
'''simple docstring'''

import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin


class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DownBlockaD  # noqa F405
     a_    	=					"down"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							[-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					ResnetDownsampleBlockaD  # noqa F405
     a_    	=					"down"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							[0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					AttnDownBlockaD  # noqa F405
     a_    	=					"down"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							[0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					CrossAttnDownBlockaD  # noqa F405
     a_    	=					"down"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							super().prepare_init_args_and_inputs_for_common()
       a_      :	Union[str, Any]      =							32
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							[0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					SimpleCrossAttnDownBlockaD  # noqa F405
     a_    	=					"down"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_encoder_hidden_states=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							super().prepare_init_args_and_inputs_for_common()
       a_      :	List[Any]      =							32
       return init_dict, inputs_dict






     @unittest.skipIf(torch_device == """mps"""							,		"""MPS result is not consistent""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							[0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					SkipDownBlockaD  # noqa F405
     a_    	=					"down"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_skip_sample=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							[-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					AttnSkipDownBlockaD  # noqa F405
     a_    	=					"down"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_skip_sample=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							[0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DownEncoderBlockaD  # noqa F405
     a_    	=					"down"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_temb=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							{
           """in_channels""": 32,
           """out_channels""": 32,
       }
       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							[1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					AttnDownEncoderBlockaD  # noqa F405
     a_    	=					"down"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_temb=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							{
           """in_channels""": 32,
           """out_channels""": 32,
       }
       a_      :	List[Any]      =							self.dummy_input
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							[0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					UNetMidBlockaD  # noqa F405
     a_    	=					"mid"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							{
           """in_channels""": 32,
           """temb_channels""": 1_28,
       }
       a_      :	str      =							self.dummy_input
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							[-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					UNetMidBlockaDCrossAttn  # noqa F405
     a_    	=					"mid"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							super().prepare_init_args_and_inputs_for_common()
       a_      :	int      =							32
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							[0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					UNetMidBlockaDSimpleCrossAttn  # noqa F405
     a_    	=					"mid"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_encoder_hidden_states=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							super().prepare_init_args_and_inputs_for_common()
       a_      :	Optional[int]      =							32
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							[0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					UpBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							[-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					ResnetUpsampleBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							[0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					CrossAttnUpBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							super().prepare_init_args_and_inputs_for_common()
       a_      :	List[Any]      =							32
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							[-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					SimpleCrossAttnUpBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_							,		include_encoder_hidden_states=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							super().prepare_init_args_and_inputs_for_common()
       a_      :	Any      =							32
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							[0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					AttnUpBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_ )






     @unittest.skipIf(torch_device == """mps"""							,		"""MPS result is not consistent""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							[0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					SkipUpBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							[-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					AttnSkipUpBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							[0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					UpDecoderBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_temb=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							{"""in_channels""": 32, """out_channels""": 32}

       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							[0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
       super().test_output(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					AttnUpDecoderBlockaD  # noqa F405
     a_    	=					"up"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return super().get_dummy_input(include_temb=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							{"""in_channels""": 32, """out_channels""": 32}

       a_      :	Optional[Any]      =							self.dummy_input
       return init_dict, inputs_dict






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							[0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
       super().test_output(lowerCAmelCase_ )




'''simple docstring'''

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__snake_case:  Dict								=			logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		*lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(*lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							eval_examples
       a_      :	Optional[Any]      =							post_process_function
       a_      :	List[str]      =							quant_trainer_args
       a_      :	List[str]      =							1_28  # default number of calibration samples


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
       a_      :	str      =							calib_dataset if calib_dataset is not None else self.calib_dataset

       a_      :	Dict      =							self._remove_unused_columns(lowerCAmelCase_							,		description="""Calibration""" )

       return DataLoader(
           lowerCAmelCase_							,		batch_size=self.args.eval_batch_size							,		collate_fn=self.data_collator							,		drop_last=self.args.dataloader_drop_last							,		num_workers=self.args.dataloader_num_workers							,		pin_memory=self.args.dataloader_pin_memory							,		shuffle=lowerCAmelCase_							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.train_dataset if calib_dataset is None else calib_dataset
       a_      :	int      =							self.get_calib_dataloader(lowerCAmelCase_ )

