
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__          =		logging.getLogger(__name__)


lowerCamelCase__          =		"""Hello world! cécé herlolip"""


lowerCamelCase__          =		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__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:
			lowerCAmelCase__      :      Dict         =		BertAbsConfig(
			    temp_dir='.'       ,							finetune_bert=SCREAMING_SNAKE_CASE_       ,							large=SCREAMING_SNAKE_CASE_       ,							share_emb=SCREAMING_SNAKE_CASE_       ,							use_bert_emb=SCREAMING_SNAKE_CASE_       ,							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=2_048       ,							dec_dropout=0.2       ,							)
			lowerCAmelCase__      :      Dict         =		torch.load(SCREAMING_SNAKE_CASE_       ,							lambda SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     : storage     )
			lowerCAmelCase__      :      Dict         =		AbsSummarizer(SCREAMING_SNAKE_CASE_       ,							torch.device('cpu'     )       ,							SCREAMING_SNAKE_CASE_     )
			original.eval()

			lowerCAmelCase__      :      str         =		BertAbsSummarizer(SCREAMING_SNAKE_CASE_       ,							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__      :      Dict         =		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(SCREAMING_SNAKE_CASE_     ))     )
			lowerCAmelCase__      :      Tuple         =		torch.tensor(SCREAMING_SNAKE_CASE_     ).unsqueeze(0     )
			lowerCAmelCase__      :      List[str]         =		tokenizer.encode('This is sample 3 éàalj\'-.'     )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(SCREAMING_SNAKE_CASE_     ))     )
			lowerCAmelCase__      :      List[Any]         =		torch.tensor(SCREAMING_SNAKE_CASE_     ).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__      :      Union[str, Any]         =		encoder_input_ids
			lowerCAmelCase__      :      str         =		decoder_input_ids
			lowerCAmelCase__      :      Any         =		None
			lowerCAmelCase__      :      Dict         =		None
			lowerCAmelCase__      :      int         =		None
			lowerCAmelCase__      :      Any         =		None
			lowerCAmelCase__      :      str         =		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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )[0]
			lowerCAmelCase__      :      Dict         =		original.generator(SCREAMING_SNAKE_CASE_     )

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

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

			lowerCAmelCase__      :      List[str]         =		torch.allclose(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							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__          =		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__          =		parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)

class      A__							:






				def __init__(						self					:      Optional[Any]    ,     a					:      list   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		set_counts
							lowerCAmelCase__      :      str         =		max(a   )
							lowerCAmelCase__      :      Any         =		len(a   )
							lowerCAmelCase__      :      List[str]         =		[1] * num_sets
							lowerCAmelCase__      :      Dict         =		list(range(a   )   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.get_parent(a   )
							lowerCAmelCase__      :      Tuple         =		self.get_parent(a   )

							if src_parent == dst_parent:
										return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
										self.set_counts[dst_parent] += self.set_counts[src_parent]
										lowerCAmelCase__      :      Tuple         =		0
										lowerCAmelCase__      :      str         =		dst_parent
										if self.ranks[dst_parent] == self.ranks[src_parent]:
													self.ranks[dst_parent] += 1
										lowerCAmelCase__      :      List[Any]         =		self.set_counts[dst_parent]
							else:
										self.set_counts[src_parent] += self.set_counts[dst_parent]
										lowerCAmelCase__      :      Optional[int]         =		0
										lowerCAmelCase__      :      Tuple         =		src_parent
										lowerCAmelCase__      :      Optional[int]         =		self.set_counts[src_parent]

							lowerCAmelCase__      :      Optional[Any]         =		max(self.max_set    ,     a   )
							return True






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      int   ):






							'''simple docstring'''







							if self.parents[disj_set] == disj_set:
										return disj_set
							lowerCAmelCase__      :      Tuple         =		self.get_parent(self.parents[disj_set]   )
							return self.parents[disj_set]