



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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				a__	:							List[str]      						= BertAbsConfig(
				    temp_dir="."	,				finetune_bert=__UpperCamelCase	,				large=__UpperCamelCase	,				share_emb=__UpperCamelCase	,				use_bert_emb=__UpperCamelCase	,				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	,				)
				a__	:							int      						= torch.load(__UpperCamelCase	,				lambda __UpperCamelCase	,				__UpperCamelCase : storage )
				a__	:							Any      						= AbsSummarizer(__UpperCamelCase	,				torch.device("cpu" )	,				__UpperCamelCase )
				original.eval()

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

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

				a__	:							str      						= new_model(
				    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )[0]
				a__	:							List[str]      						= new_model.generator(__UpperCamelCase )

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

				a__	:							Union[str, Any]      						= 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__":
						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,
						)







import unittest

from knapsack import knapsack as k


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= 0
											a__	:							Union[str, Any]      						= [0]
											a__	:							Optional[Any]      						= [0]
											a__	:							Tuple      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					0				)

											a__	:							Optional[Any]      						= [60]
											a__	:							Tuple      						= [10]
											a__	:							Optional[Any]      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					0				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= 3
											a__	:							Optional[int]      						= [1, 2, 3]
											a__	:							List[Any]      						= [3, 2, 1]
											a__	:							List[Any]      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					5				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= 50
											a__	:							Union[str, Any]      						= [60, 100, 120]
											a__	:							str      						= [10, 20, 30]
											a__	:							Union[str, Any]      						= len(__UpperCAmelCase				)
											self.assertEqual(k.knapsack(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				) ,					220				)


if __name__ == "__main__":
						unittest.main()


