Update repo to isort v5 (#6686)

* Run new isort

* More changes

* Update CI, CONTRIBUTING and benchmarks

.circleci/config.yml

@@ -235,8 +235,7 @@ jobs:
                       - v0.3-code_quality-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            # we need a version of isort with https://github.com/timothycrosley/isort/pull/1000
-            - run: pip install git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
+            - run: pip install isort
             - run: pip install .[tf,torch,quality]
             - save_cache:
                   key: v0.3-code_quality-{{ checksum "setup.py" }}

CONTRIBUTING.md

@@ -134,12 +134,6 @@ Follow these steps to start contributing:
    it with `pip uninstall transformers` before reinstalling it in editable
    mode with the `-e` flag.)
 
-   Right now, we need an unreleased version of `isort` to avoid a
-   [bug](https://github.com/timothycrosley/isort/pull/1000):
-
-   ```bash
-   $ pip install -U git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
-   ```
 5. Develop the features on your branch.
 
    As you work on the features, you should make sure that the test suite

Makefile

@@ -4,7 +4,7 @@
 
 quality:
 	black --check --line-length 119 --target-version py35 examples templates tests src utils
-	isort --check-only --recursive examples templates tests src utils
+	isort --check-only examples templates tests src utils
 	flake8 examples templates tests src utils
 	python utils/check_repo.py
 
@@ -12,7 +12,7 @@ quality:
 
 style:
 	black --line-length 119 --target-version py35 examples templates tests src utils
-	isort --recursive examples templates tests src utils
+	isort examples templates tests src utils
 
 # Run tests for the library
 

examples/adversarial/utils_hans.py

@@ -20,8 +20,8 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import tqdm
-from filelock import FileLock
 
+from filelock import FileLock
 from transformers import (
     BartTokenizer,
     BartTokenizerFast,

examples/multiple-choice/utils_multiple_choice.py

@@ -26,8 +26,8 @@ from enum import Enum
 from typing import List, Optional
 
 import tqdm
-from filelock import FileLock
 
+from filelock import FileLock
 from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
 
 

examples/seq2seq/bertabs/run_summarization.py

@@ -44,9 +44,10 @@ def evaluate(args):
         reference_summaries = []
         generated_summaries = []
 
-        import rouge
         import nltk
 
+        import rouge
+
         nltk.download("punkt")
         rouge_evaluator = rouge.Rouge(
             metrics=["rouge-n", "rouge-l"],

examples/seq2seq/distillation.py

@@ -15,27 +15,27 @@ from transformers import BartConfig, BartForConditionalGeneration, MBartTokenize
 
 try:
     from .finetune import SummarizationModule, TranslationModule
-    from .initialization_utils import init_student, copy_layers
+    from .finetune import main as ft_main
+    from .initialization_utils import copy_layers, init_student
     from .utils import (
-        use_task_specific_params,
-        pickle_load,
-        freeze_params,
-        assert_all_frozen,
         any_requires_grad,
+        assert_all_frozen,
         calculate_bleu_score,
+        freeze_params,
+        pickle_load,
+        use_task_specific_params,
     )
-    from .finetune import main as ft_main
 except ImportError:
     from finetune import SummarizationModule, TranslationModule
     from finetune import main as ft_main
-    from initialization_utils import init_student, copy_layers
+    from initialization_utils import copy_layers, init_student
     from utils import (
-        use_task_specific_params,
-        pickle_load,
-        freeze_params,
-        assert_all_frozen,
         any_requires_grad,
+        assert_all_frozen,
         calculate_bleu_score,
+        freeze_params,
+        pickle_load,
+        use_task_specific_params,
     )
 
