Add examples telemetry (#17552)

* Add examples telemetry

* Alternative approach

* Add to all other examples

* Add to templates as well

* Put framework separately

* Same for TensorFlow

examples/pytorch/text-classification/run_xnli.py

@@ -42,7 +42,7 @@ from transformers import (
     set_seed,
 )
 from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -112,8 +112,6 @@ class DataTrainingArguments:
             )
         },
     )
-    server_ip: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
-    server_port: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
 
 
 @dataclass
@@ -176,14 +174,9 @@ def main():
     parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
     model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
-    # Setup distant debugging if needed
-    if data_args.server_ip and data_args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(data_args.server_ip, data_args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_xnli", model_args)
 
     # Setup logging
     logging.basicConfig(