


'''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)
__A						:						Union[str, Any]  =  logging.getLogger(__name__)


__A						:						Dict  =  'Hello world! cécé herlolip'


__A						:						Optional[Any]  =  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   (		lowerCamelCase_	:List[Any]					,   lowerCamelCase_	:int			):







		'''simple docstring'''
		snake_case_		:				int      							=    BertAbsConfig(
		    temp_dir="""."""					,   finetune_bert=lowercase_					,   large=lowercase_					,   share_emb=lowercase_					,   use_bert_emb=lowercase_					,   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					,   )
		snake_case_		:				Union[str, Any]      							=    torch.load(lowercase_					,   lambda lowerCamelCase_					,   lowerCamelCase_			: storage			)
		snake_case_		:				List[Any]      							=    AbsSummarizer(lowercase_					,   torch.device("""cpu"""			)					,   lowercase_			)
		original.eval()

		snake_case_		:				List[Any]      							=    BertAbsSummarizer(lowercase_					,   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"""			)
		snake_case_		:				Tuple      							=    BertTokenizer.from_pretrained("""bert-base-uncased"""			)

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

		snake_case_		:				Optional[int]      							=    new_model(
		    lowercase_					,   lowercase_					,   lowercase_					,   lowercase_					,   lowercase_			)[0]
		snake_case_		:				List[str]      							=    new_model.generator(lowercase_			)

		snake_case_		:				List[str]      							=    torch.max(torch.abs(output_converted_model - output_original_model			)			).item()
		print("""Maximum absolute difference beween weights: {:.2f}""".format(lowercase_			)			)
		snake_case_		:				Any      							=    torch.max(torch.abs(output_converted_generator - output_original_generator			)			).item()
		print("""Maximum absolute difference beween weights: {:.2f}""".format(lowercase_			)			)

		snake_case_		:				List[Any]      							=    torch.allclose(lowercase_					,   lowercase_					,   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						:						Union[str, Any]  =  parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)






"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
  )
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--hf_config",
      default="facebook/mbart-large-cc25",
      type=str,
      help="Which huggingface architecture to use: mbart-large",
  )
  parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
  parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)
