






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


SCREAMING_SNAKE_CASE__		:							List[str] 				=				"""Hello world! cécé herlolip"""


SCREAMING_SNAKE_CASE__		:							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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Tuple:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			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 ,							)
     UpperCAmelCase__      :  Any				      =			torch.load(__lowerCamelCase ,							lambda __lowerCamelCase ,							__lowerCamelCase       : storage       )
     UpperCAmelCase__      :  int				      =			AbsSummarizer(__lowerCamelCase ,							torch.device("""cpu"""       ) ,							__lowerCamelCase       )
     original.eval()

     UpperCAmelCase__      :  Tuple				      =			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"""       )
     UpperCAmelCase__      :  Any				      =			BertTokenizer.from_pretrained("""bert-base-uncased"""       )

     # prepare the model inputs
     UpperCAmelCase__      :  List[Any]				      =			tokenizer.encode("""This is sample éàalj'-."""       )
     encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase       ))       )
     UpperCAmelCase__      :  Any				      =			torch.tensor(__lowerCamelCase       ).unsqueeze(0       )
     UpperCAmelCase__      :  Tuple				      =			tokenizer.encode("""This is sample 3 éàalj'-."""       )
     decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase       ))       )
     UpperCAmelCase__      :  Dict				      =			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
     UpperCAmelCase__      :  List[str]				      =			encoder_input_ids
     UpperCAmelCase__      :  List[str]				      =			decoder_input_ids
     UpperCAmelCase__      :  List[str]				      =			None
     UpperCAmelCase__      :  Tuple				      =			None
     UpperCAmelCase__      :  Optional[Any]				      =			None
     UpperCAmelCase__      :  Optional[Any]				      =			None
     UpperCAmelCase__      :  Optional[int]				      =			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
     UpperCAmelCase__      :  Any				      =			original(__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       )[0]
     UpperCAmelCase__      :  List[str]				      =			original.generator(__lowerCamelCase       )

     UpperCAmelCase__      :  List[Any]				      =			new_model(
         __lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       )[0]
     UpperCAmelCase__      :  int				      =			new_model.generator(__lowerCamelCase       )

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

     UpperCAmelCase__      :  str				      =			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__":
      SCREAMING_SNAKE_CASE__		:							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.""",
      )
      SCREAMING_SNAKE_CASE__		:							int 				=				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )













import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
    import torch.distributed.checkpoint as dist_cp
    from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
    from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase_												=	get_logger(__name__)






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         UpperCAmelCase__						=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         UpperCAmelCase__						=os.path.join(A				,							A     )
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCAmelCase__						=(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Saving model to {output_model_file}"""     )
         torch.save(A				,							A     )
         logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving model to {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": state_dict}

         dist_cp.save_state_dict(
             state_dict=A				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Model saved to {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if type(A     ) != FSDP and accelerator.process_index != 0:
            if not fsdp_plugin.sync_module_states:
               raise ValueError(
                   "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
                   "initializing FSDP object"     )
            return
         UpperCAmelCase__						=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCAmelCase__						=(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=(
             os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
             if F"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading model from {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=A				,							storage_reader=dist_cp.FileSystemReader(A     )				,							planner=DefaultLoadPlanner()				,							)
         UpperCAmelCase__						=state_dict["model"]
         logger.info(F"""Model loaded from {ckpt_dir}"""     )
      model.load_state_dict(A     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=FSDP.optim_state_dict(A				,							A     )
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            UpperCAmelCase__						=(
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            UpperCAmelCase__						=os.path.join(A				,							A     )
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
         dist_cp.save_state_dict(
             state_dict={"optimizer": optim_state}				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         UpperCAmelCase__						=None
         # below check should work but currently it isn't working (mostly opytorch issue),
         # in the meantime disabling it at the cost of excess memory usage
         # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
         UpperCAmelCase__						=(
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=(
             os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
             if F"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading Optimizer from {ckpt_dir}"""     )
         UpperCAmelCase__						=load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()				,							optimizer_key="optimizer"				,							storage_reader=dist_cp.FileSystemReader(A     )				,							)
         UpperCAmelCase__						=optim_state["optimizer"]
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
      UpperCAmelCase__						=FSDP.optim_state_dict_to_load(A				,							A				,							A     )
      optimizer.load_state_dict(A     )



