[Doc]: fix various typos in different files (#3499)

Author: didier-durand

docs/source/en/guides/cli.md

@@ -4,7 +4,7 @@ rendered properly in your Markdown viewer.
 
 # Command Line Interface (CLI)
 
-The `huggingface_hub` Python package comes with a built-in CLI called `hf`. This tool allows you to interact with the Hugging Face Hub directly from a terminal. For example, you can login to your account, create a repository, upload and download files, etc. It also comes with handy features to configure your machine or manage your cache. In this guide, we will have a look at the main features of the CLI and how to use them.
+The `huggingface_hub` Python package comes with a built-in CLI called `hf`. This tool allows you to interact with the Hugging Face Hub directly from a terminal. For example, you can log in to your account, create a repository, upload and download files, etc. It also comes with handy features to configure your machine or manage your cache. In this guide, we will have a look at the main features of the CLI and how to use them.
 
 > [!TIP]
 > This guide covers the most important features of the `hf` CLI.
@@ -174,7 +174,7 @@ hf download --help
 
 ### Download a single file
 
-To download a single file from a repo, simply provide the repo_id and filename as follow:
+To download a single file from a repo, simply provide the repo_id and filename as follows:
 
 ```bash
 >>> hf download gpt2 config.json

docs/source/en/guides/download.md

@@ -68,7 +68,7 @@ Note that it is used internally by [`hf_hub_download`].
 ## Download an entire repository
 
 [`snapshot_download`] downloads an entire repository at a given revision. It uses internally [`hf_hub_download`] which
-means all downloaded files are also cached on your local disk. Downloads are made concurrently to speed-up the process.
+means all downloaded files are also cached on your local disk. Downloads are made concurrently to speed up the process.
 
 To download a whole repository, just pass the `repo_id` and `repo_type`:
 

docs/source/en/package_reference/environment_variables.md

@@ -125,7 +125,7 @@ If `HF_HUB_OFFLINE=1` is set as environment variable and you call any method of
 
 ### HF_HUB_DISABLE_IMPLICIT_TOKEN
 
-Authentication is not mandatory for every requests to the Hub. For instance, requesting
+Authentication is not mandatory for every request to the Hub. For instance, requesting
 details about `"gpt2"` model does not require to be authenticated. However, if a user is
 [logged in](../package_reference/login), the default behavior will be to always send the token
 in order to ease user experience (never get a HTTP 401 Unauthorized) when accessing private or gated repositories. For privacy, you can
@@ -138,7 +138,7 @@ would need to explicitly pass `token=True` argument in your script.
 
 ### HF_HUB_DISABLE_PROGRESS_BARS
 
-For time consuming tasks, `huggingface_hub` displays a progress bar by default (using tqdm).
+For time-consuming tasks, `huggingface_hub` displays a progress bar by default (using tqdm).
 You can disable all the progress bars at once by setting `HF_HUB_DISABLE_PROGRESS_BARS=1`.
 
 ### HF_HUB_DISABLE_SYMLINKS_WARNING

src/huggingface_hub/_inference_endpoints.py

@@ -368,7 +368,7 @@ def scale_to_zero(self) -> "InferenceEndpoint":
         """Scale Inference Endpoint to zero.
 
         An Inference Endpoint scaled to zero will not be charged. It will be resumed on the next request to it, with a
-        cold start delay. This is different than pausing the Inference Endpoint with [`InferenceEndpoint.pause`], which
+        cold start delay. This is different from pausing the Inference Endpoint with [`InferenceEndpoint.pause`], which
         would require a manual resume with [`InferenceEndpoint.resume`].
 
         This is an alias for [`HfApi.scale_to_zero_inference_endpoint`]. The current object is mutated in place with the

src/huggingface_hub/_login.py

@@ -123,7 +123,7 @@ def logout(token_name: Optional[str] = None) -> None:
 
     Args:
         token_name (`str`, *optional*):
-            Name of the access token to logout from. If `None`, will logout from all saved access tokens.
+            Name of the access token to logout from. If `None`, will log out from all saved access tokens.
     Raises:
         [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
             If the access token name is not found.

src/huggingface_hub/_webhooks_server.py

@@ -50,7 +50,7 @@ class WebhooksServer:
     It is recommended to accept [`WebhookPayload`] as the first argument of the webhook function. It is a Pydantic
