Remove repeated import (#40937)

* Remove repeated import

Signed-off-by: Yuanyuan Chen <cyyever@outlook.com>

* Fix conflict

Signed-off-by: Yuanyuan Chen <cyyever@outlook.com>

---------

Signed-off-by: Yuanyuan Chen <cyyever@outlook.com>

Author: cyyever

src/transformers/data/data_collator.py

@@ -737,8 +737,6 @@ def get_generator(self, seed):
 
             return torch.Generator().manual_seed(seed)
         else:
-            import numpy as np
-
             return np.random.default_rng(seed)
 
     def create_rng(self):

src/transformers/integrations/integration_utils.py

@@ -544,8 +544,6 @@ def run_hp_search_sigopt(trainer, n_trials: int, direction: str, **kwargs) -> Be
 
 
 def run_hp_search_wandb(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
-    from ..integrations import is_wandb_available
-
     if not is_wandb_available():
         raise ImportError("This function needs wandb installed: `pip install wandb`")
     import wandb

src/transformers/integrations/tensor_parallel.py

@@ -1103,8 +1103,6 @@ def distribute_model(model, distributed_config, device_mesh, tp_size):
                 raise ValueError(f"Unsupported tensor parallel style {v}. Supported styles are {ALL_PARALLEL_STYLES}")
         for name, module in model.named_modules():
             if not getattr(module, "_is_hooked", False):
-                from transformers.integrations.tensor_parallel import add_tensor_parallel_hooks_to_module
-
                 plan = _get_parameter_tp_plan(parameter_name=name, tp_plan=model_plan, is_weight=False)
                 add_tensor_parallel_hooks_to_module(
                     model=model,

src/transformers/modeling_utils.py

@@ -2242,8 +2242,6 @@ def tp_plan(self, plan: dict[str, str]):
                                         flexible_matched = True
                                         break
                             if not flexible_matched:
-                                import warnings
-
                                 warnings.warn(
                                     f"Layer pattern '{layer_pattern}' does not match any parameters in the model. "
                                     f"This rule may not be applied during tensor parallelization."

src/transformers/testing_utils.py

@@ -15,6 +15,7 @@
 import ast
 import collections
 import contextlib
+import copy
 import doctest
 import functools
 import gc
@@ -2752,8 +2753,6 @@ def wrapper(*args, **kwargs):
         else:
             test = " ".join(os.environ.get("PYTEST_CURRENT_TEST").split(" ")[:-1])
             try:
-                import copy
-
                 env = copy.deepcopy(os.environ)
                 env["_INSIDE_SUB_PROCESS"] = "1"
                 # This prevents the entries in `short test summary info` given by the subprocess being truncated. so the

src/transformers/utils/hub.py

@@ -1084,7 +1084,6 @@ def get_checkpoint_shard_files(
     For the description of each arg, see [`PreTrainedModel.from_pretrained`]. `index_filename` is the full path to the
     index (downloaded and cached if `pretrained_model_name_or_path` is a model ID on the Hub).
     """
-    import json
 
     use_auth_token = deprecated_kwargs.pop("use_auth_token", None)
     if use_auth_token is not None:

src/transformers/utils/metrics.py

@@ -105,8 +105,6 @@ def decorator(func):
         if not _has_opentelemetry:
             return func
 
-        import functools
-
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
             instance = args[0] if args and (hasattr(func, "__self__") and func.__self__ is not None) else None