[Refactor] Relative imports wherever we can (#21880)

* initial commit

* update

* second batch

* style

* fix imports

* fix relative import on pipeline

src/transformers/models/sew/modeling_sew.py

@@ -24,9 +24,8 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
-from transformers.deepspeed import is_deepspeed_zero3_enabled
-
 from ...activations import ACT2FN
+from ...deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import torch_int_div

src/transformers/models/sew_d/modeling_sew_d.py

@@ -25,9 +25,8 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn import CrossEntropyLoss, LayerNorm
 
-from transformers.deepspeed import is_deepspeed_zero3_enabled
-
 from ...activations import ACT2FN
+from ...deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import softmax_backward_data, torch_int_div

src/transformers/models/swin2sr/image_processing_swin2sr.py

@@ -18,12 +18,10 @@ from typing import Optional, Union
 
 import numpy as np
 
-from transformers.utils.generic import TensorType
-
 from ...image_processing_utils import BaseImageProcessor, BatchFeature
 from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
 from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
-from ...utils import logging
+from ...utils import TensorType, logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/tvlt/feature_extraction_tvlt.py

@@ -20,8 +20,8 @@ from typing import List, Optional, Union
 import numpy as np
 from numpy.fft import fft
 
-from transformers.feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
-from transformers.utils import TensorType, logging
+from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
+from ...utils import TensorType, logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/tvlt/image_processing_tvlt.py

@@ -17,8 +17,6 @@ from typing import Dict, List, Optional, Union
 
 import numpy as np
 
-from transformers.utils.generic import TensorType
-
 from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from ...image_transforms import (
     center_crop,
@@ -38,7 +36,7 @@ from ...image_utils import (
     to_numpy_array,
     valid_images,
 )
-from ...utils import logging
+from ...utils import TensorType, logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/upernet/modeling_upernet.py

@@ -20,8 +20,7 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
-from transformers import AutoBackbone
-
+from ... import AutoBackbone
 from ...modeling_outputs import SemanticSegmenterOutput
 from ...modeling_utils import BackboneMixin, PreTrainedModel
 from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings

src/transformers/models/videomae/image_processing_videomae.py

@@ -18,9 +18,6 @@ from typing import Dict, List, Optional, Union
 
 import numpy as np
 
-from transformers.utils import is_vision_available
-from transformers.utils.generic import TensorType
-
 from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from ...image_transforms import (
     center_crop,
@@ -40,7 +37,7 @@ from ...image_utils import (
     to_numpy_array,
     valid_images,
 )
-from ...utils import logging
+from ...utils import TensorType, is_vision_available, logging
 
 
 if is_vision_available():

src/transformers/models/vilt/image_processing_vilt.py

@@ -19,9 +19,6 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 
-from transformers.utils import is_vision_available
-from transformers.utils.generic import TensorType
-
 from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from ...image_transforms import PaddingMode, normalize, pad, rescale, resize, to_channel_dimension_format
 from ...image_utils import (
@@ -36,7 +33,7 @@ from ...image_utils import (
     to_numpy_array,
     valid_images,
 )
-from ...utils import logging
+from ...utils import TensorType, is_vision_available, logging
 
 
 if is_vision_available():

src/transformers/models/vit/image_processing_vit.py

@@ -18,8 +18,6 @@ from typing import Dict, List, Optional, Union
 
 import numpy as np
 
-from transformers.utils.generic import TensorType
-
 from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
 from ...image_utils import (
@@ -32,7 +30,7 @@ from ...image_utils import (
     to_numpy_array,
     valid_images,
 )
-from ...utils import logging
+from ...utils import TensorType, logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/vit_hybrid/image_processing_vit_hybrid.py

@@ -18,8 +18,6 @@ from typing import Dict, List, Optional, Union
 
 import numpy as np
 
-from transformers.utils.generic import TensorType
-
 from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from ...image_transforms import (
     center_crop,
@@ -40,8 +38,7 @@ from ...image_utils import (
     to_numpy_array,
     valid_images,
 )
-from ...utils import logging
-from ...utils.import_utils import is_vision_available
+from ...utils import TensorType, is_vision_available, logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/yolos/image_processing_yolos.py

@@ -20,9 +20,9 @@ from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Un
 
 import numpy as np
 
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.image_processing_utils import BaseImageProcessor, get_size_dict
-from transformers.image_transforms import (
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils import BaseImageProcessor, get_size_dict
+from ...image_transforms import (
     PaddingMode,
     center_to_corners_format,
     corners_to_center_format,
@@ -34,7 +34,7 @@ from transformers.image_transforms import (
     rgb_to_id,
     to_channel_dimension_format,
 )
-from transformers.image_utils import (
+from ...image_utils import (
     IMAGENET_DEFAULT_MEAN,
     IMAGENET_DEFAULT_STD,
     ChannelDimension,
@@ -48,7 +48,9 @@ from transformers.image_utils import (
     valid_coco_panoptic_annotations,
     valid_images,
 )
-from transformers.utils import (
+from ...utils import (
+    ExplicitEnum,
+    TensorType,
     is_flax_available,
     is_jax_tensor,
     is_scipy_available,
@@ -57,8 +59,8 @@ from transformers.utils import (
     is_torch_available,
     is_torch_tensor,
     is_vision_available,
+    logging,
 )
-from transformers.utils.generic import ExplicitEnum, TensorType
 
 
 if is_torch_available():
@@ -74,6 +76,7 @@ if is_scipy_available():
     import scipy.special
     import scipy.stats
 
+logger = logging.get_logger(__name__)
 
 AnnotationType = Dict[str, Union[int, str, List[Dict]]]
 

src/transformers/pipelines/pt_utils.py

@@ -2,7 +2,7 @@ import numpy as np
 import torch
 from torch.utils.data import Dataset, IterableDataset
 
-from transformers.utils.generic import ModelOutput
+from ..utils.generic import ModelOutput
 
 
 class PipelineDataset(Dataset):

src/transformers/pipelines/text_generation.py

 import enum
 import warnings
 
-from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
-
+from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
 from ..utils import add_end_docstrings, is_tf_available
 from .base import PIPELINE_INIT_ARGS, Pipeline
 

src/transformers/sagemaker/training_args_sm.py

@@ -20,8 +20,8 @@ from dataclasses import dataclass, field
 
 import torch
 
-from transformers.training_args import TrainingArguments
-from transformers.utils import cached_property, is_sagemaker_dp_enabled, logging
+from ..training_args import TrainingArguments
+from ..utils import cached_property, is_sagemaker_dp_enabled, logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/utils/bitsandbytes.py

 from copy import deepcopy
 
-from transformers.utils import is_accelerate_available, is_bitsandbytes_available
+from .import_utils import is_accelerate_available, is_bitsandbytes_available
 
 
 if is_bitsandbytes_available():

src/transformers/utils/hub.py

@@ -49,8 +49,6 @@ from huggingface_hub.utils import (
 )
 from requests.exceptions import HTTPError
 
-from transformers.utils.logging import tqdm
-
 from . import __version__, logging
 from .generic import working_or_temp_dir
 from .import_utils import (
@@ -61,6 +59,7 @@ from .import_utils import (
     is_torch_available,
     is_training_run_on_sagemaker,
 )
+from .logging import tqdm
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

src/transformers/utils/import_utils.py

@@ -29,9 +29,8 @@ from typing import Any
 
 from packaging import version
 
-from transformers.utils.versions import importlib_metadata
-
 from . import logging
+from .versions import importlib_metadata
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name