'''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


UpperCamelCase__     :	List[str]				=			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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,*lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



         super().__init__(*lowerCamelCase_					,**lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  eval_examples
         UpperCAmelCase__							:				str   				=  post_process_function
         UpperCAmelCase__							:				Any   				=  quant_trainer_args
         UpperCAmelCase__							:				Tuple   				=  128  # default number of calibration samples




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_=None							)			->			List[str]:



         '''simple docstring'''



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

         UpperCAmelCase__							:				Any   				=  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							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.train_dataset if calib_dataset is None else calib_dataset
         UpperCAmelCase__							:				int   				=  self.get_calib_dataloader(lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  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
            UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				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							)
         UpperCAmelCase__							:				Union[str, Any]   				=  model




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_ = "eval"							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.eval_dataset if eval_dataset is None else eval_dataset
         UpperCAmelCase__							:				int   				=  self.get_eval_dataloader(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  self.eval_examples if eval_examples is None else eval_examples

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

         if self.post_process_function is not None and self.compute_metrics is not None:
            UpperCAmelCase__							:				Union[str, Any]   				=  self.post_process_function(lowerCamelCase_					,lowerCamelCase_					,output.predictions							)
            UpperCAmelCase__							:				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}_'''							):
                  UpperCAmelCase__							:				Any   				=  metrics.pop(lowerCamelCase_							)

            self.log(lowerCamelCase_							)
         else:
            UpperCAmelCase__							:				Tuple   				=  {}

         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()							)

         UpperCAmelCase__							:				List[Any]   				=  self.callback_handler.on_evaluate(self.args					,self.state					,self.control					,lowerCamelCase_							)
         return metrics




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_ = "test"							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  self.get_test_dataloader(lowerCamelCase_							)

         # Temporarily disable metric computation, we will do it in the loop here.
         UpperCAmelCase__							:				List[str]   				=  self.compute_metrics
         UpperCAmelCase__							:				Dict   				=  None
         UpperCAmelCase__							:				Optional[Any]   				=  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         try:
            UpperCAmelCase__							:				Dict   				=  eval_loop(
                lowerCamelCase_					,description='''Prediction'''					,prediction_loss_only=True if compute_metrics is None else None					,ignore_keys=lowerCamelCase_					,)
         finally:
            UpperCAmelCase__							:				str   				=  compute_metrics

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

         UpperCAmelCase__							:				Tuple   				=  self.post_process_function(lowerCamelCase_					,lowerCamelCase_					,output.predictions					,'''predict'''							)
         UpperCAmelCase__							:				List[str]   				=  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}_'''							):
               UpperCAmelCase__							:				Optional[int]   				=  metrics.pop(lowerCamelCase_							)

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_="./"							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.eval_dataset
         UpperCAmelCase__							:				Tuple   				=  self.get_eval_dataloader(lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  next(iter(lowerCamelCase_							)							)

         # saving device - to make it consistent
         UpperCAmelCase__							:				Union[str, Any]   				=  torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)

         # convert to tuple
         UpperCAmelCase__							:				Tuple   				=  tuple(v.to(lowerCamelCase_							) for k, v in batch.items()							)

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

         UpperCAmelCase__							:				Tuple   				=  True

         UpperCAmelCase__							:				Dict   				=  self.model.to(lowerCamelCase_							)

         model.eval()
         model.float()

         UpperCAmelCase__							:				List[str]   				=  model.module if hasattr(lowerCamelCase_					,'''module'''							) else model
         quant_trainer.configure_model(lowerCamelCase_					,self.quant_trainer_args							)

         UpperCAmelCase__							:				List[str]   				=  os.path.join(lowerCamelCase_					,'''model.onnx'''							)
         logger.info(f'''exporting model to {output_model_file}'''							)

         UpperCAmelCase__							:				List[str]   				=  {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'''							)





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




