





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						:   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 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					*_A     ,					_A=None     ,					_A=None     ,					_A=None     ,					**_A     ):
									super().__init__(*_A     ,					**_A     )
									__A	:						Optional[Any]       =				eval_examples
									__A	:						Optional[Any]       =				post_process_function
									__A	:						int       =				quant_trainer_args
									__A	:						Dict       =				128  # default number of calibration samples



				def 					UpperCAmelCase_		(			self     ,					_A=None     ):
									if calib_dataset is None and self.calib_dataset is None:
														raise ValueError('Trainer: calibration requires an calib_dataset.'     )
									__A	:						List[str]       =				calib_dataset if calib_dataset is not None else self.calib_dataset

									__A	:						Dict       =				self._remove_unused_columns(_A     ,					description='Calibration'     )

									return DataLoader(
									    _A     ,					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=_A     ,					)



				def 					UpperCAmelCase_		(			self     ,					_A=None     ):
									__A	:						Any       =				self.train_dataset if calib_dataset is None else calib_dataset
									__A	:						List[Any]       =				self.get_calib_dataloader(_A     )

									__A	:						Tuple       =				self.model
									quant_trainer.configure_model(_A     ,					self.quant_trainer_args     ,					calib=_A     )
									model.eval()
									quant_trainer.enable_calibration(_A     )

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

									quant_trainer.finish_calibration(_A     ,					self.quant_trainer_args     )
									__A	:						Tuple       =				model



				def 					UpperCAmelCase_		(			self     ,					_A=None     ,					_A=None     ,					_A=None     ,					_A = "eval"     ):
									__A	:						int       =				self.eval_dataset if eval_dataset is None else eval_dataset
									__A	:						Optional[Any]       =				self.get_eval_dataloader(_A     )
									__A	:						Optional[Any]       =				self.eval_examples if eval_examples is None else eval_examples

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

									if self.post_process_function is not None and self.compute_metrics is not None:
														__A	:						Dict       =				self.post_process_function(_A     ,					_A     ,					output.predictions     )
														__A	:						Optional[Any]       =				self.compute_metrics(_A     )

														# Prefix all keys with metric_key_prefix + '_'
														for key in list(metrics.keys()     ):
																			if not key.startswith(F"""{metric_key_prefix}_"""     ):
																								__A	:						List[str]       =				metrics.pop(_A     )

														self.log(_A     )
									else:
														__A	:						str       =				{}

									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	:						Any       =				self.callback_handler.on_evaluate(self.args     ,					self.state     ,					self.control     ,					_A     )
									return metrics



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A=None     ,					_A = "test"     ):
									__A	:						Optional[int]       =				self.get_test_dataloader(_A     )

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

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

									__A	:						Any       =				self.post_process_function(_A     ,					_A     ,					output.predictions     ,					'predict'     )
									__A	:						str       =				self.compute_metrics(_A     )

									# 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(_A     )

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





				def 					UpperCAmelCase_		(			self     ,					_A="./"     ):
									__A	:						Tuple       =				self.eval_dataset
									__A	:						List[str]       =				self.get_eval_dataloader(_A     )

									__A	:						Union[str, Any]       =				next(iter(_A     )     )

									# saving device - to make it consistent
									__A	:						List[str]       =				torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )

									# convert to tuple
									__A	:						Any       =				tuple(v.to(_A     ) for k, v in batch.items()     )

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

									__A	:						str       =				True

									__A	:						str       =				self.model.to(_A     )

									model.eval()
									model.float()

									__A	:						str       =				model.module if hasattr(_A     ,					'module'     ) else model
									quant_trainer.configure_model(_A     ,					self.quant_trainer_args     )

									__A	:						List[Any]       =				os.path.join(_A     ,					'model.onnx'     )
									logger.info(F"""exporting model to {output_model_file}"""     )

									__A	:						str       =				{0: 'batch_size', 1: 'seq_len'}

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











def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




