Clean high-level imports

outlines/models/transformers.py

@@ -2,8 +2,6 @@
 import inspect
 from typing import TYPE_CHECKING, Iterator, List, Optional, Tuple, Union
 
-from datasets.fingerprint import Hasher
-
 from outlines.generate.api import GenerationParameters, SamplingParameters
 from outlines.models.tokenizer import Tokenizer
 
@@ -116,6 +114,8 @@ def __eq__(self, other):
         return NotImplemented
 
     def __hash__(self):
+        from datasets.fingerprint import Hasher
+
         return hash(Hasher.hash(self.tokenizer))
 
     def __getstate__(self):

outlines/models/vllm.py

@@ -1,11 +1,10 @@
 import dataclasses
 from typing import TYPE_CHECKING, List, Optional, Union
 
-from transformers import SPIECE_UNDERLINE, PreTrainedTokenizerBase
-
 from outlines.generate.api import GenerationParameters, SamplingParameters
 
 if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizerBase
     from vllm import LLM
     from vllm.sampling_params import SamplingParams
 
@@ -188,7 +187,7 @@ def vllm(model_name: str, **vllm_model_params):
     return VLLM(model)
 
 
-def adapt_tokenizer(tokenizer: PreTrainedTokenizerBase) -> PreTrainedTokenizerBase:
+def adapt_tokenizer(tokenizer: "PreTrainedTokenizerBase") -> "PreTrainedTokenizerBase":
     """Adapt a tokenizer to use to compile the FSM.
 
     The API of Outlines tokenizers is slightly different to that of `transformers`. In
@@ -205,6 +204,8 @@ def adapt_tokenizer(tokenizer: PreTrainedTokenizerBase) -> PreTrainedTokenizerBa
     PreTrainedTokenizerBase
         The adapted tokenizer.
     """
+    from transformers import SPIECE_UNDERLINE
+
     tokenizer.vocabulary = tokenizer.get_vocab()
     tokenizer.special_tokens = set(tokenizer.all_special_tokens)