🧑‍💻 More Flake changes, Isort

Fixed a lot of bad code.

.pre-commit-config.yaml

@@ -3,6 +3,7 @@ repos:
     rev: 23.7.0
     hooks:
       - id: black
+        name: black
         # It is recommended to specify the latest version of Python
         # supported by your project here, or alternatively use
         # pre-commit's default_language_version, see
@@ -13,11 +14,15 @@ repos:
     rev: 6.1.0  # Use the ref you want to pin
     hooks:
     -   id: flake8
+        name: flake8
         #additional_dependencies: [flake8-import-order]
         args: ['--select=F,E9']
-  - repo: https://github.com/asottile/reorder-python-imports
-    rev: v3.10.0
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
     hooks:
-    -   id: reorder-python-imports
-        args: ['--py38-plus']
+      - id: isort
+        name: isort
+        args: ['--profile', 'black']
+
+
 

base_bert.py

@@ -1,11 +1,18 @@
+import os
 import re
+from typing import Optional, Union
 
-from torch import device
-from torch import dtype
+import torch
+from torch import dtype, nn
 
-from config import BertConfig
-from config import PretrainedConfig
-from utils import *
+from config import BertConfig, PretrainedConfig
+from utils import (
+    WEIGHTS_NAME,
+    cached_path,
+    get_parameter_dtype,
+    hf_bucket_url,
+    is_remote_url,
+)
 
 
 class BertPreTrainedModel(nn.Module):
@@ -102,8 +109,7 @@ def from_pretrained(
                     local_files_only=local_files_only,
                     use_auth_token=use_auth_token,
                 )
-            except EnvironmentError as err:
-                # logger.error(err)
+            except EnvironmentError:
                 msg = (
                     f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
                     f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
@@ -253,10 +259,4 @@ def load(module: nn.Module, prefix=""):
             }
             return model, loading_info
 
-        if hasattr(config, "xla_device") and config.xla_device and is_torch_tpu_available():
-            import torch_xla.core.xla_model as xm
-
-            model = xm.send_cpu_data_to_device(model, xm.xla_device())
-            model.to(xm.xla_device())
-
         return model

bert.py

@@ -1,17 +1,11 @@
 import math
-from typing import Callable
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from base_bert import BertPreTrainedModel
-from utils import *
+from utils import Tensor, get_extended_attention_mask
 
 
 class BertSelfAttention(nn.Module):

classifier.py

@@ -8,18 +8,15 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import f1_score
+from sklearn.metrics import accuracy_score, f1_score
 from torch import nn
-from torch.utils.data import DataLoader
-from torch.utils.data import Dataset
+from torch.utils.data import DataLoader, Dataset
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
 from AttentionLayer import AttentionLayer
 from bert import BertModel
-from optimizer import AdamW
-from optimizer import SophiaG
+from optimizer import AdamW, SophiaG
 from tokenizer import BertTokenizer
 
 # change it with respect to the original model
@@ -189,7 +186,7 @@ def model_eval(dataloader, model, device):
     y_pred = []
     sents = []
     sent_ids = []
-    for step, batch in enumerate(tqdm(dataloader, desc=f"eval", disable=TQDM_DISABLE)):
+    for step, batch in enumerate(tqdm(dataloader, desc="eval", disable=TQDM_DISABLE)):
         b_ids, b_mask, b_labels, b_sents, b_sent_ids = (
             batch["token_ids"],
             batch["attention_mask"],
@@ -222,7 +219,7 @@ def model_test_eval(dataloader, model, device):
     y_pred = []
     sents = []
     sent_ids = []
-    for step, batch in enumerate(tqdm(dataloader, desc=f"eval", disable=TQDM_DISABLE)):
+    for step, batch in enumerate(tqdm(dataloader, desc="eval", disable=TQDM_DISABLE)):
         b_ids, b_mask, b_sents, b_sent_ids = (
             batch["token_ids"],
             batch["attention_mask"],
@@ -424,12 +421,12 @@ def test(args):
         print("DONE Test")
         with open(args.dev_out, "w+") as f:
             print(f"dev acc :: {dev_acc :.3f}")
-            f.write(f"id \t Predicted_Sentiment \n")
+            f.write("id \t Predicted_Sentiment \n")
             for p, s in zip(dev_sent_ids, dev_pred):
                 f.write(f"{p} , {s} \n")
 
