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Check config type using type instead of isinstance (#7363)

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* Check config type instead of instance


Bad merge

* Remove for loops

* Style
This commit is contained in:
Lysandre Debut 2020-09-25 11:09:09 +02:00 committed by GitHub
parent 3c6bf8998f
commit 7cdd9da5bf
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 126 additions and 127 deletions
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120
src/transformers/modeling_auto.py
View File

@ -544,9 +544,8 @@ class AutoModel:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModel.from_config(config)
"""
for config_class, model_class in MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_MAPPING.keys():
return MODEL_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -585,9 +584,10 @@ class AutoModel:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_MAPPING.keys():
return MODEL_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -638,9 +638,8 @@ class AutoModelForPreTraining:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModelForPreTraining.from_config(config)
"""
for config_class, model_class in MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_PRETRAINING_MAPPING.keys():
return MODEL_FOR_PRETRAINING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -679,9 +678,10 @@ class AutoModelForPreTraining:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_PRETRAINING_MAPPING.keys():
return MODEL_FOR_PRETRAINING_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -744,9 +744,8 @@ class AutoModelWithLMHead:
"`AutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
for config_class, model_class in MODEL_WITH_LM_HEAD_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_WITH_LM_HEAD_MAPPING.keys():
return MODEL_WITH_LM_HEAD_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -791,9 +790,10 @@ class AutoModelWithLMHead:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_WITH_LM_HEAD_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_WITH_LM_HEAD_MAPPING.keys():
return MODEL_WITH_LM_HEAD_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -844,9 +844,8 @@ class AutoModelForCausalLM:
>>> config = AutoConfig.from_pretrained('gpt2')
>>> model = AutoModelForCausalLM.from_config(config)
"""
for config_class, model_class in MODEL_FOR_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_CAUSAL_LM_MAPPING.keys():
return MODEL_FOR_CAUSAL_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -885,9 +884,10 @@ class AutoModelForCausalLM:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_CAUSAL_LM_MAPPING.keys():
return MODEL_FOR_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -938,9 +938,8 @@ class AutoModelForMaskedLM:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModelForMaskedLM.from_config(config)
"""
for config_class, model_class in MODEL_FOR_MASKED_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_MASKED_LM_MAPPING.keys():
return MODEL_FOR_MASKED_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -979,9 +978,10 @@ class AutoModelForMaskedLM:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_MASKED_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_MASKED_LM_MAPPING.keys():
return MODEL_FOR_MASKED_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1032,9 +1032,8 @@ class AutoModelForSeq2SeqLM:
>>> config = AutoConfig.from_pretrained('t5')
>>> model = AutoModelForSeq2SeqLM.from_config(config)
"""
for config_class, model_class in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
return MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1075,9 +1074,10 @@ class AutoModelForSeq2SeqLM:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
return MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1130,9 +1130,8 @@ class AutoModelForSequenceClassification:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModelForSequenceClassification.from_config(config)
"""
for config_class, model_class in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
return MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1173,9 +1172,10 @@ class AutoModelForSequenceClassification:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
return MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1227,9 +1227,8 @@ class AutoModelForQuestionAnswering:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModelForQuestionAnswering.from_config(config)
"""
for config_class, model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
@ -1271,9 +1270,10 @@ class AutoModelForQuestionAnswering:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
@ -1326,9 +1326,8 @@ class AutoModelForTokenClassification:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModelForTokenClassification.from_config(config)
"""
for config_class, model_class in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
return MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
@ -1370,9 +1369,10 @@ class AutoModelForTokenClassification:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
return MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
@ -1426,9 +1426,8 @@ class AutoModelForMultipleChoice:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = AutoModelForMultipleChoice.from_config(config)
"""
for config_class, model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
return MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
@ -1470,9 +1469,10 @@ class AutoModelForMultipleChoice:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
return MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"