     model that contains all the information about the webhook event. The data will be parsed automatically for you.
 
-    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to setup your
+    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to set up your
     WebhooksServer and deploy it on a Space.
 
     > [!WARNING]
@@ -231,7 +231,7 @@ def webhook_endpoint(path: Optional[str] = None) -> Callable:
     you can use [`WebhooksServer`] directly. You can register multiple webhook endpoints (to the same server) by using
     this decorator multiple times.
 
-    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to setup your
+    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to set up your
     server and deploy it on a Space.
 
     > [!WARNING]

src/huggingface_hub/constants.py

@@ -233,7 +233,7 @@ def _as_int(value: Optional[str]) -> Optional[int]:
 # Used to override the get request timeout on a system level
 HF_HUB_DOWNLOAD_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_DOWNLOAD_TIMEOUT")) or DEFAULT_DOWNLOAD_TIMEOUT
 
-# Allows to add information about the requester in the user-agent (eg. partner name)
+# Allows to add information about the requester in the user-agent (e.g. partner name)
 HF_HUB_USER_AGENT_ORIGIN: Optional[str] = os.environ.get("HF_HUB_USER_AGENT_ORIGIN")
 
 # If OAuth didn't work after 2 redirects, there's likely a third-party cookie issue in the Space iframe view.

src/huggingface_hub/serialization/_dduf.py

@@ -202,7 +202,7 @@ def export_entries_as_dduf(
         ... # ... do some work with the pipeline
 
         >>> def as_entries(pipe: DiffusionPipeline) -> Generator[tuple[str, bytes], None, None]:
-        ...     # Build an generator that yields the entries to add to the DDUF file.
+        ...     # Build a generator that yields the entries to add to the DDUF file.
         ...     # The first element of the tuple is the filename in the DDUF archive (must use UNIX separator!). The second element is the content of the file.
         ...     # Entries will be evaluated lazily when the DDUF file is created (only 1 entry is loaded in memory at a time)
         ...     yield "vae/config.json", pipe.vae.to_json_string().encode()

src/huggingface_hub/serialization/_torch.py

@@ -710,7 +710,7 @@ def _get_unique_id(tensor: "torch.Tensor") -> Union[int, tuple[Any, ...]]:
         pass
 
     if tensor.device.type == "xla" and is_torch_tpu_available():
-        # NOTE: xla tensors dont have storage
+        # NOTE: xla tensors don't have storage
         # use some other unique id to distinguish.
         # this is a XLA tensor, it must be created using torch_xla's
         # device. So the following import is safe:
@@ -761,7 +761,7 @@ def get_torch_storage_size(tensor: "torch.Tensor") -> int:
             attrs, _ = tensor.__tensor_flatten__()  # type: ignore[attr-defined]
             return sum(get_torch_storage_size(getattr(tensor, attr)) for attr in attrs)
     except ImportError:
-        # for torch version less than 2.1, we can fallback to original implementation
+        # for torch version less than 2.1, we can fall back to original implementation
         pass
 
     try:
@@ -808,7 +808,7 @@ def storage_ptr(tensor: "torch.Tensor") -> Union[int, tuple[Any, ...]]:
         if is_traceable_wrapper_subclass(tensor):
             return _get_unique_id(tensor)  # type: ignore
     except ImportError:
-        # for torch version less than 2.1, we can fallback to original implementation
+        # for torch version less than 2.1, we can fall back to original implementation
         pass
 
     try:
@@ -916,7 +916,7 @@ def _is_complete(tensor: "torch.Tensor") -> bool:
             attrs, _ = tensor.__tensor_flatten__()  # type: ignore[attr-defined]
             return all(_is_complete(getattr(tensor, attr)) for attr in attrs)
     except ImportError:
-        # for torch version less than 2.1, we can fallback to original implementation
+        # for torch version less than 2.1, we can fall back to original implementation
         pass
 
     return tensor.data_ptr() == storage_ptr(tensor) and tensor.nelement() * _get_dtype_size(

src/huggingface_hub/utils/_telemetry.py

@@ -25,7 +25,7 @@ def send_telemetry(
     user_agent: Union[dict, str, None] = None,
 ) -> None:
     """
-    Sends telemetry that helps tracking usage of different HF libraries.
+    Sends telemetry that helps track usage of different HF libraries.
 
     This usage data helps us debug issues and prioritize new features. However, we understand that not everyone wants
     to share additional information, and we respect your privacy. You can disable telemetry collection by setting the