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Unverified Commit acc3bd9d authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
Browse files

Enforce string-formatting with f-strings (#10980) 



* First third

* Styling and fix mistake

* Quality

* All the rest

* Treat %s and %d

* typo

* Missing )

* Apply suggestions from code review
Co-authored-by: default avatarLysandre Debut <lysandre@huggingface.co>
Co-authored-by: default avatarLysandre Debut <lysandre@huggingface.co>
parent d0b3797a
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Showing with 137 additions and 284 deletions
src/transformers/models/auto/modeling_auto.py
View file @ acc3bd9d
......@@ -798,10 +798,8 @@ class AutoModel:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_MAPPING.keys())}."
)
@classmethod
......@@ -841,10 +839,8 @@ class AutoModel:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_MAPPING.keys())}."
)
......@@ -893,10 +889,8 @@ class AutoModelForPreTraining:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())}."
)
@classmethod
......@@ -936,10 +930,8 @@ class AutoModelForPreTraining:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())}."
)
......@@ -999,10 +991,8 @@ class AutoModelWithLMHead:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_WITH_LM_HEAD_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_WITH_LM_HEAD_MAPPING.keys())}."
)
@classmethod
......@@ -1048,10 +1038,8 @@ class AutoModelWithLMHead:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_WITH_LM_HEAD_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_WITH_LM_HEAD_MAPPING.keys())}."
)
......@@ -1099,10 +1087,8 @@ class AutoModelForCausalLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_CAUSAL_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_CAUSAL_LM_MAPPING.keys())}."
)
@classmethod
......@@ -1142,10 +1128,8 @@ class AutoModelForCausalLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_CAUSAL_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_CAUSAL_LM_MAPPING.keys())}."
)
......@@ -1193,10 +1177,8 @@ class AutoModelForMaskedLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_MASKED_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_MASKED_LM_MAPPING.keys())}."
)
@classmethod
......@@ -1236,10 +1218,8 @@ class AutoModelForMaskedLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_MASKED_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_MASKED_LM_MAPPING.keys())}."
)
......@@ -1288,12 +1268,8 @@ class AutoModelForSeq2SeqLM:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())}."
)
@classmethod
......@@ -1333,12 +1309,8 @@ class AutoModelForSeq2SeqLM:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())}."
)
......@@ -1387,12 +1359,8 @@ class AutoModelForSequenceClassification:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys())}."
)
@classmethod
......@@ -1432,12 +1400,8 @@ class AutoModelForSequenceClassification:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys())}."
)
......@@ -1485,12 +1449,8 @@ class AutoModelForQuestionAnswering:
return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())}."
)
@classmethod
......@@ -1531,12 +1491,8 @@ class AutoModelForQuestionAnswering:
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())}."
)
......@@ -1586,12 +1542,8 @@ class AutoModelForTableQuestionAnswering:
return MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys())}."
)
@classmethod
......@@ -1632,12 +1584,8 @@ class AutoModelForTableQuestionAnswering:
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys())}."
)
......@@ -1685,12 +1633,8 @@ class AutoModelForTokenClassification:
return MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())}."
)
@classmethod
......@@ -1731,12 +1675,8 @@ class AutoModelForTokenClassification:
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())}."
)
......@@ -1786,12 +1726,8 @@ class AutoModelForMultipleChoice:
return MODEL_FOR_MULTIPLE_CHOICE_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys())}."
)
@classmethod
......@@ -1832,12 +1768,8 @@ class AutoModelForMultipleChoice:
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys())}."
)
......@@ -1887,12 +1819,8 @@ class AutoModelForNextSentencePrediction:
return MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys())}."
)
@classmethod
......@@ -1933,10 +1861,6 @@ class AutoModelForNextSentencePrediction:
)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys())}."
)
src/transformers/models/auto/modeling_tf_auto.py
View file @ acc3bd9d
......@@ -590,10 +590,8 @@ class TFAutoModel(object):
if type(config) in TF_MODEL_MAPPING.keys():
