cron(style): run formatter [generated] [skip ci] (#257)

openllm-python/src/openllm/models/starcoder/__init__.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
-import sys, typing as t
+import sys
+import typing as t
+
 from openllm.exceptions import MissingDependencyError
 from openllm.utils import LazyModule, is_torch_available, is_vllm_available
 from openllm_core.config.configuration_starcoder import DEFAULT_PROMPT_TEMPLATE as DEFAULT_PROMPT_TEMPLATE, START_STARCODER_COMMAND_DOCSTRING as START_STARCODER_COMMAND_DOCSTRING, StarCoderConfig as StarCoderConfig

openllm-python/src/openllm/models/starcoder/modeling_starcoder.py

@@ -1,5 +1,9 @@
 from __future__ import annotations
-import logging, typing as t, bentoml, openllm
+import logging
+import typing as t
+
+import bentoml
+import openllm
 from openllm.utils import generate_labels
 from openllm_core.config.configuration_starcoder import EOD, FIM_MIDDLE, FIM_PAD, FIM_PREFIX, FIM_SUFFIX
 if t.TYPE_CHECKING: import transformers
@@ -12,7 +16,8 @@ class StarCoder(openllm.LLM['transformers.GPTBigCodeForCausalLM', 'transformers.
     return {'device_map': 'auto' if torch.cuda.is_available() and torch.cuda.device_count() > 1 else None, 'torch_dtype': torch.float16 if torch.cuda.is_available() else torch.float32}, {}
 
   def import_model(self, *args: t.Any, trust_remote_code: bool = False, **attrs: t.Any) -> bentoml.Model:
-    import torch, transformers
+    import torch
+    import transformers
     torch_dtype, device_map = attrs.pop('torch_dtype', torch.float16), attrs.pop('device_map', 'auto')
     tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_id, **self.llm_parameters[-1])
     tokenizer.add_special_tokens({'additional_special_tokens': [EOD, FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD], 'pad_token': EOD})

openllm-python/src/openllm/models/starcoder/modeling_vllm_starcoder.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
-import logging, typing as t, openllm
+import logging
+import typing as t
+
+import openllm
 if t.TYPE_CHECKING: import vllm, transformers
 class VLLMStarCoder(openllm.LLM['vllm.LLMEngine', 'transformers.GPT2TokenizerFast']):
   __openllm_internal__ = True