121
src/transformers/modeling_tf_auto.py
View File

@ -453,9 +453,8 @@ class TFAutoModel(object):
>>> config = TFAutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModel.from_config(config)
"""
for config_class, model_class in TF_MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_MAPPING.keys():
return TF_MODEL_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -494,9 +493,10 @@ class TFAutoModel(object):
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_MAPPING.keys():
return TF_MODEL_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -547,9 +547,8 @@ class TFAutoModelForPreTraining(object):
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForPreTraining.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_PRETRAINING_MAPPING.keys():
return TF_MODEL_FOR_PRETRAINING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -588,9 +587,10 @@ class TFAutoModelForPreTraining(object):
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_PRETRAINING_MAPPING.keys():
return TF_MODEL_FOR_PRETRAINING_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -653,9 +653,8 @@ class TFAutoModelWithLMHead(object):
"and `TFAutoModelForSeq2SeqLM` for encoder-decoder models.",
FutureWarning,
)
for config_class, model_class in TF_MODEL_WITH_LM_HEAD_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_WITH_LM_HEAD_MAPPING.keys():
return TF_MODEL_WITH_LM_HEAD_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -701,10 +700,10 @@ class TFAutoModelWithLMHead(object):
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_WITH_LM_HEAD_MAPPING.items():
# Not using isinstance() here to do not take into account inheritance
if config_class == type(config):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_WITH_LM_HEAD_MAPPING.keys():
return TF_MODEL_WITH_LM_HEAD_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -755,9 +754,8 @@ class TFAutoModelForCausalLM:
>>> config = AutoConfig.from_pretrained('gpt2')
>>> model = TFAutoModelForCausalLM.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_CAUSAL_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -796,9 +794,10 @@ class TFAutoModelForCausalLM:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -849,9 +848,8 @@ class TFAutoModelForMaskedLM:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForMaskedLM.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_MASKED_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_MASKED_LM_MAPPING.keys():
return TF_MODEL_FOR_MASKED_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -890,9 +888,10 @@ class TFAutoModelForMaskedLM:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_MASKED_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_MASKED_LM_MAPPING.keys():
return TF_MODEL_FOR_MASKED_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -943,9 +942,8 @@ class TFAutoModelForSeq2SeqLM:
>>> config = AutoConfig.from_pretrained('t5')
>>> model = TFAutoModelForSeq2SeqLM.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -986,9 +984,10 @@ class TFAutoModelForSeq2SeqLM:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys():
return TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1041,9 +1040,8 @@ class TFAutoModelForSequenceClassification(object):
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForSequenceClassification.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1084,9 +1082,10 @@ class TFAutoModelForSequenceClassification(object):
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1138,9 +1137,8 @@ class TFAutoModelForQuestionAnswering(object):
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForQuestionAnswering.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
return TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1181,9 +1179,10 @@ class TFAutoModelForQuestionAnswering(object):
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
return TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1235,9 +1234,8 @@ class TFAutoModelForTokenClassification:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForTokenClassification.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1278,9 +1276,10 @@ class TFAutoModelForTokenClassification:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys():
return TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1333,9 +1332,8 @@ class TFAutoModelForMultipleChoice:
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> model = TFAutoModelForMultipleChoice.from_config(config)
"""
for config_class, model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
if type(config) in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
return TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
@ -1376,9 +1374,10 @@ class TFAutoModelForMultipleChoice:
pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
)
for config_class, model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
if type(config) in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys():
return TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)].from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **kwargs
)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(

12
src/transformers/tokenization_auto.py
View File

@ -243,12 +243,12 @@ class AutoTokenizer:
)
config = config.encoder
for config_class, (tokenizer_class_py, tokenizer_class_fast) in TOKENIZER_MAPPING.items():
if type(config) is config_class:
if tokenizer_class_fast and use_fast:
return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
else:
return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
if type(config) in TOKENIZER_MAPPING.keys():
tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)]
if tokenizer_class_fast and use_fast:
return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
else:
return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
raise ValueError(
"Unrecognized configuration class {} to build an AutoTokenizer.\n"