Remove use of deprected method in Trainer HP search (#8996)

.circleci/deploy.sh

@@ -52,5 +52,4 @@ deploy_doc "4b3ee9c" v3.1.0
 deploy_doc "3ebb1b3" v3.2.0
 deploy_doc "0613f05" v3.3.1
 deploy_doc "eb0e0ce" v3.4.0
-deploy_doc "818878d" v3.5.1
-deploy_doc "c781171" # v4.0.0 Latest stable release
+deploy_doc "818878d" # v3.5.1 Latest stable release

.github/conda/build.sh

@@ -1 +0,0 @@
-$PYTHON setup.py install     # Python command to install the script.

.github/conda/meta.yaml

@@ -1,48 +0,0 @@
-{% set name = "transformers" %}
-
-package:
-  name: "{{ name|lower }}"
-  version: "{{ TRANSFORMERS_VERSION }}"
-
-source:
-  path: ../../
-
-build:
-  noarch: python
-
-requirements:
-  host:
-    - python
-    - pip
-    - numpy
-    - dataclasses
-    - packaging
-    - filelock
-    - requests
-    - tqdm >=4.27
-    - sacremoses
-    - regex !=2019.12.17
-    - protobuf
-    - tokenizers ==0.9.4
-  run:
-    - python
-    - numpy
-    - dataclasses
-    - packaging
-    - filelock
-    - requests
-    - tqdm >=4.27
-    - sacremoses
-    - regex !=2019.12.17
-    - protobuf
-    - tokenizers ==0.9.4
-
-test:
-  imports:
-    - transformers
-
-about:
-  home: https://huggingface.co
-  license: Apache License 2.0
-  license_file: LICENSE
-  summary: "🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0."

.github/workflows/release-conda.yml

@@ -1,46 +0,0 @@
-name: Release - Conda
-
-on:
-  push:
-    tags:
-      - v*
-    branches:
-      - v4.0.0-release
-
-
-env:
-  ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_API_TOKEN }}
-
-jobs:
-  build_and_package:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash -l {0}
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v1
-
-      - name: Install miniconda
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          auto-activate-base: false
-          activate-environment: "build-transformers"
-          channels: huggingface
-
-      - name: Setup conda env
-        run: |
-          conda install -c defaults anaconda-client conda-build
-
-      - name: Extract version
-        run: echo "TRANSFORMERS_VERSION=`python setup.py --version`" >> $GITHUB_ENV
-
-      - name: Build conda packages
-        run: |
-          conda info
-          conda build .github/conda
-
-      - name: Upload to Anaconda
-        run: anaconda upload `conda build .github/conda --output` --force

README.md

@@ -137,16 +137,14 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 
 ## Installation
 
-### With pip
-
 This repository is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for [examples](https://github.com/huggingface/transformers/tree/master/examples)) and TensorFlow 2.0.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
 First, create a virtual environment with the version of Python you're going to use and activate it.
 
-Then, you will need to install at least one of TensorFlow 2.0, PyTorch or Flax.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform and/or [Flax installation page](https://github.com/google/flax#quick-install).
+Then, you will need to install one of, or both, TensorFlow 2.0 and PyTorch.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
 
 When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
 
@@ -156,18 +154,6 @@ pip install transformers
 
 If you'd like to play with the examples, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).
 
-### With conda
-
-Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
-
-🤗 Transformers can be installed using conda as follows:
-
-```shell script
-conda install -c huggingface transformers
-```
-
-Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
-
 ## Models architectures
 
 🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/transformers/model_summary.html) for a high-level summary of each them):

docs/source/_static/js/custom.js

@@ -1,15 +1,14 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.0.0"
+const stableVersion = "v3.5.0"
 // Dictionary doc folder to label
 const versionMapping = {
     "master": "master",
-    "v4.0.0": "v4.0.0",
-    "v3.5.1": "v3.5.0/v3.5.1",
+    "": "v3.5.0/v3.5.1",
     "v3.4.0": "v3.4.0",
     "v3.3.1": "v3.3.0/v3.3.1",
     "v3.2.0": "v3.2.0",
-    "v3.1.0": "v3.1.0",
+    "v3.1.0": "v3.1.0 (stable)",
     "v3.0.2": "v3.0.0/v3.0.1/v3.0.2",
     "v2.11.0": "v2.11.0",
     "v2.10.0": "v2.10.0",

docs/source/installation.md

@@ -12,10 +12,9 @@ must install it from source.
 ## Installation with pip
 
 First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), 
-[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or 
-[Flax installation page](https://github.com/google/flax#quick-install)
-regarding the specific install command for your platform.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) 
+and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific 
+install command for your platform.
 
 When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
 
@@ -35,12 +34,6 @@ or 🤗 Transformers and TensorFlow 2.0 in one line with:
 pip install transformers[tf-cpu]
 ```
 
-or 🤗 Transformers and Flax in one line with:
-
-```bash
-pip install transformers[flax]
-```
-
 To check 🤗 Transformers is properly installed, run the following command:
 
 ```bash
@@ -73,19 +66,6 @@ python -c "from transformers import pipeline; print(pipeline('sentiment-analysis
 
 to check 🤗 Transformers is properly installed.
 
-
-## With conda
-
-Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
-
-🤗 Transformers can be installed using conda as follows:
-
-```
-conda install -c huggingface transformers
-```
-
-Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
-
 ## Caching models
 
 This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with

src/transformers/models/auto/tokenization_auto.py

@@ -342,7 +342,13 @@ class AutoTokenizer:
             if tokenizer_class_fast and (use_fast or tokenizer_class_py is None):
                 return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
             else:
-                return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+                if tokenizer_class_py is not None:
+                    return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+                else:
+                    raise ValueError(
+                        "This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed "
+                        "in order to use this tokenizer."
+                    )
 
         raise ValueError(
             "Unrecognized configuration class {} to build an AutoTokenizer.\n"

src/transformers/trainer.py

@@ -538,7 +538,7 @@ class Trainer:
             self.args.output_dir = checkpoint_dir
             output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
             self.save_model(output_dir)
-            if self.is_world_master():
+            if self.is_world_process_zero():
                 self.state.save_to_json(os.path.join(output_dir, "trainer_state.json"))
                 torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                 torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))