import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase__     						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = {}
       with open(lowerCAmelCase__     ,  'r'     ) as file:
              for line_number, line in enumerate(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = line.strip()
                     if line:
                            lowerCAmelCase__					     = line.split()
                            lowerCAmelCase__					     = line_number
                            lowerCAmelCase__					     = words[0]
                            lowerCAmelCase__					     = value
       return result






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for attribute in key.split('.'     ):
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     ).shape
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = hf_pointer
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = shape_pointer.shape

              # let's reduce dimension
              lowerCAmelCase__					     = value[0]
       else:
              lowerCAmelCase__					     = hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
                  F""" {value.shape} for {full_name}"""     )

       if weight_type == "weight":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_g":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_v":
              lowerCAmelCase__					     = value
       elif weight_type == "bias":
              lowerCAmelCase__					     = value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = value
       else:
              lowerCAmelCase__					     = value

       logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = '.'.join([key, weight_type]     )
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = '.'.join([key, hf_param_name]     )
       else:
              lowerCAmelCase__					     = key

       lowerCAmelCase__					     = value if 'lm_head' in full_key else value[0]


lowerCAmelCase__     						=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ):
       lowerCAmelCase__					     = False
       for key, mapped_key in MAPPING.items():
              lowerCAmelCase__					     = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
              if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
                     lowerCAmelCase__					     = True
                     if "*" in mapped_key:
                            lowerCAmelCase__					     = name.split(lowerCAmelCase__     )[0].split('.'     )[-2]
                            lowerCAmelCase__					     = mapped_key.replace('*'     ,  lowerCAmelCase__     )
                     if "weight_g" in name:
                            lowerCAmelCase__					     = 'weight_g'
                     elif "weight_v" in name:
                            lowerCAmelCase__					     = 'weight_v'
                     elif "bias" in name:
                            lowerCAmelCase__					     = 'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            lowerCAmelCase__					     = 'weight'
                     else:
                            lowerCAmelCase__					     = None
                     if hf_dict is not None:
                            rename_dict(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     else:
                            set_recursively(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     return is_used
       return is_used






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = fairseq_model.state_dict()

       lowerCAmelCase__					     = hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCAmelCase__					     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
                     lowerCAmelCase__					     = True
              else:
                     lowerCAmelCase__					     = load_wavaveca_layer(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              if not is_used:
                     unused_weights.append(lowerCAmelCase__     )

       logger.warning(F"""Unused weights: {unused_weights}"""     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = full_name.split('conv_layers.'     )[-1]
       lowerCAmelCase__					     = name.split('.'     )
       lowerCAmelCase__					     = int(items[0]     )
       lowerCAmelCase__					     = int(items[1]     )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
       else:
              unused_weights.append(lowerCAmelCase__     )






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=False     ):
       if config_path is not None:
              lowerCAmelCase__					     = WavaVecaConfig.from_pretrained(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaConfig()

       if is_seq_class:
              lowerCAmelCase__					     = read_txt_into_dict(lowerCAmelCase__     )
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = WavaVecaForSequenceClassification(lowerCAmelCase__     )
              lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                  feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
              feature_extractor.save_pretrained(lowerCAmelCase__     )

       elif is_finetuned:
              if dict_path:
                     lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCAmelCase__					     = target_dict.pad_index
                     lowerCAmelCase__					     = target_dict.bos_index
                     lowerCAmelCase__					     = target_dict.eos_index
                     lowerCAmelCase__					     = len(target_dict.symbols     )
                     lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  'vocab.json'     )
                     if not os.path.isdir(lowerCAmelCase__     ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase__     )     )
                            return
                     os.makedirs(lowerCAmelCase__     ,  exist_ok=lowerCAmelCase__     )
                     lowerCAmelCase__					     = target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     lowerCAmelCase__					     = 0
                     lowerCAmelCase__					     = 1
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as vocab_handle:
                            json.dump(lowerCAmelCase__     ,  lowerCAmelCase__     )
                     lowerCAmelCase__					     = WavaVecaCTCTokenizer(
                         lowerCAmelCase__     ,  unk_token=target_dict.unk_word     ,  pad_token=target_dict.pad_word     ,  bos_token=target_dict.bos_word     ,  eos_token=target_dict.eos_word     ,  word_delimiter_token='|'     ,  do_lower_case=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = True if config.feat_extract_norm == 'layer' else False
                     lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                         feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = WavaVecaProcessor(feature_extractor=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     )
                     processor.save_pretrained(lowerCAmelCase__     )

              lowerCAmelCase__					     = WavaVecaForCTC(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaForPreTraining(lowerCAmelCase__     )

       if is_finetuned or is_seq_class:
              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
       else:
              lowerCAmelCase__					     = argparse.Namespace(task='audio_pretraining'     )
              lowerCAmelCase__					     = fairseq.tasks.setup_task(lowerCAmelCase__     )