return TF_MODEL_MAPPING[type(config)](config, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_MAPPING.keys())}."
)
@classmethod
......@@ -633,10 +631,8 @@ class TFAutoModel(object):
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_MAPPING.keys())}."
)
......@@ -685,10 +681,8 @@ class TFAutoModelForPreTraining(object):
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())}."
)
@classmethod
......@@ -728,10 +722,8 @@ class TFAutoModelForPreTraining(object):
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())}."
)
......@@ -791,10 +783,8 @@ class TFAutoModelWithLMHead(object):
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())}."
)
@classmethod
......@@ -840,10 +830,8 @@ class TFAutoModelWithLMHead(object):
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())}."
)
......@@ -891,10 +879,8 @@ class TFAutoModelForCausalLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())}."
)
@classmethod
......@@ -934,10 +920,8 @@ class TFAutoModelForCausalLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())}."
)
......@@ -985,10 +969,8 @@ class TFAutoModelForMaskedLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_MASKED_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_MASKED_LM_MAPPING.keys())}."
)
@classmethod
......@@ -1028,10 +1010,8 @@ class TFAutoModelForMaskedLM:
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(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_MASKED_LM_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_MASKED_LM_MAPPING.keys())}."
)
......@@ -1080,12 +1060,8 @@ class TFAutoModelForSeq2SeqLM:
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, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())}."
)
@classmethod
......@@ -1125,12 +1101,8 @@ class TFAutoModelForSeq2SeqLM:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())}."
)
......@@ -1179,12 +1151,8 @@ class TFAutoModelForSequenceClassification(object):
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys())}."
)
@classmethod
......@@ -1224,12 +1192,8 @@ class TFAutoModelForSequenceClassification(object):
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys())}."
)
......@@ -1277,12 +1241,8 @@ class TFAutoModelForQuestionAnswering(object):
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())}."
)
@classmethod
......@@ -1322,12 +1282,8 @@ class TFAutoModelForQuestionAnswering(object):
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())}."
)
......@@ -1374,12 +1330,8 @@ class TFAutoModelForTokenClassification:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())}."
)
@classmethod
......@@ -1419,12 +1371,8 @@ class TFAutoModelForTokenClassification:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())}."
)
......@@ -1473,12 +1421,8 @@ class TFAutoModelForMultipleChoice:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys())}."
)
@classmethod
......@@ -1518,12 +1462,8 @@ class TFAutoModelForMultipleChoice:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys())}."
)
......@@ -1572,12 +1512,8 @@ class TFAutoModelForNextSentencePrediction:
if type(config) in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys():
return TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING[type(config)](config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys())}."
)
@classmethod
......@@ -1617,10 +1553,6 @@ class TFAutoModelForNextSentencePrediction:
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(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys()),
)
f"Unrecognized configuration class {config.__class__} for this kind of TFAutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.keys())}."
)
src/transformers/models/auto/tokenization_auto.py
View file @ acc3bd9d
......@@ -402,7 +402,7 @@ class AutoTokenizer:
if tokenizer_class is None:
raise ValueError(
"Tokenizer class {} does not exist or is not currently imported.".format(tokenizer_class_candidate)
f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported."
)
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
......@@ -431,8 +431,6 @@ class AutoTokenizer:
)
raise ValueError(
"Unrecognized configuration class {} to build an AutoTokenizer.\n"
"Model type should be one of {}.".format(
config.__class__, ", ".join(c.__name__ for c in TOKENIZER_MAPPING.keys())
)
f"Unrecognized configuration class {config.__class__} to build an AutoTokenizer.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TOKENIZER_MAPPING.keys())}."
)
src/transformers/models/barthez/tokenization_barthez.py
View file @ acc3bd9d
......@@ -256,7 +256,7 @@ class BarthezTokenizer(PreTrainedTokenizer):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......
