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/flax/image-captioning/run_image_captioning_flax.py

@@ -52,7 +52,7 @@ from transformers import (
     HfArgumentParser,
     is_tensorboard_available,
 )
-from transformers.utils import get_full_repo_name, is_offline_mode
+from transformers.utils import get_full_repo_name, is_offline_mode, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -388,6 +388,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_image_captioning", model_args, data_args, framework="flax")
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)

examples/flax/language-modeling/run_clm_flax.py

@@ -58,7 +58,7 @@ from transformers import (
     set_seed,
 )
 from transformers.testing_utils import CaptureLogger
-from transformers.utils import get_full_repo_name
+from transformers.utils import get_full_repo_name, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -328,6 +328,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_clm", model_args, data_args, framework="flax")
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)

examples/flax/language-modeling/run_mlm_flax.py

@@ -58,7 +58,7 @@ from transformers import (
     is_tensorboard_available,
     set_seed,
 )
-from transformers.utils import get_full_repo_name
+from transformers.utils import get_full_repo_name, send_example_telemetry
 
 
 MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -365,6 +365,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_mlm", model_args, data_args, framework="flax")
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -57,7 +57,7 @@ from transformers import (
     set_seed,
 )
 from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
-from transformers.utils import get_full_repo_name
+from transformers.utils import get_full_repo_name, send_example_telemetry
 
 
 MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -498,6 +498,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_t5_mlm", model_args, data_args, framework="flax")
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)

examples/flax/question-answering/run_qa.py

@@ -53,7 +53,7 @@ from transformers import (
     PreTrainedTokenizerFast,
     is_tensorboard_available,
 )
-from transformers.utils import check_min_version, get_full_repo_name
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from utils_qa import postprocess_qa_predictions
 
 
@@ -424,6 +424,10 @@ def main():
         model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # 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_qa", model_args, data_args, framework="flax")
     # endregion
 
     # region Logging

examples/flax/summarization/run_summarization_flax.py

@@ -54,7 +54,7 @@ from transformers import (
     HfArgumentParser,
     is_tensorboard_available,
 )
-from transformers.utils import get_full_repo_name, is_offline_mode
+from transformers.utils import get_full_repo_name, is_offline_mode, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -399,6 +399,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_summarization", model_args, data_args, framework="flax")
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)

examples/flax/text-classification/run_flax_glue.py

@@ -48,7 +48,7 @@ from transformers import (
     TrainingArguments,
     is_tensorboard_available,
 )
-from transformers.utils import check_min_version, get_full_repo_name
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -308,6 +308,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_glue", model_args, data_args, framework="flax")
+
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",

examples/flax/token-classification/run_flax_ner.py

@@ -47,7 +47,7 @@ from transformers import (
     HfArgumentParser,
     is_tensorboard_available,
 )
-from transformers.utils import check_min_version, get_full_repo_name
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -366,6 +366,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_ner", model_args, data_args, framework="flax")
+
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",

examples/flax/vision/run_image_classification.py

@@ -53,7 +53,7 @@ from transformers import (
     is_tensorboard_available,
     set_seed,
 )
-from transformers.utils import get_full_repo_name
+from transformers.utils import get_full_repo_name, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -256,6 +256,10 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    # 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_image_classification", model_args, data_args, framework="flax")
+
     if (
         os.path.exists(training_args.output_dir)
         and os.listdir(training_args.output_dir)