalj\'-."						)
				encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a						))						)
				_a								= torch.tensor(_a						).unsqueeze(0						)
				_a								= tokenizer.encode("This is sample 3 éàalj\'-."						)
				decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a						))						)
				_a								= 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
				_a								= encoder_input_ids
				_a								= decoder_input_ids
				_a								= _a								= None
				_a								= None
				_a								= _a								= None
				_a								= _a								= None
				_a								= 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
				_a								= original(_a    ,							_a    ,							_a    ,							_a    ,							_a    ,							_a    ,							_a						)[0]
				_a								= original.generator(_a						)

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

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

				_a								= 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_					:    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.',
					)
					lowerCAmelCase_					:    List[Any]      	=			parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)






'''simple docstring'''







from ..utils import DummyObject, requires_backends


class 			_a  (	metaclass=__a ):
       __a				:     int   				=					["""flax""", """transformers"""]
       def __init__(	self						:	Optional[Any]   ,				*lowercase						:	str   ,				**lowercase						:	List[Any]				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	List[Any]   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	Tuple   ,				**lowercase						:	int				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)






class 			_a  (	metaclass=__a ):
       __a				:     int   				=					["""flax""", """transformers"""]
       def __init__(	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	List[str]   ,				*lowercase						:	Optional[int]   ,				**lowercase						:	List[Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Dict   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)






class 			_a  (	metaclass=__a ):
       __a				:     int   				=					["""flax""", """transformers"""]
       def __init__(	self						:	Optional[int]   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Any				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	Tuple   ,				**lowercase						:	Any				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Any   ,				*lowercase						:	Dict   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)






class 			_a  (	metaclass=__a ):
       __a				:     Any   				=					["""flax""", """transformers"""]
       def __init__(	self						:	Any   ,				*lowercase						:	Optional[Any]   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	Dict   ,				*lowercase						:	Optional[Any]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	str   ,				**lowercase						:	Any				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)




