


"""simple docstring"""

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase__					=logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__    :
      a			:  str           =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
      a			:  str           =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
      a			:  int           =		field(
          default=1_28      , metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }      , )
      a			:  bool           =		field(
          default=_a      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.task_name.lower()






class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		"""train"""
      a			:  List[str]           =		"""dev"""
      a			:  Optional[int]           =		"""test"""






class  lowerCamelCase__    (		_a     ):
      a			:  GlueDataTrainingArguments
      a			:  str
      a			:  List[InputFeatures]
      def __init__(		self						:						Optional[int]   ,							A_						:						GlueDataTrainingArguments   ,							A_						:						PreTrainedTokenizerBase   ,							A_						:						Optional[int] = None   ,							A_						:						Union[str, Split] = Split.train   ,							A_						:						Optional[str] = None   ,							):






       '''simple docstring'''


       warnings.warn(
           """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
           """library. You can have a look at this example script for pointers: """
           """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""   ,							A_   ,							)
       __lowercase						=						args
       __lowercase						=						glue_processors[args.task_name]()
       __lowercase						=						glue_output_modes[args.task_name]
       if isinstance(A_   ,							A_				):
        try:
         __lowercase						=						Split[mode]
        except KeyError:
         raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
       __lowercase						=						os.path.join(
           cache_dir if cache_dir is not None else args.data_dir   ,							F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''   ,							)
       __lowercase						=						self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        __lowercase				,		__lowercase						=						label_list[2], label_list[1]
       __lowercase						=						label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       __lowercase						=						cached_features_file + """.lock"""
       with FileLock(A_				):
        if os.path.exists(A_				) and not args.overwrite_cache:
         __lowercase						=						time.time()
         __lowercase						=						torch.load(A_				)
         logger.info(
             F'''Loading features from cached file {cached_features_file} [took %.3f s]'''   ,							time.time() - start				)
        else:
         logger.info(F'''Creating features from dataset file at {args.data_dir}'''				)

         if mode == Split.dev:
          __lowercase						=						self.processor.get_dev_examples(args.data_dir				)
         elif mode == Split.test:
          __lowercase						=						self.processor.get_test_examples(args.data_dir				)
         else:
          __lowercase						=						self.processor.get_train_examples(args.data_dir				)
         if limit_length is not None:
          __lowercase						=						examples[:limit_length]
         __lowercase						=						glue_convert_examples_to_features(
             A_   ,							A_   ,							max_length=args.max_seq_length   ,							label_list=A_   ,							output_mode=self.output_mode   ,							)
         __lowercase						=						time.time()
         torch.save(self.features   ,							A_				)
         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
         logger.info(
             F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''				)
      def __len__(		self						:						int				):






       '''simple docstring'''


       return len(self.features				)
      def __getitem__(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       return self.features[i]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self.label_list




"""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__					=logging.getLogger(__name__)


UpperCAmelCase__					="Hello world! cécé herlolip"


UpperCAmelCase__					=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_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







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

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

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

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

 __lowercase						=						torch.allclose(UpperCamelCase__					, UpperCamelCase__					, 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__					=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__					=parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )
