mirror of
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215 lines
8.0 KiB
Python
215 lines
8.0 KiB
Python
# coding=utf-8
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# Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
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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 unittest
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from transformers import is_torch_available
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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from .utils import CACHE_DIR, require_torch, slow
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if is_torch_available():
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from transformers import T5Config, T5Model, T5WithLMHeadModel
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from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP
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@require_torch
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class T5ModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (T5Model, T5WithLMHeadModel) if is_torch_available() else ()
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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is_encoder_decoder = True
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class T5ModelTester(object):
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def __init__(
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self,
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parent,
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batch_size=13,
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encoder_seq_length=7,
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decoder_seq_length=9,
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is_training=True,
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use_attention_mask=True,
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use_labels=True,
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vocab_size=99,
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n_positions=14,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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d_ff=37,
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relative_attention_num_buckets=8,
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dropout_rate=0.1,
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initializer_factor=0.002,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.encoder_seq_length = encoder_seq_length
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self.decoder_seq_length = decoder_seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.n_positions = n_positions
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.d_ff = d_ff
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.dropout_rate = dropout_rate
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self.initializer_factor = initializer_factor
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self.scope = scope
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def prepare_config_and_inputs(self):
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encoder_input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
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decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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encoder_attention_mask = None
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decoder_attention_mask = None
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if self.use_attention_mask:
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encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
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decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
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decoder_lm_labels = None
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if self.use_labels:
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decoder_lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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config = T5Config(
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vocab_size=self.vocab_size,
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n_positions=self.n_positions,
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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)
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return (
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config,
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encoder_input_ids,
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decoder_input_ids,
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encoder_attention_mask,
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decoder_attention_mask,
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decoder_lm_labels,
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)
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def check_loss_output(self, result):
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self.parent.assertListEqual(list(result["loss"].size()), [])
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def create_and_check_t5_model(
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self,
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config,
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encoder_input_ids,
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decoder_input_ids,
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encoder_attention_mask,
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decoder_attention_mask,
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decoder_lm_labels,
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):
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model = T5Model(config=config)
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model.eval()
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decoder_output, encoder_output = model(
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encoder_input_ids=encoder_input_ids,
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decoder_input_ids=decoder_input_ids,
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encoder_attention_mask=encoder_attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
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decoder_output, encoder_output = model(
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encoder_input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids
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)
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result = {
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"encoder_output": encoder_output,
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"decoder_output": decoder_output,
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}
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self.parent.assertListEqual(
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list(result["encoder_output"].size()), [self.batch_size, self.encoder_seq_length, self.hidden_size]
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)
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self.parent.assertListEqual(
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list(result["decoder_output"].size()), [self.batch_size, self.decoder_seq_length, self.hidden_size]
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)
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def create_and_check_t5_with_lm_head(
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self,
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config,
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encoder_input_ids,
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decoder_input_ids,
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encoder_attention_mask,
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decoder_attention_mask,
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decoder_lm_labels,
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):
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model = T5WithLMHeadModel(config=config)
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model.eval()
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outputs = model(
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encoder_input_ids=encoder_input_ids,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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decoder_lm_labels=decoder_lm_labels,
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)
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loss, prediction_scores = outputs[0], outputs[1]
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()), [self.batch_size, self.decoder_seq_length, self.vocab_size]
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)
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self.check_loss_output(result)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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encoder_input_ids,
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decoder_input_ids,
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encoder_attention_mask,
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decoder_attention_mask,
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decoder_lm_labels,
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) = config_and_inputs
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inputs_dict = {
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"encoder_input_ids": encoder_input_ids,
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"decoder_input_ids": decoder_input_ids,
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"decoder_attention_mask": decoder_attention_mask,
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"encoder_attention_mask": encoder_attention_mask,
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}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = T5ModelTest.T5ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_t5_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_t5_model(*config_and_inputs)
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def test_with_lm_head(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = T5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
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self.assertIsNotNone(model)
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