Fix unnecessary single-item container checks

examples/legacy/pytorch-lightning/run_ner.py

@@ -72,12 +72,12 @@ def prepare_data(self):
                     self.labels,
                     args.max_seq_length,
                     self.tokenizer,
-                    cls_token_at_end=bool(self.config.model_type in ["xlnet"]),
+                    cls_token_at_end=bool(self.config.model_type == "xlnet"),
                     cls_token=self.tokenizer.cls_token,
-                    cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0,
+                    cls_token_segment_id=2 if self.config.model_type == "xlnet" else 0,
                     sep_token=self.tokenizer.sep_token,
                     sep_token_extra=False,
-                    pad_on_left=bool(self.config.model_type in ["xlnet"]),
+                    pad_on_left=bool(self.config.model_type == "xlnet"),
                     pad_token=self.tokenizer.pad_token_id,
                     pad_token_segment_id=self.tokenizer.pad_token_type_id,
                     pad_token_label_id=self.pad_token_label_id,

examples/legacy/token-classification/utils_ner.py

@@ -246,10 +246,10 @@ def __init__(
                         labels,
                         max_seq_length,
                         tokenizer,
-                        cls_token_at_end=bool(model_type in ["xlnet"]),
+                        cls_token_at_end=bool(model_type == "xlnet"),
                         # xlnet has a cls token at the end
                         cls_token=tokenizer.cls_token,
-                        cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
+                        cls_token_segment_id=2 if model_type == "xlnet" else 0,
                         sep_token=tokenizer.sep_token,
                         sep_token_extra=False,
                         # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805

src/transformers/audio_utils.py

@@ -587,7 +587,7 @@ def window_function(
         window = np.hamming(length)
     elif name in ["hann", "hann_window"]:
         window = np.hanning(length)
-    elif name in ["povey"]:
+    elif name == "povey":
         window = np.power(np.hanning(length), 0.85)
     else:
         raise ValueError(f"Unknown window function '{name}'")

src/transformers/generation/utils.py

@@ -883,7 +883,7 @@ def _prepare_decoder_input_ids_for_generation(
             self.config.model_type == "vision-encoder-decoder" and "donut" in self.config.encoder.model_type.lower()
         ):
             pass
-        elif self.config.model_type in ["whisper"]:
+        elif self.config.model_type == "whisper":
             pass
         # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
         # decoder_attention_mask if provided)

src/transformers/integrations/ggml.py

@@ -329,11 +329,11 @@ def _gguf_parse_value(_value, data_type):
         _value = int(_value[0])
     elif data_type in [6, 12]:
         _value = float(_value[0])
-    elif data_type in [7]:
+    elif data_type == 7:
         _value = bool(_value[0])
-    elif data_type in [8]:
+    elif data_type == 8:
         _value = array("B", list(_value)).tobytes().decode()
-    elif data_type in [9]:
+    elif data_type == 9:
         _value = _gguf_parse_value(_value, array_data_type)
     return _value
 

src/transformers/modeling_utils.py

@@ -4001,7 +4001,7 @@ def save_pretrained(
                 if _is_dtensor_available and isinstance(state_dict[tensor], DTensor):
                     full_tensor = state_dict[tensor].full_tensor()
                     # to get the correctly ordered tensor we need to repack if packed
-                    if _get_parameter_tp_plan(tensor, self._tp_plan) in ("local_packed_rowwise",):
+                    if _get_parameter_tp_plan(tensor, self._tp_plan) == "local_packed_rowwise":
                         full_tensor = repack_weights(full_tensor, -1, self._tp_size, 2)
                     shard[tensor] = full_tensor.contiguous()  # only do contiguous after it's permuted correctly
                 else:

src/transformers/models/altclip/configuration_altclip.py

@@ -303,7 +303,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -335,7 +335,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
             for key, value in _vision_config_dict.items():
-                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
                     # If specified in `vision_config_dict`
                     if key in vision_config_dict:
                         message = (

src/transformers/models/chinese_clip/configuration_chinese_clip.py

@@ -306,7 +306,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -338,7 +338,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
             for key, value in _vision_config_dict.items():
-                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
                     # If specified in `vision_config_dict`
                     if key in vision_config_dict:
                         message = (

src/transformers/models/clip/configuration_clip.py

@@ -295,7 +295,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -327,7 +327,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
             for key, value in _vision_config_dict.items():
-                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
                     # If specified in `vision_config_dict`
                     if key in vision_config_dict:
                         message = (

src/transformers/models/clipseg/configuration_clipseg.py

@@ -307,7 +307,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -339,7 +339,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
             for key, value in _vision_config_dict.items():
-                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
                     # If specified in `vision_config_dict`
                     if key in vision_config_dict:
                         message = (

src/transformers/models/dpt/modeling_dpt.py

@@ -864,7 +864,7 @@ def __init__(self, config: DPTConfig):
         self.config = config
 
