ci : switch from pyright to ty (#20826)
* type fixes * switch to ty * tweak rules * tweak more rules * more tweaks * final tweak * use common import-not-found rule
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+13
-12
@@ -31,10 +31,10 @@ import gguf
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from gguf.vocab import MistralTokenizerType, MistralVocab
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try:
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from mistral_common.tokens.tokenizers.base import TokenizerVersion # pyright: ignore[reportMissingImports]
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from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # pyright: ignore[reportMissingImports]
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from mistral_common.tokens.tokenizers.tekken import Tekkenizer # pyright: ignore[reportMissingImports]
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from mistral_common.tokens.tokenizers.sentencepiece import ( # pyright: ignore[reportMissingImports]
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from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found]
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from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found]
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from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found]
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from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found]
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SentencePieceTokenizer,
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)
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@@ -45,9 +45,9 @@ except ImportError:
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_MISTRAL_COMMON_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
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_mistral_common_installed = False
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TokenizerVersion = None
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Tekkenizer = None
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SentencePieceTokenizer = None
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TokenizerVersion: Any = None
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Tekkenizer: Any = None
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SentencePieceTokenizer: Any = None
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_mistral_import_error_msg = (
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"Mistral format requires `mistral-common` to be installed. Please run "
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"`pip install mistral-common[image,audio]` to install it."
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@@ -220,7 +220,7 @@ class ModelBase:
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if weight_map is None or not isinstance(weight_map, dict):
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raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
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tensor_names_from_index.update(weight_map.keys())
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part_dict: dict[str, None] = dict.fromkeys(weight_map.values(), None)
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part_dict: dict[str, None] = dict.fromkeys(weight_map.values(), None) # ty: ignore[invalid-assignment]
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part_names = sorted(part_dict.keys())
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else:
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weight_map = {}
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@@ -5882,7 +5882,7 @@ class InternLM2Model(TextModel):
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logger.error(f'Error: Missing {tokenizer_path}')
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sys.exit(1)
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
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@@ -6203,7 +6203,7 @@ class BertModel(TextModel):
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vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)
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else:
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
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@@ -8880,7 +8880,7 @@ class T5Model(TextModel):
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if not tokenizer_path.is_file():
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raise FileNotFoundError(f"File not found: {tokenizer_path}")
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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# some models like Pile-T5 family use BPE tokenizer instead of Unigram
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@@ -9017,7 +9017,7 @@ class T5EncoderModel(TextModel):
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if not tokenizer_path.is_file():
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raise FileNotFoundError(f"File not found: {tokenizer_path}")
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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# some models like Pile-T5 family use BPE tokenizer instead of Unigram
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@@ -12279,6 +12279,7 @@ class LazyTorchTensor(gguf.LazyBase):
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kwargs = {}
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if func is torch.Tensor.numpy:
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assert len(args)
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return args[0].numpy()
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return cls._wrap_fn(func)(*args, **kwargs)
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