



"""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)
_lowercase			:			List[str]										=						logging.getLogger(__name__)


_lowercase			:			Any										=						"Hello world! cécé herlolip"


_lowercase			:			List[str]										=						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__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			lowerCamelCase__		:		str		     =BertAbsConfig(
			    temp_dir='''.'''							,		finetune_bert=lowerCamelCase__							,		large=lowerCamelCase__							,		share_emb=lowerCamelCase__							,		use_bert_emb=lowerCamelCase__							,		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__		:		List[Any]		     =torch.load(lowerCamelCase__							,		lambda __lowerCamelCase							,		__lowerCamelCase     : storage     )
			lowerCamelCase__		:		int		     =AbsSummarizer(lowerCamelCase__							,		torch.device('''cpu'''     )							,		lowerCamelCase__     )
			original.eval()

			lowerCamelCase__		:		Tuple		     =BertAbsSummarizer(lowerCamelCase__							,		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__		:		int		     =BertTokenizer.from_pretrained('''bert-base-uncased'''     )

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

			lowerCamelCase__		:		Union[str, Any]		     =new_model(
			    lowerCamelCase__							,		lowerCamelCase__							,		lowerCamelCase__							,		lowerCamelCase__							,		lowerCamelCase__     )[0]
			lowerCamelCase__		:		str		     =new_model.generator(lowerCamelCase__     )

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

			lowerCamelCase__		:		Any		     =torch.allclose(lowerCamelCase__							,		lowerCamelCase__							,		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			:			Optional[int]										=						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			:			List[Any]										=						parser.parse_args()

		convert_bertabs_checkpoints(
		    args.bertabs_checkpoint_path,
		    args.pytorch_dump_folder_path,
		)









"""simple docstring"""






from __future__ import annotations






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   list[str] | None = None     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =word_bank or []
			# create a table
			lowerCamelCase__		:		int		     =len(__lowerCamelCase     ) + 1

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

			# iterate through the indices
			for i in range(__lowerCamelCase     ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(__lowerCamelCase     )] == 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(__lowerCamelCase     )] += new_combinations

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

			return table[len(__lowerCamelCase     )]


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"],
		    )
		)




