338 lines
11 KiB
Python
338 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import json
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import logging
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import os
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import sys
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from unittest import skip
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from unittest.mock import patch
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import tensorflow as tf
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from packaging.version import parse
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try:
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import tf_keras as keras
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except (ModuleNotFoundError, ImportError):
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import keras
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if parse(keras.__version__).major > 2:
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raise ValueError(
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"Your currently installed version of Keras is Keras 3, but this is not yet supported in "
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"Transformers. Please install the backwards-compatible tf-keras package with "
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"`pip install tf-keras`."
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)
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from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
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SRC_DIRS = [
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os.path.join(os.path.dirname(__file__), dirname)
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for dirname in [
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"text-generation",
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"text-classification",
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"token-classification",
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"language-modeling",
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"multiple-choice",
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"question-answering",
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"summarization",
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"translation",
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"image-classification",
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]
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]
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sys.path.extend(SRC_DIRS)
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if SRC_DIRS is not None:
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import run_clm
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import run_image_classification
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import run_mlm
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import run_ner
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import run_qa as run_squad
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import run_summarization
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import run_swag
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import run_text_classification
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import run_translation
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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def get_setup_file():
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parser = argparse.ArgumentParser()
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parser.add_argument("-f")
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args = parser.parse_args()
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return args.f
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def get_results(output_dir):
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results = {}
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path = os.path.join(output_dir, "all_results.json")
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if os.path.exists(path):
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with open(path, "r") as f:
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results = json.load(f)
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else:
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raise ValueError(f"can't find {path}")
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return results
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def is_cuda_available():
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return bool(tf.config.list_physical_devices("GPU"))
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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class ExamplesTests(TestCasePlus):
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@skip("Skipping until shape inference for to_tf_dataset PR is merged.")
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def test_run_text_classification(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_text_classification.py
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--model_name_or_path distilbert/distilbert-base-uncased
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
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--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
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--do_train
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--do_eval
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--learning_rate=1e-4
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--max_steps=10
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--warmup_steps=2
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--seed=42
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--max_seq_length=128
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""".split()
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if is_cuda_available():
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testargs.append("--fp16")
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with patch.object(sys, "argv", testargs):
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run_text_classification.main()
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# Reset the mixed precision policy so we don't break other tests
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keras.mixed_precision.set_global_policy("float32")
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_accuracy"], 0.75)
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def test_run_clm(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_clm.py
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--model_name_or_path distilbert/distilgpt2
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--train_file ./tests/fixtures/sample_text.txt
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--validation_file ./tests/fixtures/sample_text.txt
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--do_train
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--do_eval
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--block_size 128
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--per_device_train_batch_size 2
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--per_device_eval_batch_size 1
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--num_train_epochs 2
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--output_dir {tmp_dir}
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--overwrite_output_dir
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""".split()
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if len(tf.config.list_physical_devices("GPU")) > 1:
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# Skipping because there are not enough batches to train the model + would need a drop_last to work.
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return
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with patch.object(sys, "argv", testargs):
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run_clm.main()
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result = get_results(tmp_dir)
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self.assertLess(result["eval_perplexity"], 100)
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def test_run_mlm(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_mlm.py
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--model_name_or_path distilbert/distilroberta-base
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--train_file ./tests/fixtures/sample_text.txt
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--validation_file ./tests/fixtures/sample_text.txt
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--max_seq_length 64
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--do_train
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--do_eval
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--prediction_loss_only
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--num_train_epochs=1
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--learning_rate=1e-4
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""".split()
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with patch.object(sys, "argv", testargs):
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run_mlm.main()
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result = get_results(tmp_dir)
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self.assertLess(result["eval_perplexity"], 42)
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def test_run_ner(self):
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# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
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epochs = 7 if get_gpu_count() > 1 else 2
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_ner.py
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--model_name_or_path google-bert/bert-base-uncased
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--train_file tests/fixtures/tests_samples/conll/sample.json
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--validation_file tests/fixtures/tests_samples/conll/sample.json
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--do_train
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--do_eval
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--warmup_steps=2
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=2
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--num_train_epochs={epochs}
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--seed 7
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""".split()
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with patch.object(sys, "argv", testargs):
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run_ner.main()
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["accuracy"], 0.75)
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def test_run_squad(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_qa.py
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--model_name_or_path google-bert/bert-base-uncased
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--version_2_with_negative
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--train_file tests/fixtures/tests_samples/SQUAD/sample.json
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--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--max_steps=10
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--warmup_steps=2
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--do_train
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--do_eval
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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""".split()
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with patch.object(sys, "argv", testargs):
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run_squad.main()
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["f1"], 30)
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self.assertGreaterEqual(result["exact"], 30)
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def test_run_swag(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_swag.py
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--model_name_or_path google-bert/bert-base-uncased
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--train_file tests/fixtures/tests_samples/swag/sample.json
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--validation_file tests/fixtures/tests_samples/swag/sample.json
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--max_steps=20
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--warmup_steps=2
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--do_train
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--do_eval
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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""".split()
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with patch.object(sys, "argv", testargs):
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run_swag.main()
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["val_accuracy"], 0.8)
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@slow
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def test_run_summarization(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_summarization.py
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--model_name_or_path google-t5/t5-small
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--train_file tests/fixtures/tests_samples/xsum/sample.json
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--validation_file tests/fixtures/tests_samples/xsum/sample.json
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--max_steps=50
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--warmup_steps=8
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--do_train
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--do_eval
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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""".split()
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with patch.object(sys, "argv", testargs):
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run_summarization.main()
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["rouge1"], 10)
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self.assertGreaterEqual(result["rouge2"], 2)
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self.assertGreaterEqual(result["rougeL"], 7)
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self.assertGreaterEqual(result["rougeLsum"], 7)
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@slow
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def test_run_translation(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_translation.py
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--model_name_or_path Rocketknight1/student_marian_en_ro_6_1
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--source_lang en
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--target_lang ro
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--train_file tests/fixtures/tests_samples/wmt16/sample.json
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--validation_file tests/fixtures/tests_samples/wmt16/sample.json
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--warmup_steps=8
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--do_train
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--do_eval
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--learning_rate=3e-3
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--num_train_epochs 12
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--source_lang en_XX
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--target_lang ro_RO
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""".split()
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with patch.object(sys, "argv", testargs):
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run_translation.main()
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["bleu"], 30)
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def test_run_image_classification(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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run_image_classification.py
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--dataset_name hf-internal-testing/cats_vs_dogs_sample
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--model_name_or_path microsoft/resnet-18
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--do_train
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--do_eval
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--learning_rate 1e-4
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--per_device_train_batch_size 2
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--per_device_eval_batch_size 1
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--output_dir {tmp_dir}
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--overwrite_output_dir
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--dataloader_num_workers 16
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--num_train_epochs 2
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--train_val_split 0.1
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--seed 42
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--ignore_mismatched_sizes True
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""".split()
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with patch.object(sys, "argv", testargs):
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run_image_classification.main()
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["accuracy"], 0.7)
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