              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=lowerCAmelCase__     )

       lowerCAmelCase__					     = model[0].eval()

       recursively_load_weights(lowerCAmelCase__     ,  lowerCAmelCase__     ,  not is_finetuned     )

       hf_wavavec.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    lowerCAmelCase__     						=			parser.parse_args()

    lowerCAmelCase__     						=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )

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


lowerCAmelCase__     						=			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     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  int      ,   *lowercase__ :  Dict      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  Dict=None      ,   **lowercase__ :  Optional[int]):







        '''simple docstring'''



        super().__init__(*lowercase__      ,   **lowercase__)
        lowerCAmelCase__					     = eval_examples
        lowerCAmelCase__					     = post_process_function
        lowerCAmelCase__					     = quant_trainer_args
        lowerCAmelCase__					     = 128  # default number of calibration samples




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  Any=None):







        '''simple docstring'''



        if calib_dataset is None and self.calib_dataset is None:
               raise ValueError('Trainer: calibration requires an calib_dataset.')
        lowerCAmelCase__					     = calib_dataset if calib_dataset is not None else self.calib_dataset

        lowerCAmelCase__					     = self._remove_unused_columns(lowercase__      ,   description='Calibration')

        return DataLoader(
            lowercase__      ,   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=lowercase__      ,   )




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Union[str, Any]=None):







        '''simple docstring'''



        lowerCAmelCase__					     = self.train_dataset if calib_dataset is None else calib_dataset
        lowerCAmelCase__					     = self.get_calib_dataloader(lowercase__)

        lowerCAmelCase__					     = self.model
        quant_trainer.configure_model(lowercase__      ,   self.quant_trainer_args      ,   calib=lowercase__)
        model.eval()
        quant_trainer.enable_calibration(lowercase__)

        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(lowercase__):
               # Prediction step
               lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = self.prediction_step(lowercase__      ,   lowercase__      ,   prediction_loss_only=lowercase__)
               if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                      break

        quant_trainer.finish_calibration(lowercase__      ,   self.quant_trainer_args)
        lowerCAmelCase__					     = model




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  str = "eval"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.eval_dataset if eval_dataset is None else eval_dataset
        lowerCAmelCase__					     = self.get_eval_dataloader(lowercase__)
        lowerCAmelCase__					     = self.eval_examples if eval_examples is None else eval_examples

        # Temporarily disable metric computation, we will do it in the loop here.
        lowerCAmelCase__					     = self.compute_metrics
        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
               lowerCAmelCase__					     = eval_loop(
                   lowercase__      ,   description='Evaluation'      ,   prediction_loss_only=True if compute_metrics is None else None      ,   ignore_keys=lowercase__      ,   )
        finally:
               lowerCAmelCase__					     = compute_metrics

        if self.post_process_function is not None and self.compute_metrics is not None:
               lowerCAmelCase__					     = self.post_process_function(lowercase__      ,   lowercase__      ,   output.predictions)
               lowerCAmelCase__					     = self.compute_metrics(lowercase__)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()):
                      if not key.startswith(F"""{metric_key_prefix}_"""):
                             lowerCAmelCase__					     = metrics.pop(lowercase__)

               self.log(lowercase__)
        else:
               lowerCAmelCase__					     = {}

        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())

        lowerCAmelCase__					     = self.callback_handler.on_evaluate(self.args      ,   self.state      ,   self.control      ,   lowercase__)
        return metrics




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  str      ,   lowercase__ :  Any      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  str = "test"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_test_dataloader(lowercase__)

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

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

        lowerCAmelCase__					     = self.post_process_function(lowercase__      ,   lowercase__      ,   output.predictions      ,   'predict')
        lowerCAmelCase__					     = self.compute_metrics(lowercase__)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
               if not key.startswith(F"""{metric_key_prefix}_"""):
                      lowerCAmelCase__					     = metrics.pop(lowercase__)

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




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  List[str]="./"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.eval_dataset
        lowerCAmelCase__					     = self.get_eval_dataloader(lowercase__)

        lowerCAmelCase__					     = next(iter(lowercase__))

        # saving device - to make it consistent
        lowerCAmelCase__					     = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        # convert to tuple
        lowerCAmelCase__					     = tuple(v.to(lowercase__) for k, v in batch.items())

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

        lowerCAmelCase__					     = True

        lowerCAmelCase__					     = self.model.to(lowercase__)

        model.eval()
        model.float()

        lowerCAmelCase__					     = model.module if hasattr(lowercase__      ,   'module') else model
        quant_trainer.configure_model(lowercase__      ,   self.quant_trainer_args)

        lowerCAmelCase__					     = os.path.join(lowercase__      ,   'model.onnx')
        logger.info(F"""exporting model to {output_model_file}""")

        lowerCAmelCase__					     = {0: 'batch_size', 1: 'seq_len'}

        torch.onnx.export(
            lowercase__      ,   lowercase__      ,   lowercase__      ,   export_params=lowercase__      ,   opset_version=13      ,   do_constant_folding=lowercase__      ,   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=lowercase__      ,   )
        logger.info('onnx export finished')