       a_      :	Dict      =							self.model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args							,		calib=lowerCAmelCase_ )
       model.eval()
       quant_trainer.enable_calibration(lowerCAmelCase_ )

       logger.info("""***** Running calibration *****""" )
       logger.info(f'''  Num examples = {self.calib_num}''' )
       logger.info(f'''  Batch size = {calib_dataloader.batch_size}''' )

       for step, inputs in enumerate(lowerCAmelCase_ ):
         # Prediction step
         a_      , a_      , a_      :	Optional[int]      =							self.prediction_step(lowerCAmelCase_							,		lowerCAmelCase_							,		prediction_loss_only=lowerCAmelCase_ )
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
           break

       quant_trainer.finish_calibration(lowerCAmelCase_							,		self.quant_trainer_args )
       a_      :	Optional[int]      =							model


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "eval" ):

       '''simple docstring'''

       a_      :	str      =							self.eval_dataset if eval_dataset is None else eval_dataset
       a_      :	List[str]      =							self.get_eval_dataloader(lowerCAmelCase_ )
       a_      :	str      =							self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	List[str]      =							self.compute_metrics
       a_      :	Tuple      =							None
       a_      :	Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	List[str]      =							eval_loop(
             lowerCAmelCase_							,		description="""Evaluation"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is not None and self.compute_metrics is not None:
         a_      :	Optional[Any]      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions )
         a_      :	Tuple      =							self.compute_metrics(lowerCAmelCase_ )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys() ):
           if not key.startswith(f'''{metric_key_prefix}_''' ):
             a_      :	Optional[int]      =							metrics.pop(lowerCAmelCase_ )

         self.log(lowerCAmelCase_ )
       else:
         a_      :	Any      =							{}

       if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report() )

       a_      :	Optional[Any]      =							self.callback_handler.on_evaluate(self.args							,		self.state							,		self.control							,		lowerCAmelCase_ )
       return metrics


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "test" ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.get_test_dataloader(lowerCAmelCase_ )

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	Any      =							self.compute_metrics
       a_      :	Dict      =							None
       a_      :	Dict      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	Dict      =							eval_loop(
             lowerCAmelCase_							,		description="""Prediction"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is None or self.compute_metrics is None:
         return output

       a_      :	Tuple      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions							,		"""predict""" )
       a_      :	List[Any]      =							self.compute_metrics(lowerCAmelCase_ )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
         if not key.startswith(f'''{metric_key_prefix}_''' ):
           a_      :	Tuple      =							metrics.pop(lowerCAmelCase_ )

       return PredictionOutput(predictions=predictions.predictions							,		label_ids=predictions.label_ids							,		metrics=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_="./" ):

       '''simple docstring'''

       a_      :	Any      =							self.eval_dataset
       a_      :	Tuple      =							self.get_eval_dataloader(lowerCAmelCase_ )

       a_      :	List[str]      =							next(iter(lowerCAmelCase_ ) )

       # saving device - to make it consistent
       a_      :	Dict      =							torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

       # convert to tuple
       a_      :	Optional[int]      =							tuple(v.to(lowerCAmelCase_ ) for k, v in batch.items() )

       logger.info("""Converting model to be onnx compatible""" )
       from pytorch_quantization.nn import TensorQuantizer

       a_      :	List[str]      =							True

       a_      :	str      =							self.model.to(lowerCAmelCase_ )

       model.eval()
       model.float()

       a_      :	Optional[int]      =							model.module if hasattr(lowerCAmelCase_							,		"""module""" ) else model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args )

       a_      :	Union[str, Any]      =							os.path.join(lowerCAmelCase_							,		"""model.onnx""" )
       logger.info(f'''exporting model to {output_model_file}''' )

       a_      :	Dict      =							{0: """batch_size""", 1: """seq_len"""}

       torch.onnx.export(
           lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		export_params=lowerCAmelCase_							,		opset_version=13							,		do_constant_folding=lowerCAmelCase_							,		input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]							,		output_names=["""output_start_logits""", """output_end_logits"""]							,		dynamic_axes={
               """input_ids""": axes,
               """attention_mask""": axes,
               """token_type_ids""": axes,
               """output_start_logits""": axes,
               """output_end_logits""": axes,
           }							,		verbose=lowerCAmelCase_							,		)
       logger.info("""onnx export finished""" )


