



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)
lowercase__     							=						logging.getLogger(__name__)


lowercase__     							=						"""Hello world! cécé herlolip"""


lowercase__     							=						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				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Optional[int] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		BertAbsConfig(
			    temp_dir="""."""      ,			finetune_bert=UpperCamelCase      ,			large=UpperCamelCase      ,			share_emb=UpperCamelCase      ,			use_bert_emb=UpperCamelCase      ,			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      ,			)
			lowerCamelCase__ :      Optional[Any]								=		torch.load(UpperCamelCase      ,			lambda UpperCamelCase      ,			UpperCamelCase : storage )
			lowerCamelCase__ :      Dict								=		AbsSummarizer(UpperCamelCase      ,			torch.device("""cpu""" )      ,			UpperCamelCase )
			original.eval()

			lowerCamelCase__ :      Union[str, Any]								=		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__ :      Union[str, Any]								=		BertTokenizer.from_pretrained("""bert-base-uncased""" )

			# prepare the model inputs
			lowerCamelCase__ :      str								=		tokenizer.encode("""This is sample éàalj'-.""" )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase )) )
			lowerCamelCase__ :      List[Any]								=		torch.tensor(UpperCamelCase ).unsqueeze(0 )
			lowerCamelCase__ :      Tuple								=		tokenizer.encode("""This is sample 3 éàalj'-.""" )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase )) )
			lowerCamelCase__ :      int								=		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__ :      Optional[Any]								=		encoder_input_ids
			lowerCamelCase__ :      Union[str, Any]								=		decoder_input_ids
			lowerCamelCase__ :      Tuple								=		None
			lowerCamelCase__ :      List[str]								=		None
			lowerCamelCase__ :      Dict								=		None
			lowerCamelCase__ :      Any								=		None
			lowerCamelCase__ :      Dict								=		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__ :      List[str]								=		original(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )[0]
			lowerCamelCase__ :      Optional[int]								=		original.generator(UpperCamelCase )

			lowerCamelCase__ :      int								=		new_model(
			    UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )[0]
			lowerCamelCase__ :      List[str]								=		new_model.generator(UpperCamelCase )

			lowerCamelCase__ :      Any								=		torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase ) )
			lowerCamelCase__ :      Optional[Any]								=		torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase ) )

			lowerCamelCase__ :      Dict								=		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__":
				lowercase__     							=						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.''',
				)
				lowercase__     							=						parser.parse_args()

				convert_bertabs_checkpoints(
				    args.bertabs_checkpoint_path,
				    args.pytorch_dump_folder_path,
				)








from __future__ import annotations






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] | None = None )  ->list[list[str]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		word_bank or []
			# create a table
			lowerCamelCase__ :      int								=		len(UpperCamelCase ) + 1

			lowerCamelCase__ :      list[list[list[str]]]								=		[]
			for _ in range(UpperCamelCase ):
						table.append([] )
			# seed value
			lowerCamelCase__ :      Tuple								=		[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(UpperCamelCase ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(UpperCamelCase )] == word:
															lowerCamelCase__ :      list[list[str]]								=		[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(UpperCamelCase )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(UpperCamelCase )]:
						combination.reverse()

			return table[len(UpperCamelCase )]


if __name__ == "__main__":
				print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
				print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
				print(
				    all_construct(
				        '''hexagonosaurus''',
				        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
				    )
				)



