


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			:						int        =  'Hello world! cécé herlolip'


A			:						Optional[int]        =  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 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				List[str]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						BertAbsConfig(
		    temp_dir="""."""       ,       finetune_bert=__magic_name__       ,       large=__magic_name__       ,       share_emb=__magic_name__       ,       use_bert_emb=__magic_name__       ,       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       ,       )
		lowercase__	     =						torch.load(__magic_name__       ,       lambda __magic_name__       ,       __magic_name__	: storage	)
		lowercase__	     =						AbsSummarizer(__magic_name__       ,       torch.device("""cpu"""	)       ,       __magic_name__	)
		original.eval()

		lowercase__	     =						BertAbsSummarizer(__magic_name__       ,       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__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""	)

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

		lowercase__	     =						new_model(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)[0]
		lowercase__	     =						new_model.generator(__magic_name__	)

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

		lowercase__	     =						torch.allclose(__magic_name__       ,       __magic_name__       ,       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			:						List[str]        =  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			:						Tuple        =  parser.parse_args()

	convert_bertabs_checkpoints(
	    args.bertabs_checkpoint_path,
	    args.pytorch_dump_folder_path,
	)





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		NestedDataStructureLike[PathLike] ,   _UpperCAmelCase       :		Optional[NamedSplit] = None ,   _UpperCAmelCase       :		Optional[Features] = None ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		Optional[int] = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  List[str]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase ,   split=_UpperCAmelCase ,   features=_UpperCAmelCase ,   cache_dir=_UpperCAmelCase ,   keep_in_memory=_UpperCAmelCase ,   streaming=_UpperCAmelCase ,   num_proc=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						path_or_paths if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else {self.split: path_or_paths}
							lowercase__	     =						Text(
							    cache_dir=_UpperCAmelCase ,   data_files=_UpperCAmelCase ,   features=_UpperCAmelCase ,   **_UpperCAmelCase ,   )


					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							if self.streaming:
									lowercase__	     =						self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase ,   download_mode=_UpperCAmelCase ,   verification_mode=_UpperCAmelCase ,   base_path=_UpperCAmelCase ,   num_proc=self.num_proc ,   )
									lowercase__	     =						self.builder.as_dataset(
									    split=self.split ,   verification_mode=_UpperCAmelCase ,   in_memory=self.keep_in_memory						)
							return dataset

