



"""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   :  Any					=      logging.getLogger(__name__)


__A   :  str					=      'Hello world! cécé herlolip'


__A   :  Dict					=      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,  _lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		str												=				BertAbsConfig(
		    temp_dir=".",  finetune_bert=lowerCAmelCase__,  large=lowerCAmelCase__,  share_emb=lowerCAmelCase__,  use_bert_emb=lowerCAmelCase__,  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,  )
		__lowercase				:		str												=				torch.load(lowerCAmelCase__,  lambda _lowerCamelCase,  _lowerCamelCase				: storage				)
		__lowercase				:		str												=				AbsSummarizer(lowerCAmelCase__,  torch.device("cpu"				),  lowerCAmelCase__				)
		original.eval()

		__lowercase				:		List[str]												=				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"				)
		__lowercase				:		Any												=				BertTokenizer.from_pretrained("bert-base-uncased"				)

		# prepare the model inputs
		__lowercase				:		List[str]												=				tokenizer.encode("This is sample éàalj\'-."				)
		encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCAmelCase__				))				)
		__lowercase				:		Optional[int]												=				torch.tensor(lowerCAmelCase__				).unsqueeze(0				)
		__lowercase				:		Union[str, Any]												=				tokenizer.encode("This is sample 3 éàalj\'-."				)
		decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCAmelCase__				))				)
		__lowercase				:		Optional[int]												=				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
		__lowercase				:		str												=				encoder_input_ids
		__lowercase				:		Optional[Any]												=				decoder_input_ids
		__lowercase				:		Optional[int]												=				None
		__lowercase				:		Optional[int]												=				None
		__lowercase				:		Optional[Any]												=				None
		__lowercase				:		Tuple												=				None
		__lowercase				:		Union[str, 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
		__lowercase				:		Any												=				original(lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__				)[0]
		__lowercase				:		Union[str, Any]												=				original.generator(lowerCAmelCase__				)

		__lowercase				:		List[Any]												=				new_model(
		    lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__				)[0]
		__lowercase				:		Union[str, Any]												=				new_model.generator(lowerCAmelCase__				)

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

		__lowercase				:		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__":
						__A   :  Union[str, 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.',
						)
						__A   :  str					=      parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)






"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

__A   :  str					=      tuple[int, int]




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	set[int]    ,   lowercase__					:	Mapping[EdgeT, int]						):
						__lowercase				:		set[int]												=				vertices
						__lowercase				:		dict[EdgeT, int]												=				{
						    (min(lowercase__						), max(lowercase__						)): weight for edge, weight in edges.items()
						}



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	EdgeT    ,   lowercase__					:	int						):
						self.vertices.add(edge[0]						)
						self.vertices.add(edge[1]						)
						__lowercase				:		Union[str, Any]												=				weight



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Graph												=				Graph({min(self.vertices						)}    ,   {}						)
						__lowercase				:		EdgeT
						__lowercase				:		int
						__lowercase				:		EdgeT
						__lowercase				:		int

						while len(subgraph.vertices						) < len(self.vertices						):
								__lowercase				:		int												=				max(self.edges.values()						) + 1
								for edge, weight in self.edges.items():
										if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
												if weight < min_weight:
														__lowercase				:		Optional[Any]												=				edge
														__lowercase				:		List[str]												=				weight

								subgraph.add_edge(lowercase__    ,   lowercase__						)

						return subgraph





def 						snake_case__       (					_lowerCamelCase = "p107_network.txt"				)			->int:






		"""simple docstring"""


		__lowercase				:		str												=				os.path.abspath(os.path.dirname(_lowerCamelCase				)				)
		__lowercase				:		str												=				os.path.join(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		dict[EdgeT, int]												=				{}
		__lowercase				:		list[str]
		__lowercase				:		int
		__lowercase				:		int

		with open(_lowerCamelCase				) as f:
				__lowercase				:		List[str]												=				f.read().strip().split("\n"				)

		__lowercase				:		Any												=				[line.split(","				) for line in data]

		for edgea in range(1,  len(_lowerCamelCase				)				):
				for edgea in range(_lowerCamelCase				):
						if adjaceny_matrix[edgea][edgea] != "-":
								__lowercase				:		Dict												=				int(adjaceny_matrix[edgea][edgea]				)

		__lowercase				:		Graph												=				Graph(set(range(len(_lowerCamelCase				)				)				),  _lowerCamelCase				)

		__lowercase				:		Graph												=				graph.prims_algorithm()

		__lowercase				:		int												=				sum(graph.edges.values()				)
		__lowercase				:		int												=				sum(subgraph.edges.values()				)

		return initial_total - optimal_total


if __name__ == "__main__":
						print(F"""{solution() = }""")