         # postprocessing: only required in case of a non-hierarchical backbone (e.g. ViT, BEiT)
-        if config.backbone_config is not None and config.backbone_config.model_type in ["swinv2"]:
+        if config.backbone_config is not None and config.backbone_config.model_type == "swinv2":
             self.reassemble_stage = None
         else:
             self.reassemble_stage = DPTReassembleStage(config)

src/transformers/models/flava/configuration_flava.py

@@ -516,7 +516,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -548,7 +548,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_image_config_dict` and `image_config` but being different.
             for key, value in _image_config_dict.items():
-                if key in image_config and value != image_config[key] and key not in ["transformers_version"]:
+                if key in image_config and value != image_config[key] and key != "transformers_version":
                     # If specified in `image_config_dict`
                     if key in image_config_dict:
                         message = (
@@ -576,11 +576,7 @@ def __init__(
             # Give a warning if the values exist in both `_multimodal_config_dict` and `multimodal_config` but being
             # different.
             for key, value in _multimodal_config_dict.items():
-                if (
-                    key in multimodal_config
-                    and value != multimodal_config[key]
-                    and key not in ["transformers_version"]
-                ):
+                if key in multimodal_config and value != multimodal_config[key] and key != "transformers_version":
                     # If specified in `multimodal_config_dict`
                     if key in multimodal_config_dict:
                         message = (
@@ -611,7 +607,7 @@ def __init__(
                 if (
                     key in image_codebook_config
                     and value != image_codebook_config[key]
-                    and key not in ["transformers_version"]
+                    and key != "transformers_version"
                 ):
                     # If specified in `image_codebook_config_dict`
                     if key in image_codebook_config_dict:

src/transformers/models/groupvit/configuration_groupvit.py

@@ -288,7 +288,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -320,7 +320,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
             for key, value in _vision_config_dict.items():
-                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
                     # If specified in `vision_config_dict`
                     if key in vision_config_dict:
                         message = (

src/transformers/models/imagegpt/convert_imagegpt_original_tf2_to_pytorch.py

@@ -60,15 +60,18 @@ def load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path):
 
         # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
         # which are not required for using pretrained model
-        if any(
-            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
-            for n in name
-        ) or name[-1] in ["_step"]:
+        if (
+            any(
+                n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
+                for n in name
+            )
+            or name[-1] == "_step"
+        ):
             logger.info("Skipping {}".format("/".join(name)))
             continue
 
         pointer = model
-        if name[-1] not in ["wtet"]:
+        if name[-1] != "wtet":
             pointer = getattr(pointer, "transformer")
 
         for m_name in name:

src/transformers/models/kosmos2_5/modeling_kosmos2_5.py

@@ -273,9 +273,7 @@ class Kosmos2_5ModelOutput(ModelOutput):
     vision_model_output: BaseModelOutputWithPooling = None
 
     def to_tuple(self) -> tuple[Any]:
-        return tuple(
-            (self[k] if k not in ["vision_model_output"] else getattr(self, k).to_tuple()) for k in self.keys()
-        )
+        return tuple((self[k] if k != "vision_model_output" else getattr(self, k).to_tuple()) for k in self.keys())
 
 
 @dataclass
@@ -333,9 +331,7 @@ class Kosmos2_5ForConditionalGenerationModelOutput(ModelOutput):
     vision_model_output: BaseModelOutputWithPooling = None
 
     def to_tuple(self) -> tuple[Any]:
-        return tuple(
-            (self[k] if k not in ["vision_model_output"] else getattr(self, k).to_tuple()) for k in self.keys()
-        )
+        return tuple((self[k] if k != "vision_model_output" else getattr(self, k).to_tuple()) for k in self.keys())
 
