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isinstance(examples[0], Mapping): - batch: Dict[str, Any] = self.tokenizer.pad( # type: ignore[assignment] - examples, # type: ignore[arg-type] + batch: Dict[str, Any] = self.tokenizer.pad( + examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of, ) @@ -186,7 +186,7 @@ def torch_mask_tokens( inputs = torch.where( ~mask, inputs, - self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token), # type: ignore[arg-type] + self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token), ) labels = torch.where(mask, labels, -100) if special_tokens_mask is not None: diff --git a/atomgen/data/tokenizer.py b/atomgen/data/tokenizer.py index 5a89279..8370865 100644 --- a/atomgen/data/tokenizer.py +++ b/atomgen/data/tokenizer.py @@ -13,7 +13,7 @@ VOCAB_FILES_NAMES: Dict[str, str] = {"vocab_file": "tokenizer.json"} -class AtomTokenizer(PreTrainedTokenizer): +class AtomTokenizer(PreTrainedTokenizer): # type: ignore[misc] """ Tokenizer for atomistic data. @@ -43,7 +43,7 @@ def __init__( [(ids, tok) for tok, ids in self.vocab.items()] ) - super().__init__( # type: ignore[no-untyped-call] + super().__init__( pad_token=pad_token, mask_token=mask_token, bos_token=bos_token, @@ -63,7 +63,7 @@ def load_vocab(vocab_file: str) -> Dict[str, int]: ) return vocab - def _tokenize(self, text: str) -> List[str]: # type: ignore[override] + def _tokenize(self, text: str) -> List[str]: """Tokenize the text.""" tokens = [] i = 0 @@ -95,7 +95,7 @@ def convert_tokens_to_string(self, tokens: List[str]) -> str: """Convert the list of chemical symbol tokens to a concatenated string.""" return "".join(tokens) - def pad( # type: ignore[override] + def pad( self, encoded_inputs: Union[ BatchEncoding, @@ -155,7 +155,7 @@ def pad( # type: ignore[override] pad_to_multiple_of=pad_to_multiple_of, ) - return super().pad( + return super().pad( # type: ignore[no-any-return] encoded_inputs=encoded_inputs, padding=padding, max_length=max_length, diff --git a/atomgen/models/modeling_atomformer.py b/atomgen/models/modeling_atomformer.py index d67b8c7..104edc6 100644 --- a/atomgen/models/modeling_atomformer.py +++ b/atomgen/models/modeling_atomformer.py @@ -2550,7 +2550,7 @@ def forward( class AtomformerPreTrainedModel(PreTrainedModel): # type: ignore[no-untyped-call] """Base class for all transformer models.""" - config_class = AtomformerConfig + config_class = AtomformerConfig # type: ignore[assignment] base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["ParallelBlock"] diff --git a/atomgen/models/schnet.py b/atomgen/models/schnet.py index 21e0ac8..aaa5164 100644 --- a/atomgen/models/schnet.py +++ b/atomgen/models/schnet.py @@ -134,7 +134,7 @@ class SchNetPreTrainedModel(PreTrainedModel): # type: ignore[no-untyped-call] simple interface for loading and exporting models. """ - config_class = SchNetConfig + config_class = SchNetConfig # type: ignore[assignment] base_model_prefix = "model" supports_gradient_checkpointing = False diff --git a/atomgen/models/tokengt.py b/atomgen/models/tokengt.py index 2476d0a..3549de1 100644 --- a/atomgen/models/tokengt.py +++ b/atomgen/models/tokengt.py @@ -2510,7 +2510,7 @@ def custom_forward(*inputs: Any) -> Any: class TransformerPreTrainedModel(PreTrainedModel): # type: ignore[no-untyped-call] """Base class for all transformer models.""" - config_class = TransformerConfig + config_class = TransformerConfig # type: ignore[assignment] base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["ParallelBlock"]