src/transformers/models/barthez/tokenization_barthez_fast.py
View file @ acc3bd9d
......@@ -218,7 +218,7 @@ class BarthezTokenizerFast(PreTrainedTokenizerFast):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......
src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py
View file @ acc3bd9d
......@@ -38,14 +38,14 @@ logger = logging.get_logger(__name__)
def load_tf2_weights_in_bert(model, tf_checkpoint_path, config):
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
layer_depth = []
for full_name, shape in init_vars:
# logger.info("Loading TF weight {} with shape {}".format(name, shape))
# logger.info(f"Loading TF weight {name} with shape {shape}")
name = full_name.split("/")
if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
logger.info(f"Skipping non-model layer {full_name}")
......
src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py
View file @ acc3bd9d
......@@ -29,14 +29,14 @@ logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = BertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
print(f"Building PyTorch model from configuration: {config}")
model = BertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
print(f"Save PyTorch model to {pytorch_dump_path}")
torch.save(model.state_dict(), pytorch_dump_path)
......
src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py
View file @ acc3bd9d
......@@ -65,7 +65,7 @@ def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name
def to_tf_var_name(name: str):
for patt, repl in iter(var_map):
name = name.replace(patt, repl)
return "bert/{}".format(name)
return f"bert/{name}"
def create_tf_var(tensor: np.ndarray, name: str, session: tf.Session):
tf_dtype = tf.dtypes.as_dtype(tensor.dtype)
......@@ -84,7 +84,7 @@ def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name
tf_var = create_tf_var(tensor=torch_tensor, name=tf_name, session=session)
tf.keras.backend.set_value(tf_var, torch_tensor)
tf_weight = session.run(tf_var)
print("Successfully created {}: {}".format(tf_name, np.allclose(tf_weight, torch_tensor)))
print(f"Successfully created {tf_name}: {np.allclose(tf_weight, torch_tensor)}")
saver = tf.train.Saver(tf.trainable_variables())
saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))
......
src/transformers/models/bert/modeling_bert.py
View file @ acc3bd9d
......@@ -103,13 +103,13 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
......@@ -122,7 +122,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info("Skipping {}".format("/".join(name)))
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
......@@ -142,7 +142,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info("Skipping {}".format("/".join(name)))
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
......@@ -158,7 +158,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
......@@ -215,8 +215,8 @@ class BertSelfAttention(nn.Module):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
......
src/transformers/models/bert/modeling_tf_bert.py
View file @ acc3bd9d
......@@ -411,7 +411,7 @@ class TFBertEncoder(tf.keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
self.layer = [TFBertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
......
src/transformers/models/bert/tokenization_bert.py
View file @ acc3bd9d
......@@ -192,8 +192,8 @@ class BertTokenizer(PreTrainedTokenizer):
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
......@@ -343,8 +343,8 @@ class BertTokenizer(PreTrainedTokenizer):
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
"Saving vocabulary to {}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!".format(vocab_file)
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
......
src/transformers/models/bert_generation/modeling_bert_generation.py
View file @ acc3bd9d
......@@ -109,7 +109,7 @@ def load_tf_weights_in_bert_generation(
array = np.asarray(sess.run(all_variables[key]))
if not is_embedding:
logger.info("Transposing numpy weight of shape {} for {}".format(array.shape, key))
logger.info(f"Transposing numpy weight of shape {array.shape} for {key}")
array = np.transpose(array)
else:
model_pointer = model_pointer.weight
......@@ -126,7 +126,7 @@ def load_tf_weights_in_bert_generation(
model_pointer.data = torch.from_numpy(array.astype(np.float32))
keep_track_variables.pop(key, None)
logger.info("Weights not copied to PyTorch model: {}".format(", ".join(keep_track_variables.keys())))
logger.info(f"Weights not copied to PyTorch model: {', '.join(keep_track_variables.keys())}")
return model
......
src/transformers/models/bert_generation/tokenization_bert_generation.py
View file @ acc3bd9d
