test code format

src/transformers/models/plm/configuration_plm.py

@@ -24,7 +24,7 @@ logger = logging.get_logger(__name__)
 class PLMConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`PLMModel`]. It is used to instantiate a
-    PLM model according to the specified arguments, defining the model architecture. 
+    PLM model according to the specified arguments, defining the model architecture.
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the
     defaults will yield a similar configuration to that of the PLM model.
@@ -147,4 +147,4 @@ class PLMConfig(PretrainedConfig):
             **kwargs,
         )
 
-__all__ = ["PLMConfig"]
+__all__ = ["PLMConfig"]

src/transformers/models/plm/modular_plm.py

@@ -1,4 +1,3 @@
-import math
 from typing import Callable, Optional, Tuple
 
 import torch
@@ -15,12 +14,12 @@ from ...utils import logging
 from ..llama.modeling_llama import (
     LlamaDecoderLayer,
     LlamaForCausalLM,
+    LlamaForSequenceClassification,
+    LlamaForTokenClassification,
     LlamaModel,
     LlamaPreTrainedModel,
     LlamaRMSNorm,
-    LlamaForSequenceClassification,
     LlamaRotaryEmbedding,
-    LlamaForTokenClassification,
     apply_rotary_pos_emb,
     eager_attention_forward,
     rotate_half,
@@ -87,7 +86,7 @@ class PLMMLP(nn.Module):
         self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
         self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
         self.act_fn = ACT2FN[config.hidden_act]
-    
+
     def forward(self, hidden_state):
         h = self.up_proj(hidden_state)
         h = self.act_fn(h)
@@ -121,7 +120,7 @@ class PLMAttention(nn.Module):
             self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
         else:
             self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
-     
+
 
         self.kv_a_proj_with_mqa = nn.Linear(
             config.hidden_size,
@@ -215,7 +214,6 @@ class PLMAttention(nn.Module):
         attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
         attn_output = self.o_proj(attn_output)
         return attn_output, attn_weights
-    
 
 
 class PLMDecoderLayer(LlamaDecoderLayer, nn.Module):
@@ -259,4 +257,4 @@ __all__ = [
     "PLMForCausalLM",
     "PLMForSequenceClassification",
     "PLMForTokenClassification"
-]
+]

tests/models/plm/test_modeling_plm.py

@@ -19,12 +19,11 @@ import unittest
 from packaging import version
 from parameterized import parameterized
 
-from transformers import AutoTokenizer, PLMConfig, is_torch_available, set_seed
+from transformers import AutoTokenizer, PLMConfig, is_torch_available
 from transformers.testing_utils import (
     require_read_token,
     require_torch,
     require_torch_accelerator,
-    require_torch_sdpa,
     slow,
     torch_device,
 )
@@ -42,9 +41,9 @@ if is_torch_available():
         PLMForCausalLM,
         PLMModel,
     )
-    from transformers.models.plm.modeling_plm import (
-        PLMRotaryEmbedding,
-    )
+    # from transformers.models.plm.modeling_plm import (
+    #     PLMRotaryEmbedding,
+    # )
 
 
 
@@ -291,10 +290,6 @@ class PLMModelTester:
         ) = config_and_inputs
         inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict
-    
-
-
-
 
 
 @require_torch
@@ -498,7 +493,6 @@ class PLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
     #     ).to(torch_device)
 
     #     self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-        
     #     model_eager = PLMForCausalLM.from_pretrained(
     #         "PLM-Team/PLM-1.8B-Base",
     #         torch_dtype=torch.float16,