333 lines
13 KiB
Python
333 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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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 copy
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import os
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import tempfile
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import (
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DUMMY_UNKNOWN_IDENTIFIER,
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SMALL_MODEL_IDENTIFIER,
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require_scatter,
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require_torch,
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slow,
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)
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if is_torch_available():
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import torch
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForMaskedLM,
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AutoModelForPreTraining,
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AutoModelForQuestionAnswering,
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AutoModelForSeq2SeqLM,
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AutoModelForSequenceClassification,
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AutoModelForTableQuestionAnswering,
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AutoModelForTokenClassification,
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AutoModelWithLMHead,
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BertConfig,
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BertForMaskedLM,
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BertForPreTraining,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BertForTokenClassification,
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BertModel,
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FunnelBaseModel,
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FunnelModel,
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GPT2Config,
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GPT2LMHeadModel,
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PreTrainedModel,
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RobertaForMaskedLM,
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T5Config,
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T5ForConditionalGeneration,
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TapasConfig,
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TapasForQuestionAnswering,
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)
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from transformers.models.auto.modeling_auto import (
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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MODEL_FOR_PRETRAINING_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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MODEL_MAPPING,
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MODEL_WITH_LM_HEAD_MAPPING,
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)
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from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.gpt2.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.tapas.modeling_tapas import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
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if is_torch_available():
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class FakeModel(PreTrainedModel):
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config_class = BertConfig
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base_model_prefix = "fake"
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def __init__(self, config):
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super().__init__(config)
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self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size)
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def forward(self, x):
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return self.linear(x)
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def _init_weights(self, module):
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pass
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# Make sure this is synchronized with the model above.
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FAKE_MODEL_CODE = """
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import torch
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from transformers import BertConfig, PreTrainedModel
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class FakeModel(PreTrainedModel):
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config_class = BertConfig
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base_model_prefix = "fake"
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def __init__(self, config):
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super().__init__(config)
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self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size)
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def forward(self, x):
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return self.linear(x)
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def _init_weights(self, module):
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pass
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"""
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@require_torch
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class AutoModelTest(unittest.TestCase):
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@slow
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def test_model_from_pretrained(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModel.from_pretrained(model_name)
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model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertModel)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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self.assertEqual(len(loading_info["unexpected_keys"]), 8)
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self.assertEqual(len(loading_info["mismatched_keys"]), 0)
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self.assertEqual(len(loading_info["error_msgs"]), 0)
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@slow
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def test_model_for_pretraining_from_pretrained(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForPreTraining.from_pretrained(model_name)
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model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForPreTraining)
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# Only one value should not be initialized and in the missing keys.
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missing_keys = loading_info.pop("missing_keys")
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self.assertListEqual(["cls.predictions.decoder.bias"], missing_keys)
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for key, value in loading_info.items():
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self.assertEqual(len(value), 0)
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@slow
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def test_lmhead_model_from_pretrained(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelWithLMHead.from_pretrained(model_name)
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model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForMaskedLM)
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@slow
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def test_model_for_causal_lm(self):
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for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, GPT2Config)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, GPT2LMHeadModel)
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@slow
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def test_model_for_masked_lm(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForMaskedLM.from_pretrained(model_name)
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model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForMaskedLM)
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@slow
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def test_model_for_encoder_decoder_lm(self):
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for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, T5Config)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, T5ForConditionalGeneration)
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@slow
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def test_sequence_classification_model_from_pretrained(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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model, loading_info = AutoModelForSequenceClassification.from_pretrained(
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model_name, output_loading_info=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForSequenceClassification)
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@slow
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def test_question_answering_model_from_pretrained(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForQuestionAnswering)
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@slow
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@require_scatter
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def test_table_question_answering_model_from_pretrained(self):
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for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, TapasConfig)
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model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
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model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(
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model_name, output_loading_info=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TapasForQuestionAnswering)
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@slow
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def test_token_classification_model_from_pretrained(self):
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for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForTokenClassification.from_pretrained(model_name)
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model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForTokenClassification)
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def test_from_pretrained_identifier(self):
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model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(model, BertForMaskedLM)
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self.assertEqual(model.num_parameters(), 14410)
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self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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def test_from_identifier_from_model_type(self):
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model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
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self.assertIsInstance(model, RobertaForMaskedLM)
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self.assertEqual(model.num_parameters(), 14410)
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self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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def test_from_pretrained_with_tuple_values(self):
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# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
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model = AutoModel.from_pretrained("sgugger/funnel-random-tiny")
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self.assertIsInstance(model, FunnelModel)
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config = copy.deepcopy(model.config)
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config.architectures = ["FunnelBaseModel"]
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model = AutoModel.from_config(config)
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self.assertIsInstance(model, FunnelBaseModel)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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model = AutoModel.from_pretrained(tmp_dir)
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self.assertIsInstance(model, FunnelBaseModel)
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def test_parents_and_children_in_mappings(self):
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# Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
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# by the parents and will return the wrong configuration type when using auto models
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mappings = (
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MODEL_MAPPING,
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MODEL_FOR_PRETRAINING_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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MODEL_WITH_LM_HEAD_MAPPING,
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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)
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for mapping in mappings:
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mapping = tuple(mapping.items())
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for index, (child_config, child_model) in enumerate(mapping[1:]):
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for parent_config, parent_model in mapping[: index + 1]:
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assert not issubclass(
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child_config, parent_config
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), f"{child_config.__name__} is child of {parent_config.__name__}"
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# Tuplify child_model and parent_model since some of them could be tuples.
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if not isinstance(child_model, (list, tuple)):
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child_model = (child_model,)
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if not isinstance(parent_model, (list, tuple)):
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parent_model = (parent_model,)
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for child, parent in [(a, b) for a in child_model for b in parent_model]:
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assert not issubclass(child, parent), f"{child.__name__} is child of {parent.__name__}"
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def test_from_pretrained_dynamic_model(self):
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config = BertConfig(
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vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
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)
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config.auto_map = {"AutoModel": "modeling.FakeModel"}
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model = FakeModel(config)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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with open(os.path.join(tmp_dir, "modeling.py"), "w") as f:
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f.write(FAKE_MODEL_CODE)
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new_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
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for p1, p2 in zip(model.parameters(), new_model.parameters()):
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self.assertTrue(torch.equal(p1, p2))
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