



"""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)
UpperCAmelCase						:		int			= logging.getLogger(__name__)


UpperCAmelCase						:		Optional[int]			= """Hello world! cécé herlolip"""


UpperCAmelCase						:		List[str]			= 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						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  lowercase_								= BertAbsConfig(
      temp_dir=""".""" ,  finetune_bert=__lowerCAmelCase ,  large=__lowerCAmelCase ,  share_emb=__lowerCAmelCase ,  use_bert_emb=__lowerCAmelCase ,  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 ,  )
  lowercase_								= torch.load(__lowerCAmelCase ,  lambda __lowerCAmelCase ,  __lowerCAmelCase							: storage							)
  lowercase_								= AbsSummarizer(__lowerCAmelCase ,  torch.device("""cpu"""							) ,  __lowerCAmelCase							)
  original.eval()

  lowercase_								= 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"""							)
  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] * (5_12 - len(__lowerCAmelCase							))							)
  lowercase_								= torch.tensor(__lowerCAmelCase							).unsqueeze(0							)
  lowercase_								= tokenizer.encode("""This is sample 3 éàalj'-."""							)
  decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(__lowerCAmelCase							))							)
  lowercase_								= 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
  lowercase_								= encoder_input_ids
  lowercase_								= decoder_input_ids
  lowercase_								= None
  lowercase_								= None
  lowercase_								= None
  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(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)[0]
  lowercase_								= original.generator(__lowerCAmelCase							)

  lowercase_								= new_model(
      __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)[0]
  lowercase_								= new_model.generator(__lowerCAmelCase							)

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

  lowercase_								= 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__":
       UpperCAmelCase						:		Union[str, Any]			= 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.",
       )
       UpperCAmelCase						:		Union[str, Any]			= parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      raise NotImplementedError()

