'''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


lowercase__  :      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 lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       Tuple  ,							*UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       List[Any]=None  ,							UpperCAmelCase__			:       str=None  ,							UpperCAmelCase__			:       str=None  ,							**UpperCAmelCase__			:       List[str]  )				->Tuple:
        super().__init__(*UpperCAmelCase__  ,							**UpperCAmelCase__  )
        UpperCAmelCase_            =  eval_examples
        UpperCAmelCase_            =  post_process_function
        UpperCAmelCase_            =  quant_trainer_args
        UpperCAmelCase_            =  128  # default number of calibration samples






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       Tuple=None  )				->Union[str, Any]:
        if calib_dataset is None and self.calib_dataset is None:
              raise ValueError('''Trainer: calibration requires an calib_dataset.'''  )
        UpperCAmelCase_            =  calib_dataset if calib_dataset is not None else self.calib_dataset

        UpperCAmelCase_            =  self._remove_unused_columns(UpperCAmelCase__  ,							description='''Calibration'''  )

        return DataLoader(
            UpperCAmelCase__  ,							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=UpperCAmelCase__  ,							)






  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       Optional[int]=None  )				->Any:
        UpperCAmelCase_            =  self.train_dataset if calib_dataset is None else calib_dataset
        UpperCAmelCase_            =  self.get_calib_dataloader(UpperCAmelCase__  )

        UpperCAmelCase_            =  self.model
        quant_trainer.configure_model(UpperCAmelCase__  ,							self.quant_trainer_args  ,							calib=UpperCAmelCase__  )
        model.eval()
        quant_trainer.enable_calibration(UpperCAmelCase__  )

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

        quant_trainer.finish_calibration(UpperCAmelCase__  ,							self.quant_trainer_args  )
        UpperCAmelCase_            =  model






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       Tuple=None  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							UpperCAmelCase__			:       str = "eval"  )				->List[str]:
        UpperCAmelCase_            =  self.eval_dataset if eval_dataset is None else eval_dataset
        UpperCAmelCase_            =  self.get_eval_dataloader(UpperCAmelCase__  )
        UpperCAmelCase_            =  self.eval_examples if eval_examples is None else eval_examples

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

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

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

              self.log(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  {}

        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_            =  self.callback_handler.on_evaluate(self.args  ,							self.state  ,							self.control  ,							UpperCAmelCase__  )
        return metrics






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Dict=None  ,							UpperCAmelCase__			:       str = "test"  )				->Dict:
        UpperCAmelCase_            =  self.get_test_dataloader(UpperCAmelCase__  )

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

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

        UpperCAmelCase_            =  self.post_process_function(UpperCAmelCase__  ,							UpperCAmelCase__  ,							output.predictions  ,							'''predict'''  )
        UpperCAmelCase_            =  self.compute_metrics(UpperCAmelCase__  )

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

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






  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       Dict="./"  )				->List[str]:
        UpperCAmelCase_            =  self.eval_dataset
        UpperCAmelCase_            =  self.get_eval_dataloader(UpperCAmelCase__  )

        UpperCAmelCase_            =  next(iter(UpperCAmelCase__  )  )

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

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

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

        UpperCAmelCase_            =  True

        UpperCAmelCase_            =  self.model.to(UpperCAmelCase__  )

        model.eval()
        model.float()

        UpperCAmelCase_            =  model.module if hasattr(UpperCAmelCase__  ,							'''module'''  ) else model
        quant_trainer.configure_model(UpperCAmelCase__  ,							self.quant_trainer_args  )

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

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

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






'''simple docstring'''
from __future__ import annotations



def  __lowerCamelCase       (      _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  position
      UpperCAmelCase_            =  [
          (y + 1, x + 2),
          (y - 1, x + 2),
          (y + 1, x - 2),
          (y - 1, x - 2),
          (y + 2, x + 1),
          (y + 2, x - 1),
          (y - 2, x + 1),
          (y - 2, x - 1),
      ]
      UpperCAmelCase_            =  []

      for position in positions:
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position
            if 0 <= y_test < n and 0 <= x_test < n:
                  permissible_positions.append(_UpperCamelCase					)

      return permissible_positions



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]					):


      '''simple docstring'''


      return not any(elem == 0 for row in board for elem in row					)



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if is_complete(_UpperCamelCase					):
            return True

      for position in get_valid_pos(_UpperCamelCase							,    len(_UpperCamelCase					)					):
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position

            if board[y][x] == 0:
                  UpperCAmelCase_            =  curr + 1
                  if open_knight_tour_helper(_UpperCamelCase							,    _UpperCamelCase							,    curr + 1					):
                        return True
                  UpperCAmelCase_            =  0

      return False



def  __lowerCamelCase       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  [[0 for i in range(_UpperCamelCase					)] for j in range(_UpperCamelCase					)]

      for i in range(_UpperCamelCase					):
            for j in range(_UpperCamelCase					):
                  UpperCAmelCase_            =  1
                  if open_knight_tour_helper(_UpperCamelCase							,    (i, j)							,    1					):
                        return board
                  UpperCAmelCase_            =  0

      UpperCAmelCase_            =  F"""Open Kight Tour cannot be performed on a board of size {n}"""
      raise ValueError(_UpperCamelCase					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