......@@ -134,7 +134,7 @@ class BertGenerationTokenizer(PreTrainedTokenizer):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......
src/transformers/models/bert_japanese/tokenization_bert_japanese.py
View file @ acc3bd9d
......@@ -130,8 +130,8 @@ class BertJapaneseTokenizer(BertTokenizer):
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
......@@ -151,7 +151,7 @@ class BertJapaneseTokenizer(BertTokenizer):
do_lower_case=do_lower_case, never_split=never_split, **(mecab_kwargs or {})
)
else:
raise ValueError("Invalid word_tokenizer_type '{}' is specified.".format(word_tokenizer_type))
raise ValueError(f"Invalid word_tokenizer_type '{word_tokenizer_type}' is specified.")
self.do_subword_tokenize = do_subword_tokenize
self.subword_tokenizer_type = subword_tokenizer_type
......@@ -161,7 +161,7 @@ class BertJapaneseTokenizer(BertTokenizer):
elif subword_tokenizer_type == "character":
self.subword_tokenizer = CharacterTokenizer(vocab=self.vocab, unk_token=self.unk_token)
else:
raise ValueError("Invalid subword_tokenizer_type '{}' is specified.".format(subword_tokenizer_type))
raise ValueError(f"Invalid subword_tokenizer_type '{subword_tokenizer_type}' is specified.")
@property
def do_lower_case(self):
......@@ -279,7 +279,7 @@ class MecabTokenizer:
raise ValueError("Invalid mecab_dic is specified.")
mecabrc = os.path.join(dic_dir, "mecabrc")
mecab_option = '-d "{}" -r "{}" '.format(dic_dir, mecabrc) + mecab_option
mecab_option = f'-d "{dic_dir}" -r "{mecabrc}" ' + mecab_option
self.mecab = fugashi.GenericTagger(mecab_option)
......
src/transformers/models/bertweet/tokenization_bertweet.py
View file @ acc3bd9d
......@@ -385,7 +385,7 @@ class BertweetTokenizer(PreTrainedTokenizer):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......@@ -419,7 +419,7 @@ class BertweetTokenizer(PreTrainedTokenizer):
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("Incorrect encoding detected in {}, please " "rebuild the dataset".format(f))
raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset")
return
lines = f.readlines()
......
src/transformers/models/big_bird/convert_bigbird_original_tf_checkpoint_to_pytorch.py
View file @ acc3bd9d
......@@ -27,7 +27,7 @@ logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, pytorch_dump_path, is_trivia_qa):
# Initialise PyTorch model
config = BigBirdConfig.from_json_file(big_bird_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
print(f"Building PyTorch model from configuration: {config}")
if is_trivia_qa:
model = BigBirdForQuestionAnswering(config)
......
src/transformers/models/big_bird/modeling_big_bird.py
View file @ acc3bd9d
......@@ -122,7 +122,7 @@ def load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=False):
if i >= len(init_vars) - 2:
name = name.replace("intermediate", "output")
logger.info("Loading TF weight {} with shape {}".format(name, var.shape))
logger.info(f"Loading TF weight {name} with shape {var.shape}")
array = var.value().numpy()
names.append(name)
tf_weights[name] = array
......@@ -141,7 +141,7 @@ def load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=False):
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.saved_model.load(tf_path).variables if is_trivia_qa else tf.train.list_variables(tf_path)
......@@ -304,8 +304,8 @@ class BigBirdSelfAttention(nn.Module):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
......@@ -2171,9 +2171,8 @@ class BigBirdModel(BigBirdPreTrainedModel):
padding_len = (block_size - seq_len % block_size) % block_size
if padding_len > 0:
logger.info(
"Input ids are automatically padded from {} to {} to be a multiple of `config.block_size`: {}".format(
seq_len, seq_len + padding_len, block_size
)
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.block_size`: {block_size}"
)
if input_ids is not None:
input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
......
src/transformers/models/big_bird/tokenization_big_bird.py
View file @ acc3bd9d
......@@ -164,7 +164,7 @@ class BigBirdTokenizer(PreTrainedTokenizer):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......
src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py
View file @ acc3bd9d
......@@ -208,7 +208,7 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......@@ -226,8 +226,8 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
"Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file)
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
......
src/transformers/models/camembert/tokenization_camembert.py
View file @ acc3bd9d
......@@ -256,7 +256,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
......
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