Merge pull request #2352 from huggingface/cli_tweaks

Cli tweaks

README.md

@@ -499,7 +499,7 @@ model = AutoModel.from_pretrained("username/pretrained_model")
 
 Finally, list all your files on S3:
 ```shell
-transformers-cli ls
+transformers-cli s3 ls
 # List all your S3 objects.
 ```
 

docs/source/model_sharing.md

@@ -34,7 +34,7 @@ model = AutoModel.from_pretrained("username/pretrained_model")
 
 Finally, list all your files on S3:
 ```shell
-transformers-cli ls
+transformers-cli s3 ls
 # List all your S3 objects.
 ```
 

src/transformers/__main__.py

-# coding: utf8
-
-
-def main():
-    import sys
-
-    if len(sys.argv) < 2 or sys.argv[1] not in ["convert", "train", "predict", "serve"]:
-        print(
-            "First argument to `transformers` command line interface should be one of: \n"
-            ">> convert serve train predict"
-        )
-    if sys.argv[1] == "convert":
-        from transformers.commands import convert
-
-        convert(sys.argv)
-    elif sys.argv[1] == "train":
-        from transformers.commands import train
-
-        train(sys.argv)
-    elif sys.argv[1] == "serve":
-        pass
-        # from argparse import ArgumentParser
-        # from transformers.commands.serving import ServeCommand
-        # parser = ArgumentParser('Transformers CLI tool', usage='transformers serve <command> [<args>]')
-        # commands_parser = parser.add_subparsers(help='transformers-cli command helpers')
-
-        # # Register commands
-        # ServeCommand.register_subcommand(commands_parser)
-
-        # # Let's go
-        # args = parser.parse_args()
-
-        # if not hasattr(args, 'func'):
-        #     parser.print_help()
-        #     exit(1)
-        # # Run
-        # service = args.func(args)
-        # service.run()
-
-
-if __name__ == "__main__":
-    main()

src/transformers/commands/convert.py

@@ -25,7 +25,7 @@ class ConvertCommand(BaseTransformersCLICommand):
         train_parser = parser.add_parser(
             "convert",
             help="CLI tool to run convert model from original "
-            "author checkpoints to Transformesr PyTorch checkpoints.",
+            "author checkpoints to Transformers PyTorch checkpoints.",
         )
         train_parser.add_argument("--model_type", type=str, required=True, help="Model's type.")
         train_parser.add_argument(

src/transformers/commands/user.py

@@ -9,17 +9,26 @@ from transformers.commands import BaseTransformersCLICommand
 from transformers.hf_api import HfApi, HfFolder
 
 
+UPLOAD_MAX_FILES = 15
+
+
 class UserCommands(BaseTransformersCLICommand):
     @staticmethod
     def register_subcommand(parser: ArgumentParser):
-        login_parser = parser.add_parser("login")
+        login_parser = parser.add_parser("login", help="Log in using the same credentials as on huggingface.co")
         login_parser.set_defaults(func=lambda args: LoginCommand(args))
-        whoami_parser = parser.add_parser("whoami")
+        whoami_parser = parser.add_parser("whoami", help="Find out which huggingface.co account you are logged in as.")
         whoami_parser.set_defaults(func=lambda args: WhoamiCommand(args))
-        logout_parser = parser.add_parser("logout")
+        logout_parser = parser.add_parser("logout", help="Log out")
         logout_parser.set_defaults(func=lambda args: LogoutCommand(args))
-        list_parser = parser.add_parser("ls")
-        list_parser.set_defaults(func=lambda args: ListObjsCommand(args))
+        # s3
+        s3_parser = parser.add_parser("s3", help="{ls, rm} Commands to interact with the files you upload on S3.")
+        s3_subparsers = s3_parser.add_subparsers(help="s3 related commands")
+        ls_parser = s3_subparsers.add_parser("ls")
+        ls_parser.set_defaults(func=lambda args: ListObjsCommand(args))
+        rm_parser = s3_subparsers.add_parser("rm")
+        rm_parser.add_argument("filename", type=str, help="individual object filename to delete from S3.")
+        rm_parser.set_defaults(func=lambda args: DeleteObjCommand(args))
         # upload
         upload_parser = parser.add_parser("upload")
         upload_parser.add_argument("path", type=str, help="Local path of the folder or individual file to upload.")
@@ -131,13 +140,27 @@ class ListObjsCommand(BaseUserCommand):
         print(self.tabulate(rows, headers=["Filename", "LastModified", "ETag", "Size"]))
 
