[warnings] fix E721 warnings (#32223)

fix E721 warnings

src/transformers/generation/candidate_generator.py

@@ -162,7 +162,7 @@ class AssistedCandidateGenerator(CandidateGenerator):
         self.generation_config.min_length = 0
         self.generation_config.min_new_tokens = None
         for processor in self.logits_processor:
-            if type(processor) == MinLengthLogitsProcessor:
+            if isinstance(processor, MinLengthLogitsProcessor):
                 raise ValueError(
                     "Passing `MinLengthLogitsProcessor` when using `assisted_generation is disabled. "
                     "Please pass in `min_length` into `.generate()` instead"

src/transformers/models/bart/modeling_flax_bart.py

@@ -1599,7 +1599,7 @@ class FlaxBartForSequenceClassificationModule(nn.Module):
         eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0)
 
         # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation
-        if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer:
+        if not isinstance(eos_mask, jax.interpreters.partial_eval.DynamicJaxprTracer):
             if len(jnp.unique(eos_mask.sum(1))) > 1:
                 raise ValueError("All examples must have the same number of <eos> tokens.")
 

src/transformers/models/esm/openfold_utils/chunk_utils.py

@@ -356,7 +356,7 @@ class ChunkSizeTuner:
     def _compare_arg_caches(self, ac1: Iterable, ac2: Iterable) -> bool:
         consistent = True
         for a1, a2 in zip(ac1, ac2):
-            assert type(ac1) == type(ac2)
+            assert type(ac1) is type(ac2)
             if isinstance(ac1, (list, tuple)):
                 consistent &= self._compare_arg_caches(a1, a2)
             elif isinstance(ac1, dict):

src/transformers/models/mbart/modeling_flax_mbart.py

@@ -1635,7 +1635,7 @@ class FlaxMBartForSequenceClassificationModule(nn.Module):
         eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0)
 
         # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation
-        if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer:
+        if not isinstance(eos_mask, jax.interpreters.partial_eval.DynamicJaxprTracer):
             if len(jnp.unique(eos_mask.sum(1))) > 1:
                 raise ValueError("All examples must have the same number of <eos> tokens.")
 

src/transformers/trainer_pt_utils.py

@@ -128,7 +128,7 @@ def nested_concat(tensors, new_tensors, padding_index=-100):
     """
     if not (isinstance(tensors, torch.Tensor) and isinstance(new_tensors, torch.Tensor)):
         assert (
-            type(tensors) == type(new_tensors)
+            type(tensors) is type(new_tensors)
         ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
     if isinstance(tensors, (list, tuple)):
         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))

src/transformers/utils/chat_template_utils.py

@@ -74,7 +74,7 @@ def _parse_type_hint(hint: str) -> Dict:
 
     elif origin is Union:
         # Recurse into each of the subtypes in the Union, except None, which is handled separately at the end
-        subtypes = [_parse_type_hint(t) for t in args if t != type(None)]
+        subtypes = [_parse_type_hint(t) for t in args if t is not type(None)]
         if len(subtypes) == 1:
             # A single non-null type can be expressed directly
             return_dict = subtypes[0]

src/transformers/utils/generic.py

@@ -214,7 +214,7 @@ def _is_tf_symbolic_tensor(x):
     # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
     if hasattr(tf, "is_symbolic_tensor"):
         return tf.is_symbolic_tensor(x)
-    return type(x) == tf.Tensor
+    return isinstance(x, tf.Tensor)
 
 
 def is_tf_symbolic_tensor(x):

tests/models/ibert/test_modeling_ibert.py

@@ -684,10 +684,10 @@ class IBertModelIntegrationTest(unittest.TestCase):
         # Recursively convert all the `quant_mode` attributes as `True`
         if hasattr(model, "quant_mode"):
             model.quant_mode = True
-        elif type(model) == nn.Sequential:
+        elif isinstance(model, nn.Sequential):
             for n, m in model.named_children():
                 self.quantize(m)
-        elif type(model) == nn.ModuleList:
+        elif isinstance(model, nn.ModuleList):
             for n in model:
                 self.quantize(n)
         else: