





'''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


_SCREAMING_SNAKE_CASE									=				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  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  *_lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase		)     ->				Any:
     super().__init__(*_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   eval_examples
     _lowerCAmelCase						      =   post_process_function
     _lowerCAmelCase						      =   quant_trainer_args
     _lowerCAmelCase						      =   128  # default number of calibration samples




 def 				_snake_case (						self      ,  _lowerCAmelCase=None		)     ->				Optional[int]:
     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(_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 				_snake_case (						self      ,  _lowerCAmelCase=None		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.train_dataset if calib_dataset is None else calib_dataset
     _lowerCAmelCase						      =   self.get_calib_dataloader(_lowerCAmelCase		)

     _lowerCAmelCase						      =   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
         _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   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		)
     _lowerCAmelCase						      =   model




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase = "eval"		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.eval_dataset if eval_dataset is None else eval_dataset
     _lowerCAmelCase						      =   self.get_eval_dataloader(_lowerCAmelCase		)
     _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(
             _lowerCAmelCase      ,  description="Evaluation"      ,  prediction_loss_only=True if compute_metrics is None else None      ,  ignore_keys=_lowerCAmelCase      ,  )
     finally:
         _lowerCAmelCase						      =   compute_metrics

     if self.post_process_function is not None and self.compute_metrics is not None:
         _lowerCAmelCase						      =   self.post_process_function(_lowerCAmelCase      ,  _lowerCAmelCase      ,  output.predictions		)
         _lowerCAmelCase						      =   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}_'''		):
                 _lowerCAmelCase						      =   metrics.pop(_lowerCAmelCase		)

         self.log(_lowerCAmelCase		)
     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      ,  _lowerCAmelCase		)
     return metrics




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase = "test"		)     ->				Any:
     _lowerCAmelCase						      =   self.get_test_dataloader(_lowerCAmelCase		)

     # 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(
             _lowerCAmelCase      ,  description="Prediction"      ,  prediction_loss_only=True if compute_metrics is None else None      ,  ignore_keys=_lowerCAmelCase      ,  )
     finally:
         _lowerCAmelCase						      =   compute_metrics

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

     _lowerCAmelCase						      =   self.post_process_function(_lowerCAmelCase      ,  _lowerCAmelCase      ,  output.predictions      ,  "predict"		)
     _lowerCAmelCase						      =   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}_'''		):
             _lowerCAmelCase						      =   metrics.pop(_lowerCAmelCase		)

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





 def 				_snake_case (						self      ,  _lowerCAmelCase="./"		)     ->				int:
     _lowerCAmelCase						      =   self.eval_dataset
     _lowerCAmelCase						      =   self.get_eval_dataloader(_lowerCAmelCase		)

     _lowerCAmelCase						      =   next(iter(_lowerCAmelCase		)		)

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

     # convert to tuple
     _lowerCAmelCase						      =   tuple(v.to(_lowerCAmelCase		) 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(_lowerCAmelCase		)

     model.eval()
     model.float()

     _lowerCAmelCase						      =   model.module if hasattr(_lowerCAmelCase      ,  "module"		) else model
     quant_trainer.configure_model(_lowerCAmelCase      ,  self.quant_trainer_args		)

     _lowerCAmelCase						      =   os.path.join(_lowerCAmelCase      ,  "model.onnx"		)
     logger.info(f'''exporting model to {output_model_file}'''		)

     _lowerCAmelCase						      =   {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


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


