

'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__      		=			logging.get_logger(__name__)


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				torch.load(A__  ,       map_location='''cpu'''   )
 if "model" in sd.keys():
  __lowercase													=				torch.load(A__  ,       map_location='''cpu'''   )['''model''']

 # pop unnecessary weights
 __lowercase													=				[
     '''decoder.version''',
     '''decoder.output_projection.weight''',
 ]
 for key in keys_to_delete:
  if key in sd:
   sd.pop(A__   )

 __lowercase													=				{
     '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
     '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
     '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
     '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
 }
 for old_key, new_key in keys_to_rename.items():
  if old_key in sd:
   __lowercase													=				sd.pop(A__   )

 __lowercase													=				list(sd.keys()   )
 for key in keys:
  if ".qkv_proj." in key:
   __lowercase													=				sd[key]
   # We split QKV in separate Q,K,V

   __lowercase													=				key.replace('''.qkv_proj.'''  ,       '''.q_proj.'''   )
   __lowercase													=				key.replace('''.qkv_proj.'''  ,       '''.k_proj.'''   )
   __lowercase													=				key.replace('''.qkv_proj.'''  ,       '''.v_proj.'''   )

   __lowercase													=				value.shape[0]
   assert depth % 3 == 0
   # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
   # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
   __lowercase						,			__lowercase						,			__lowercase													=				torch.split(A__  ,       depth // 3  ,       dim=0   )

   __lowercase													=				q
   __lowercase													=				k
   __lowercase													=				v
   del sd[key]

 return sd


@torch.no_grad()
def 	_A  (					A__  ,       A__  ,       A__=None   ):






 """simple docstring"""



 __lowercase													=				load_checkpoint(A__   )

 if config is not None:
  __lowercase													=				OPTConfig.from_pretrained(A__   )
 else:
  __lowercase													=				OPTConfig()

 __lowercase													=				OPTModel(A__   ).half().eval()
 model.load_state_dict(A__   )

 # Check results
 Path(A__   ).mkdir(exist_ok=A__   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--fairseq_path''',
     type=str,
     help=(
         '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
         ''' https://huggingface.co/models?other=opt_metasq'''
     ),
 )
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
 lowerCAmelCase__      		=			parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




'''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)
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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				BertAbsConfig(
     temp_dir='''.'''  ,       finetune_bert=A__  ,       large=A__  ,       share_emb=A__  ,       use_bert_emb=A__  ,       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(A__  ,       lambda A__  ,       A__   : storage   )
 __lowercase													=				AbsSummarizer(A__  ,       torch.device('''cpu'''   )  ,       A__   )
 original.eval()

 __lowercase													=				BertAbsSummarizer(A__  ,       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(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).unsqueeze(0   )
 __lowercase													=				tokenizer.encode('''This is sample 3 éàalj\'-.'''   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).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(A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				original.generator(A__   )

 __lowercase													=				new_model(
     A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				new_model.generator(A__   )

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

 __lowercase													=				torch.allclose(A__  ,       A__  ,       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,
 )