         with open(args.test_out, "w+") as f:
-            f.write(f"id \t Predicted_Sentiment \n")
+            f.write("id \t Predicted_Sentiment \n")
             for p, s in zip(test_sent_ids, test_pred):
                 f.write(f"{p} , {s} \n")
 

config.py

@@ -1,14 +1,8 @@
 import json
 import os
-from typing import Any
-from typing import Dict
-from typing import Tuple
-from typing import Union
-
-from utils import cached_path
-from utils import CONFIG_NAME
-from utils import hf_bucket_url
-from utils import is_remote_url
+from typing import Any, Dict, Tuple, Union
+
+from utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
 
 
 class PretrainedConfig(object):

evaluation.py

@@ -15,16 +15,17 @@
 """
 import numpy as np
 import torch
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import f1_score
+from sklearn.metrics import accuracy_score, f1_score
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-from datasets import load_multitask_data
-from datasets import SentenceClassificationDataset
-from datasets import SentenceClassificationTestDataset
-from datasets import SentencePairDataset
-from datasets import SentencePairTestDataset
+from datasets import (
+    SentenceClassificationDataset,
+    SentenceClassificationTestDataset,
+    SentencePairDataset,
+    SentencePairTestDataset,
+    load_multitask_data,
+)
 
 TQDM_DISABLE = False
 

multitask_classifier.py

@@ -18,13 +18,13 @@
 
 from AttentionLayer import AttentionLayer
 from bert import BertModel
-from datasets import load_multitask_data
-from datasets import SentenceClassificationDataset
-from datasets import SentencePairDataset
-from evaluation import model_eval_multitask
-from evaluation import test_model_multitask
-from optimizer import AdamW
-from optimizer import SophiaH
+from datasets import (
+    SentenceClassificationDataset,
+    SentencePairDataset,
+    load_multitask_data,
+)
+from evaluation import model_eval_multitask, test_model_multitask
+from optimizer import AdamW, SophiaH
 
 TQDM_DISABLE = False
 

optimizer.py

@@ -1,6 +1,4 @@
-from typing import Callable
-from typing import Iterable
-from typing import Tuple
+from typing import Callable, Iterable, Tuple
 
 import torch
 from torch.optim import Optimizer

tokenizer.py

@@ -5,30 +5,27 @@
 import os
 import re
 import unicodedata
-from collections import OrderedDict
-from collections import UserDict
+from collections import OrderedDict, UserDict
 from contextlib import contextmanager
 from enum import Enum
-from typing import Any
-from typing import Dict
-from typing import List
-from typing import NamedTuple
-from typing import Optional
-from typing import overload
-from typing import Sequence
-from typing import Tuple
-from typing import Union
+from typing import (
+    Any,
+    Dict,
+    List,
+    NamedTuple,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    overload,
+)
 
 import numpy as np
 import requests
 from tokenizers import AddedToken
 from tokenizers import Encoding as EncodingFast
 
-from utils import cached_path
-from utils import hf_bucket_url
-from utils import is_remote_url
-from utils import is_torch_available
-
+from utils import cached_path, hf_bucket_url, is_remote_url, is_torch_available
 
 VERY_LARGE_INTEGER = int(
     1e30

utils.py

@@ -13,15 +13,9 @@
 from hashlib import sha256
 from io import UnsupportedOperation
 from pathlib import Path
-from typing import BinaryIO
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
+from typing import BinaryIO, Dict, List, Optional, Tuple, Union
 from urllib.parse import urlparse
-from zipfile import is_zipfile
-from zipfile import ZipFile
+from zipfile import ZipFile, is_zipfile
 
 import importlib_metadata
 import requests