221 строка
9.5 KiB
Python
221 строка
9.5 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_torch, slow
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if is_torch_available():
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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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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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GPT2Config,
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GPT2LMHeadModel,
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RobertaForMaskedLM,
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T5Config,
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T5ForConditionalGeneration,
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)
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from transformers.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_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.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
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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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for value in loading_info.values():
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self.assertEqual(len(value), 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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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_UNKWOWN_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_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_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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), "{child_config.__name__} is child of {parent_config.__name__}"
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assert not issubclass(
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child_model, parent_model
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), "{child_config.__name__} is child of {parent_config.__name__}"
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