


'''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)
__snake_case    						=logging.getLogger(__name__)


__snake_case    						="""Hello world! cécé herlolip"""


__snake_case    						=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 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			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     =			torch.load(lowerCamelCase	, lambda lowerCamelCase	, lowerCamelCase				: storage				)
   lowerCAmelCase     =			AbsSummarizer(lowerCamelCase	, torch.device('cpu'				)	, lowerCamelCase				)
   original.eval()

   lowerCAmelCase     =			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     =			BertTokenizer.from_pretrained('bert-base-uncased'				)

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

   lowerCAmelCase     =			new_model(
       lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)[0]
   lowerCAmelCase     =			new_model.generator(lowerCamelCase				)

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

   lowerCAmelCase     =			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__":
       __snake_case    						=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.""",
       )
       __snake_case    						=parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )









'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu

@require_cpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[Any]:
        lowerCAmelCase     =			torch.nn.Linear(1_0    ,      1_0    )
        lowerCAmelCase     =			torch.optim.SGD(model.parameters()    ,      0.1    )
        lowerCAmelCase     =			Accelerator()
        lowerCAmelCase     =			accelerator.prepare(UpperCAmelCase__    )
        try:
           pickle.loads(pickle.dumps(UpperCAmelCase__    )    )
        except Exception as e:
           self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
        AcceleratorState._reset_state()





