fix style

Author: echarlaix

optimum/exporters/base.py

@@ -14,28 +14,15 @@
 # limitations under the License.
 """Base exporters config."""
 
-from abc import ABC
-
-
-
 import copy
-import enum
-import gc
-import inspect
-import itertools
-import os
-import re
 from abc import ABC, abstractmethod
-from collections import OrderedDict
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
-import numpy as np
-from transformers.utils import is_accelerate_available, is_torch_available
+from transformers.utils import is_torch_available
 
 
 if is_torch_available():
-    import torch.nn as nn
+    pass
 
 from .utils import (
     DEFAULT_DUMMY_SHAPES,
@@ -46,19 +33,18 @@
 from .utils import TRANSFORMERS_MINIMUM_VERSION as GLOBAL_MIN_TRANSFORMERS_VERSION
 from .utils.doc import add_dynamic_docstring
 from .utils.import_utils import is_torch_version, is_transformers_version
-from .error_utils import MissingMandatoryAxisDimension
+
 
 # from .model_patcher import ModelPatcher
 
 if TYPE_CHECKING:
-    from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel
+    from transformers import PretrainedConfig
 
     from .model_patcher import PatchingSpec
 
 logger = logging.get_logger(__name__)
 
 
-
 GENERATE_DUMMY_DOCSTRING = r"""
         Generates the dummy inputs necessary for tracing the model. If not explicitely specified, default input shapes are used.
 
@@ -90,13 +76,11 @@
 """
 
 
-
 # TODO: Remove
 class ExportConfig(ABC):
     pass
 
 
-
 class ExportersConfig(ABC):
     """
     Base class describing metadata on how to export the model through the ONNX format.
@@ -141,19 +125,19 @@ class ExportersConfig(ABC):
         "audio-xvector": ["logits"],  # for onnx :  ["logits", "embeddings"]
         "depth-estimation": ["predicted_depth"],
         "document-question-answering": ["logits"],
-        "feature-extraction": ["last_hidden_state"], # for neuron : ["last_hidden_state", "pooler_output"]
+        "feature-extraction": ["last_hidden_state"],  # for neuron : ["last_hidden_state", "pooler_output"]
         "fill-mask": ["logits"],
         "image-classification": ["logits"],
         "image-segmentation": ["logits"],  # for tflite : ["logits", "pred_boxes", "pred_masks"]
         "image-to-text": ["logits"],
         "image-to-image": ["reconstruction"],
         "mask-generation": ["logits"],
-        "masked-im": ["logits"], # for onnx :  ["reconstruction"]
+        "masked-im": ["logits"],  # for onnx :  ["reconstruction"]
         "multiple-choice": ["logits"],
         "object-detection": ["logits", "pred_boxes"],
         "question-answering": ["start_logits", "end_logits"],
         "semantic-segmentation": ["logits"],
-        "text2text-generation": ["logits"], # for tflite : ["logits", "encoder_last_hidden_state"],
+        "text2text-generation": ["logits"],  # for tflite : ["logits", "encoder_last_hidden_state"],
         "text-classification": ["logits"],
         "text-generation": ["logits"],
         "time-series-forecasting": ["prediction_outputs"],
@@ -179,7 +163,6 @@ def __init__(
         self.mandatory_axes = ()
         self._axes: Dict[str, int] = {}
 
-
     def _create_dummy_input_generator_classes(self, **kwargs) -> List[DummyInputGenerator]:
         """
         Instantiates the dummy input generators from `self.DUMMY_INPUT_GENERATOR_CLASSES`.
@@ -190,7 +173,6 @@ def _create_dummy_input_generator_classes(self, **kwargs) -> List[DummyInputGene
         # self._validate_mandatory_axes()
         return [cls_(self.task, self._normalized_config, **kwargs) for cls_ in self.DUMMY_INPUT_GENERATOR_CLASSES]
 
-
     @property
     @abstractmethod
     def inputs(self) -> Dict[str, Dict[int, str]]:
@@ -213,7 +195,6 @@ def outputs(self) -> Dict[str, Dict[int, str]]:
         common_outputs = self._TASK_TO_COMMON_OUTPUTS[self.task]
         return copy.deepcopy(common_outputs)
 
-
     @property
     def values_override(self) -> Optional[Dict[str, Any]]:
         """
@@ -251,18 +232,15 @@ def is_torch_support_available(self) -> bool:
 
         return False
 
-
     @add_dynamic_docstring(text=GENERATE_DUMMY_DOCSTRING, dynamic_elements=DEFAULT_DUMMY_SHAPES)
     def generate_dummy_inputs(self, framework: str = "pt", **kwargs) -> Dict:
-
         """
         Generates dummy inputs that the exported model should be able to process.
         This method is actually used to determine the input specs that are needed for the export.
 