 

examples/seq2seq/finetune.py

@@ -17,44 +17,43 @@ from transformers import MarianTokenizer, MBartTokenizer, T5ForConditionalGenera
 
 
 try:
+    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
     from .utils import (
+        ROUGE_KEYS,
+        Seq2SeqDataset,
+        TranslationDataset,
         assert_all_frozen,
-        use_task_specific_params,
-        lmap,
+        calculate_bleu_score,
+        calculate_rouge,
         flatten_list,
-        pickle_save,
-        save_git_info,
-        save_json,
         freeze_params,
-        calculate_rouge,
         get_git_info,
-        ROUGE_KEYS,
-        calculate_bleu_score,
-        Seq2SeqDataset,
-        TranslationDataset,
         label_smoothed_nll_loss,
+        lmap,
+        pickle_save,
+        save_git_info,
+        save_json,
+        use_task_specific_params,
     )
-
-    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
 except ImportError:
+    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
     from utils import (
+        ROUGE_KEYS,
         Seq2SeqDataset,
         TranslationDataset,
         assert_all_frozen,
-        use_task_specific_params,
-        lmap,
+        calculate_bleu_score,
+        calculate_rouge,
         flatten_list,
-        pickle_save,
-        save_git_info,
-        save_json,
         freeze_params,
-        calculate_rouge,
         get_git_info,
-        ROUGE_KEYS,
-        calculate_bleu_score,
         label_smoothed_nll_loss,
+        lmap,
+        pickle_save,
+        save_git_info,
+        save_json,
+        use_task_specific_params,
     )
-    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
 
 logger = logging.getLogger(__name__)
 

examples/seq2seq/run_eval.py

@@ -9,9 +9,9 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 
 try:
-    from .utils import calculate_rouge, use_task_specific_params, calculate_bleu_score, trim_batch
+    from .utils import calculate_bleu_score, calculate_rouge, trim_batch, use_task_specific_params
 except ImportError:
-    from utils import calculate_rouge, use_task_specific_params, calculate_bleu_score, trim_batch
+    from utils import calculate_bleu_score, calculate_rouge, trim_batch, use_task_specific_params
 
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 

examples/test_examples.py

@@ -35,8 +35,8 @@ sys.path.extend(SRC_DIRS)
 if SRC_DIRS is not None:
     import run_generation
     import run_glue
-    import run_pl_glue
     import run_language_modeling
+    import run_pl_glue
     import run_squad
 
 

examples/token-classification/utils_ner.py

@@ -23,7 +23,6 @@ from enum import Enum
 from typing import List, Optional, Union
 
 from filelock import FileLock
-
 from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
 
 

setup.cfg

 [isort]
+default_section = FIRSTPARTY
 ensure_newline_before_comments = True
 force_grid_wrap = 0
 include_trailing_comma = True

setup.py

@@ -91,12 +91,7 @@ extras["all"] = extras["serving"] + ["tensorflow", "torch"]
 extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator", "psutil"]
 # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
 extras["docs"] = ["recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rtd-theme==0.4.3", "sphinx-copybutton"]
-extras["quality"] = [
-    "black",
-    # "isort",
-    "isort @ git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort",
-    "flake8",
-]
+extras["quality"] = ["black", "isort >= 5", "flake8"]
 extras["dev"] = extras["testing"] + extras["quality"] + extras["ja"] + ["scikit-learn", "tensorflow", "torch"]
 
 setup(

src/transformers/benchmark/benchmark.py

@@ -22,14 +22,9 @@ import logging
 import timeit
 from typing import Callable, Optional
 
-from transformers import (
-    MODEL_MAPPING,
-    MODEL_WITH_LM_HEAD_MAPPING,
-    PretrainedConfig,
-    is_py3nvml_available,
-    is_torch_available,
-)
-
+from ..configuration_utils import PretrainedConfig
+from ..file_utils import is_py3nvml_available, is_torch_available
+from ..modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
 from .benchmark_utils import (
     Benchmark,
     Memory,
@@ -42,6 +37,7 @@ from .benchmark_utils import (
 
 if is_torch_available():
     import torch
+
     from .benchmark_args import PyTorchBenchmarkArguments
 
 

src/transformers/benchmark/benchmark_tf.py

@@ -24,14 +24,9 @@ import timeit
 from functools import wraps
 from typing import Callable, Optional
 