 
+class DeleteObjCommand(BaseUserCommand):
+    def run(self):
+        token = HfFolder.get_token()
+        if token is None:
+            print("Not logged in")
+            exit(1)
+        try:
+            self._api.delete_obj(token, filename=self.args.filename)
+        except HTTPError as e:
+            print(e)
+            exit(1)
+        print("Done")
+
+
 class UploadCommand(BaseUserCommand):
     def walk_dir(self, rel_path):
         """
         Recursively list all files in a folder.
         """
         entries: List[os.DirEntry] = list(os.scandir(rel_path))
-        files = [(os.path.join(os.getcwd(), f.path), f.path) for f in entries if f.is_file()]  # filepath  # filename
+        files = [(os.path.join(os.getcwd(), f.path), f.path) for f in entries if f.is_file()]  # (filepath, filename)
         for f in entries:
             if f.is_dir():
                 files += self.walk_dir(f.path)
@@ -160,6 +183,14 @@ class UploadCommand(BaseUserCommand):
         else:
             raise ValueError("Not a valid file or directory: {}".format(local_path))
 
+        if len(files) > UPLOAD_MAX_FILES:
+            print(
+                "About to upload {} files to S3. This is probably wrong. Please filter files before uploading.".format(
+                    ANSI.bold(len(files))
+                )
+            )
+            exit(1)
+
         for filepath, filename in files:
             print("About to upload file {} to S3 under filename {}".format(ANSI.bold(filepath), ANSI.bold(filename)))
 

src/transformers/hf_api.py

@@ -79,7 +79,7 @@ class HfApi:
         r = requests.post(path, headers={"authorization": "Bearer {}".format(token)})
         r.raise_for_status()
 
-    def presign(self, token: str, filename) -> PresignedUrl:
+    def presign(self, token: str, filename: str) -> PresignedUrl:
         """
         Call HF API to get a presigned url to upload `filename` to S3.
         """
@@ -89,7 +89,7 @@ class HfApi:
         d = r.json()
         return PresignedUrl(**d)
 
-    def presign_and_upload(self, token: str, filename, filepath) -> str:
+    def presign_and_upload(self, token: str, filename: str, filepath: str) -> str:
         """
         Get a presigned url, then upload file to S3.
 
@@ -111,7 +111,7 @@ class HfApi:
             pf.close()
         return urls.access
 
-    def list_objs(self, token) -> List[S3Obj]:
+    def list_objs(self, token: str) -> List[S3Obj]:
         """
         Call HF API to list all stored files for user.
         """
@@ -121,6 +121,14 @@ class HfApi:
         d = r.json()
         return [S3Obj(**x) for x in d]
 
+    def delete_obj(self, token: str, filename: str):
+        """
+        Call HF API to delete a file stored by user
+        """
+        path = "{}/api/deleteObj".format(self.endpoint)
+        r = requests.delete(path, headers={"authorization": "Bearer {}".format(token)}, json={"filename": filename})
+        r.raise_for_status()
+
 
 class TqdmProgressFileReader:
     """

templates/adding_a_new_model/README.md

@@ -56,7 +56,7 @@ You can then finish the addition step by adding imports for your classes in the
 - [ ] add your PyTorch and TF 2.0 model respectively in `modeling_auto.py` and `modeling_tf_auto.py`
 - [ ] add your tokenizer in `tokenization_auto.py`
 - [ ] add your models and tokenizer to `pipeline.py`
-- [ ] add a link to your conversion script in the main conversion utility (currently in `__main__` but will be moved to the `commands` subfolder in the near future)
+- [ ] add a link to your conversion script in the main conversion utility (in `commands/convert.py`)
 - [ ] edit the PyTorch to TF 2.0 conversion script to add your model in the `convert_pytorch_checkpoint_to_tf2.py` file
 - [ ] add a mention of your model in the doc: `README.md` and the documentation itself at `docs/source/pretrained_models.rst`.
 - [ ] upload the pretrained weigths, configurations and vocabulary files.

tests/test_hf_api.py

@@ -60,6 +60,11 @@ class HfApiEndpointsTest(HfApiCommonTest):
         """
         cls._token = cls._api.login(username=USER, password=PASS)
 
+    @classmethod
+    def tearDownClass(cls):
+        for FILE_KEY, FILE_PATH in FILES:
+            cls._api.delete_obj(token=cls._token, filename=FILE_KEY)
+
     def test_whoami(self):
         user = self._api.whoami(token=self._token)
         self.assertEqual(user, USER)