 
 # Copied from transformers.models.pix2struct.modeling_pix2struct.Pix2StructLayerNorm with Pix2Struct->Kosmos2_5

src/transformers/models/llama/convert_llama_weights_to_hf.py

@@ -398,7 +398,7 @@ def permute(w, n_heads, dim1=dim, dim2=dim):
             max_position_embeddings=max_position_embeddings,
             bos_token_id=bos_token_id,
             eos_token_id=eos_token_id,
-            tie_word_embeddings=llama_version in ["3.2"],
+            tie_word_embeddings=llama_version == "3.2",
         )
 
         config.save_pretrained(tmp_model_path)
@@ -451,7 +451,7 @@ def __init__(self, vocab_file, special_tokens=None, instruct=False, llama_versio
         # Prevents a null chat_template, which triggers
         # a parsing warning in the Hub.
         additional_kwargs = {}
-        if instruct or llama_version in ["Guard-3"]:
+        if instruct or llama_version == "Guard-3":
             model_id, revision = templates_for_version.get(llama_version, (None, None))
             if model_id is not None:
                 from transformers import AutoTokenizer

src/transformers/models/metaclip_2/configuration_metaclip_2.py

@@ -277,7 +277,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
             for key, value in _text_config_dict.items():
-                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
+                if key in text_config and value != text_config[key] and key != "transformers_version":
                     # If specified in `text_config_dict`
                     if key in text_config_dict:
                         message = (
@@ -309,7 +309,7 @@ def __init__(
 
             # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
             for key, value in _vision_config_dict.items():
-                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
                     # If specified in `vision_config_dict`
                     if key in vision_config_dict:
                         message = (

src/transformers/models/perceiver/modeling_perceiver.py

@@ -2804,7 +2804,7 @@ class PerceiverAudioPostprocessor(nn.Module):
     def __init__(self, config: PerceiverConfig, in_channels: int, postproc_type: str = "patches") -> None:
         super().__init__()
 
-        if postproc_type not in ("patches",):  # to be supported: 'conv', 'patches', 'pixels'
+        if postproc_type != "patches":  # to be supported: 'conv', 'patches', 'pixels'
             raise ValueError("Invalid postproc_type!")
 
         # Architecture parameters:
@@ -3137,7 +3137,7 @@ def __init__(
         super().__init__()
         self.config = config
 
-        if prep_type not in ("patches",):
+        if prep_type != "patches":
             raise ValueError(f"Prep_type {prep_type} is invalid, can only be 'patches'.")
 
         if concat_or_add_pos not in ["concat", "add"]:

src/transformers/models/phi3/configuration_phi3.py

@@ -210,7 +210,7 @@ def _rope_scaling_validation(self):
         rope_scaling_type = self.rope_scaling.get("type", None)
         rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
         rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
+        if rope_scaling_type is None or rope_scaling_type != "longrope":
             raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
         if not (
             isinstance(rope_scaling_short_factor, list)

src/transformers/models/phi4_multimodal/configuration_phi4_multimodal.py

@@ -452,7 +452,7 @@ def _rope_scaling_validation(self):
         rope_scaling_type = self.rope_scaling.get("type", None)
         rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
         rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
+        if rope_scaling_type is None or rope_scaling_type != "longrope":
             raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
         if not (
             isinstance(rope_scaling_short_factor, list)

src/transformers/models/qwen3_omni_moe/modeling_qwen3_omni_moe.py

@@ -3848,7 +3848,7 @@ def generate(
                     self.config.talker_config.text_config.vocab_size - 1024,
                     self.config.talker_config.text_config.vocab_size,
                 )
-                if i not in (self.config.talker_config.codec_eos_token_id,)
+                if i != self.config.talker_config.codec_eos_token_id
             ]  # Suppress additional special tokens, should not be predicted
             talker_kwargs = {
                 "max_new_tokens": talker_max_new_tokens,

src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py

@@ -2425,7 +2425,7 @@ def generate(
                     self.config.talker_config.text_config.vocab_size - 1024,
                     self.config.talker_config.text_config.vocab_size,
                 )
-                if i not in (self.config.talker_config.codec_eos_token_id,)
+                if i != self.config.talker_config.codec_eos_token_id
             ]  # Suppress additional special tokens, should not be predicted
             talker_kwargs = {
                 "max_new_tokens": talker_max_new_tokens,

src/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py

@@ -96,7 +96,7 @@ def load_tf_weights_in_tapas(model, config, tf_checkpoint_path):
                 continue
         # in case the model is TapasForMaskedLM, we skip the pooler
         if isinstance(model, TapasForMaskedLM):
-            if any(n in ["pooler"] for n in name):
+            if any(n == "pooler" for n in name):
                 logger.info(f"Skipping {'/'.join(name)}")
                 continue
         # if first scope name starts with "bert", change it to "tapas"