-from transformers import (
-    TF_MODEL_MAPPING,
-    TF_MODEL_WITH_LM_HEAD_MAPPING,
-    PretrainedConfig,
-    is_py3nvml_available,
-    is_tf_available,
-)
-
+from ..configuration_utils import PretrainedConfig
+from ..file_utils import is_py3nvml_available, is_tf_available
+from ..modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
 from .benchmark_utils import (
     Benchmark,
     Memory,
@@ -44,9 +39,10 @@ from .benchmark_utils import (
 
 if is_tf_available():
     import tensorflow as tf
-    from .benchmark_args_tf import TensorFlowBenchmarkArguments
     from tensorflow.python.framework.errors_impl import ResourceExhaustedError
 
+    from .benchmark_args_tf import TensorFlowBenchmarkArguments
+
 if is_py3nvml_available():
     import py3nvml.py3nvml as nvml
 

src/transformers/commands/serving.py

@@ -8,11 +8,11 @@ from transformers.pipelines import SUPPORTED_TASKS, pipeline
 
 
 try:
-    from uvicorn import run
-    from fastapi import FastAPI, HTTPException, Body
+    from fastapi import Body, FastAPI, HTTPException
     from fastapi.routing import APIRoute
     from pydantic import BaseModel
     from starlette.responses import JSONResponse
+    from uvicorn import run
 
     _serve_dependencies_installed = True
 except (ImportError, AttributeError):

src/transformers/commands/user.py

@@ -5,7 +5,6 @@ from getpass import getpass
 from typing import List, Union
 
 from requests.exceptions import HTTPError
-
 from transformers.commands import BaseTransformersCLICommand
 from transformers.hf_api import HfApi, HfFolder
 

src/transformers/convert_graph_to_onnx.py

@@ -273,7 +273,9 @@ def convert_tensorflow(nlp: Pipeline, opset: int, output: Path):
 
     try:
         import tensorflow as tf
-        from keras2onnx import convert_keras, save_model, __version__ as k2ov
+
+        from keras2onnx import __version__ as k2ov
+        from keras2onnx import convert_keras, save_model
 
         print(f"Using framework TensorFlow: {tf.version.VERSION}, keras2onnx: {k2ov}")
 
@@ -340,7 +342,7 @@ def optimize(onnx_model_path: Path) -> Path:
     Returns: Path where the optimized model binary description has been saved
 
     """
-    from onnxruntime import SessionOptions, InferenceSession
+    from onnxruntime import InferenceSession, SessionOptions
 
     # Generate model name with suffix "optimized"
     opt_model_path = generate_identified_filename(onnx_model_path, "-optimized")
@@ -364,7 +366,7 @@ def quantize(onnx_model_path: Path) -> Path:
     """
     try:
         import onnx
-        from onnxruntime.quantization import quantize, QuantizationMode
+        from onnxruntime.quantization import QuantizationMode, quantize
 
         onnx_model = onnx.load(onnx_model_path.as_posix())
 

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -78,28 +78,29 @@ from transformers.file_utils import hf_bucket_url
 
 
 if is_torch_available():
-    import torch
     import numpy as np
+    import torch
+
     from transformers import (
+        AlbertForPreTraining,
         BertForPreTraining,
         BertForQuestionAnswering,
         BertForSequenceClassification,
+        CamembertForMaskedLM,
+        CTRLLMHeadModel,
+        DistilBertForMaskedLM,
+        DistilBertForQuestionAnswering,
+        ElectraForPreTraining,
+        FlaubertWithLMHeadModel,
         GPT2LMHeadModel,
-        XLNetLMHeadModel,
-        XLMWithLMHeadModel,
-        XLMRobertaForMaskedLM,
-        TransfoXLLMHeadModel,
         OpenAIGPTLMHeadModel,
         RobertaForMaskedLM,
         RobertaForSequenceClassification,
-        CamembertForMaskedLM,
-        FlaubertWithLMHeadModel,
-        DistilBertForMaskedLM,
-        DistilBertForQuestionAnswering,
-        CTRLLMHeadModel,
-        AlbertForPreTraining,
         T5ForConditionalGeneration,
-        ElectraForPreTraining,
+        TransfoXLLMHeadModel,
+        XLMRobertaForMaskedLM,
+        XLMWithLMHeadModel,
+        XLNetLMHeadModel,
     )