         Returns:
             `Dict[str, [tf.Tensor, torch.Tensor]]`: A dictionary mapping input names to dummy tensors.
         """
-                
 
         dummy_inputs_generators = self._create_dummy_input_generator_classes(**kwargs)
         dummy_inputs = {}
@@ -303,5 +281,4 @@ def flatten_inputs(cls, inputs: Dict[str, Any]) -> Dict[str, Any]:
     # ) -> ModelPatcher:
     #    return ModelPatcher(self, model, model_kwargs=model_kwargs)
 
-
     ############################################################################################################################################################

optimum/exporters/onnx/base.py

@@ -21,7 +21,7 @@
 import itertools
 import os
 import re
-from abc import ABC, abstractmethod
+from abc import ABC
 from collections import OrderedDict
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
@@ -41,16 +41,13 @@
     is_diffusers_available,
     logging,
 )
-from ...utils import TORCH_MINIMUM_VERSION as GLOBAL_MIN_TORCH_VERSION
-from ...utils import TRANSFORMERS_MINIMUM_VERSION as GLOBAL_MIN_TRANSFORMERS_VERSION
 from ...utils.doc import add_dynamic_docstring
 from ...utils.import_utils import (
     is_onnx_available,
     is_onnxruntime_available,
-    is_torch_version,
     is_transformers_version,
 )
-from ..base import ExportConfig, ExportersConfig
+from ..base import ExportersConfig
 from .constants import ONNX_DECODER_MERGED_NAME, ONNX_DECODER_NAME, ONNX_DECODER_WITH_PAST_NAME
 from .model_patcher import ModelPatcher, Seq2SeqModelPatcher
 
@@ -66,7 +63,6 @@
     if is_diffusers_available():
         from diffusers import ModelMixin
 
-    from .model_patcher import PatchingSpec
 
 logger = logging.get_logger(__name__)
 
@@ -103,7 +99,6 @@
 
 
 class OnnxConfig(ExportersConfig):
-
     DEFAULT_ONNX_OPSET = 11
     VARIANTS = {"default": "The default ONNX variant."}
     DEFAULT_VARIANT = "default"
@@ -281,7 +276,6 @@ def patch_model_for_export(
         self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
     ) -> ModelPatcher:
         return ModelPatcher(self, model, model_kwargs=model_kwargs)
-    
 
     @property
     def torch_to_onnx_input_map(self) -> Dict[str, str]:
@@ -348,8 +342,6 @@ def ordered_inputs(self, model: Union["PreTrainedModel", "TFPreTrainedModel"]) -
                 ordered_inputs[name] = dynamic_axes
         return ordered_inputs
 
-
-
     # TODO: use instead flatten_inputs and remove
     @classmethod
     def flatten_output_collection_property(cls, name: str, field: Iterable[Any]) -> Dict[str, Any]:

optimum/exporters/tflite/base.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 """TensorFlow Lite configuration base classes."""
 
-from abc import ABC, abstractmethod
+from abc import ABC
 from ctypes import ArgumentError
 from dataclasses import dataclass
 from enum import Enum
@@ -192,7 +192,6 @@ def __init__(
         point_batch_size: Optional[int] = None,
         nb_points_per_image: Optional[int] = None,
     ):
-
         super().__init__(config=config, task=task, int_dtype="int64", float_dtype="fp32")
 
         # self.mandatory_axes = ()
@@ -269,7 +268,6 @@ def _create_dummy_input_generator_classes(self) -> List["DummyInputGenerator"]:
     def generate_dummy_inputs(self) -> Dict[str, "tf.Tensor"]:
         return super().generate_dummy_inputs(framework="tf")
 
-
     @property
     def inputs_specs(self) -> List["TensorSpec"]:
         """

optimum/exporters/utils.py

@@ -545,7 +545,7 @@ def get_speecht5_models_for_export(
         use_past=use_past,
         use_past_in_inputs=False,  # Irrelevant here.
         behavior=config._behavior,  # Irrelevant here.
-        preprocessors=config._preprocessors,
+        # preprocessors=config._preprocessors,
         is_postnet_and_vocoder=True,
         legacy=config.legacy,
     )