245 lines
7.9 KiB
Python
245 lines
7.9 KiB
Python
import itertools
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import json
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import linecache
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import os
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import pickle
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import re
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import socket
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import string
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from collections import Counter
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from logging import getLogger
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from pathlib import Path
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from typing import Callable, Dict, Iterable, List
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import git
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import torch
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from torch.utils.data import Dataset
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from transformers import BartTokenizer, RagTokenizer, T5Tokenizer
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def encode_line(tokenizer, line, max_length, padding_side, pad_to_max_length=True, return_tensors="pt"):
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extra_kw = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) and not line.startswith(" ") else {}
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tokenizer.padding_side = padding_side
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return tokenizer(
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[line],
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max_length=max_length,
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padding="max_length" if pad_to_max_length else None,
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truncation=True,
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return_tensors=return_tensors,
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add_special_tokens=True,
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**extra_kw,
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)
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def trim_batch(
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input_ids,
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pad_token_id,
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attention_mask=None,
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):
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"""Remove columns that are populated exclusively by pad_token_id"""
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keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
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if attention_mask is None:
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return input_ids[:, keep_column_mask]
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else:
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return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
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class Seq2SeqDataset(Dataset):
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def __init__(
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self,
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tokenizer,
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data_dir,
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max_source_length,
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max_target_length,
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type_path="train",
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n_obs=None,
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src_lang=None,
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tgt_lang=None,
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prefix="",
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):
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super().__init__()
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self.src_file = Path(data_dir).joinpath(type_path + ".source")
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self.tgt_file = Path(data_dir).joinpath(type_path + ".target")
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self.src_lens = self.get_char_lens(self.src_file)
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self.max_source_length = max_source_length
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self.max_target_length = max_target_length
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assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
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self.tokenizer = tokenizer
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self.prefix = prefix
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if n_obs is not None:
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self.src_lens = self.src_lens[:n_obs]
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self.src_lang = src_lang
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self.tgt_lang = tgt_lang
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def __len__(self):
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return len(self.src_lens)
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def __getitem__(self, index) -> Dict[str, torch.Tensor]:
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index = index + 1 # linecache starts at 1
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source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
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tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
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assert source_line, f"empty source line for index {index}"
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assert tgt_line, f"empty tgt line for index {index}"
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# Need to add eos token manually for T5
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if isinstance(self.tokenizer, T5Tokenizer):
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source_line += self.tokenizer.eos_token
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tgt_line += self.tokenizer.eos_token
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# Pad source and target to the right
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source_tokenizer = (
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self.tokenizer.question_encoder if isinstance(self.tokenizer, RagTokenizer) else self.tokenizer
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)
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target_tokenizer = self.tokenizer.generator if isinstance(self.tokenizer, RagTokenizer) else self.tokenizer
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source_inputs = encode_line(source_tokenizer, source_line, self.max_source_length, "right")
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target_inputs = encode_line(target_tokenizer, tgt_line, self.max_target_length, "right")
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source_ids = source_inputs["input_ids"].squeeze()
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target_ids = target_inputs["input_ids"].squeeze()
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src_mask = source_inputs["attention_mask"].squeeze()
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return {
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"input_ids": source_ids,
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"attention_mask": src_mask,
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"decoder_input_ids": target_ids,
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}
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@staticmethod
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def get_char_lens(data_file):
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return [len(x) for x in Path(data_file).open().readlines()]
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def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
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input_ids = torch.stack([x["input_ids"] for x in batch])
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masks = torch.stack([x["attention_mask"] for x in batch])
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target_ids = torch.stack([x["decoder_input_ids"] for x in batch])
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tgt_pad_token_id = (
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self.tokenizer.generator.pad_token_id
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if isinstance(self.tokenizer, RagTokenizer)
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else self.tokenizer.pad_token_id
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)
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src_pad_token_id = (
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self.tokenizer.question_encoder.pad_token_id
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if isinstance(self.tokenizer, RagTokenizer)
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else self.tokenizer.pad_token_id
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)
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y = trim_batch(target_ids, tgt_pad_token_id)
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source_ids, source_mask = trim_batch(input_ids, src_pad_token_id, attention_mask=masks)
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batch = {
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"input_ids": source_ids,
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"attention_mask": source_mask,
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"decoder_input_ids": y,
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}
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return batch
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logger = getLogger(__name__)
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def flatten_list(summary_ids: List[List]):
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return list(itertools.chain.from_iterable(summary_ids))
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def save_git_info(folder_path: str) -> None:
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"""Save git information to output_dir/git_log.json"""
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repo_infos = get_git_info()
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save_json(repo_infos, os.path.join(folder_path, "git_log.json"))
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def save_json(content, path, indent=4, **json_dump_kwargs):
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with open(path, "w") as f:
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json.dump(content, f, indent=indent, **json_dump_kwargs)
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def load_json(path):
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with open(path) as f:
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return json.load(f)
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def get_git_info():
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repo = git.Repo(search_parent_directories=True)
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repo_infos = {
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"repo_id": str(repo),
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"repo_sha": str(repo.head.object.hexsha),
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"repo_branch": str(repo.active_branch),
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"hostname": str(socket.gethostname()),
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}
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return repo_infos
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def lmap(f: Callable, x: Iterable) -> List:
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"""list(map(f, x))"""
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return list(map(f, x))
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def pickle_save(obj, path):
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"""pickle.dump(obj, path)"""
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with open(path, "wb") as f:
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return pickle.dump(obj, f)
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def normalize_answer(s):
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"""Lower text and remove punctuation, articles and extra whitespace."""
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def remove_articles(text):
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return re.sub(r"\b(a|an|the)\b", " ", text)
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def white_space_fix(text):
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return " ".join(text.split())
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def remove_punc(text):
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exclude = set(string.punctuation)
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return "".join(ch for ch in text if ch not in exclude)
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def lower(text):
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return text.lower()
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return white_space_fix(remove_articles(remove_punc(lower(s))))
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def f1_score(prediction, ground_truth):
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prediction_tokens = normalize_answer(prediction).split()
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ground_truth_tokens = normalize_answer(ground_truth).split()
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common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
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num_same = sum(common.values())
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if num_same == 0:
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return 0
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precision = 1.0 * num_same / len(prediction_tokens)
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recall = 1.0 * num_same / len(ground_truth_tokens)
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f1 = (2 * precision * recall) / (precision + recall)
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return f1
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def exact_match_score(prediction, ground_truth):
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return normalize_answer(prediction) == normalize_answer(ground_truth)
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def calculate_exact_match(output_lns: List[str], reference_lns: List[str]) -> Dict:
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assert len(output_lns) == len(reference_lns)
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em = 0
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for hypo, pred in zip(output_lns, reference_lns):
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em += exact_match_score(hypo, pred)
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if len(output_lns) > 0:
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em /= len(output_lns)
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return {"em": em}
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def is_rag_model(model_prefix):
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return model_prefix.startswith("rag")
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def set_extra_model_params(extra_params, hparams, config):
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equivalent_param = {p: p for p in extra_params}
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# T5 models don't have `dropout` param, they have `dropout_rate` instead
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equivalent_param["dropout"] = "dropout_rate"
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for p in extra_params:
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if getattr(hparams, p, None):
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if not hasattr(config, p) and not hasattr(config, equivalent_param[p]):
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logger.info("config doesn't have a `{}` attribute".format(p))
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delattr(hparams, p)
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continue
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set_p = p if hasattr(config, p) else equivalent_param[p]
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setattr(config, set_p, getattr(hparams, p))
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delattr(hparams, p)
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return hparams, config
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