




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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						BertAbsConfig(
          temp_dir=""".""",finetune_bert=snake_case_,large=snake_case_,share_emb=snake_case_,use_bert_emb=snake_case_,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,)
      _A							:     Optional[int]			=						torch.load(snake_case_,lambda snake_case_,snake_case_     : storage     )
      _A							:     Optional[int]			=						AbsSummarizer(snake_case_,torch.device("""cpu"""     ),snake_case_     )
      original.eval()

      _A							:     List[str]			=						BertAbsSummarizer(snake_case_,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							:     List[Any]			=						tokenizer.encode("""This is sample éàalj'-."""     )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case_     ))     )
      _A							:     int			=						torch.tensor(snake_case_     ).unsqueeze(0     )
      _A							:     Any			=						tokenizer.encode("""This is sample 3 éàalj'-."""     )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case_     ))     )
      _A							:     List[str]			=						torch.tensor(snake_case_     ).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							:     List[str]			=						encoder_input_ids
      _A							:     int			=						decoder_input_ids
      _A							:     Tuple			=						None
      _A							:     List[Any]			=						None
      _A							:     Tuple			=						None
      _A							:     Tuple			=						None
      _A							:     List[str]			=						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							:     List[str]			=						original(snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_     )[0]
      _A							:     List[str]			=						original.generator(snake_case_     )

      _A							:     str			=						new_model(
          snake_case_,snake_case_,snake_case_,snake_case_,snake_case_     )[0]
      _A							:     Optional[Any]			=						new_model.generator(snake_case_     )

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

      _A							:     Optional[Any]			=						torch.allclose(snake_case_,snake_case_,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,
  )







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

