

'''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 				a  (	__snake_case				):


						def __init__(       self						,					*A_						,					A_=None						,					A_=None						,					A_=None						,					**A_	):







										'''simple docstring'''


										super().__init__(*A_						,					**A_	)
										_UpperCAmelCase       :		Tuple 		=    eval_examples
										_UpperCAmelCase       :		Dict 		=    post_process_function
										_UpperCAmelCase       :		Union[str, Any] 		=    quant_trainer_args
										_UpperCAmelCase       :		Any 		=    128  # default number of calibration samples


						def        _UpperCAmelCase				(       self						,					A_=None	):







										'''simple docstring'''


										if calib_dataset is None and self.calib_dataset is None:
														raise ValueError("Trainer: calibration requires an calib_dataset."	)
										_UpperCAmelCase       :		Any 		=    calib_dataset if calib_dataset is not None else self.calib_dataset

										_UpperCAmelCase       :		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	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.train_dataset if calib_dataset is None else calib_dataset
										_UpperCAmelCase       :		Dict 		=    self.get_calib_dataloader(A_	)

										_UpperCAmelCase       :		Optional[int] 		=    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
														_UpperCAmelCase       :		List[Any] 		=    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	)
										_UpperCAmelCase       :		str 		=    model


						def        _UpperCAmelCase				(       self						,					A_=None						,					A_=None						,					A_=None						,					A_ = "eval"	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.eval_dataset if eval_dataset is None else eval_dataset
										_UpperCAmelCase       :		Any 		=    self.get_eval_dataloader(A_	)
										_UpperCAmelCase       :		List[Any] 		=    self.eval_examples if eval_examples is None else eval_examples

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

										if self.post_process_function is not None and self.compute_metrics is not None:
														_UpperCAmelCase       :		List[str] 		=    self.post_process_function(A_						,					A_						,					output.predictions	)
														_UpperCAmelCase       :		Dict 		=    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}_'	):
																						_UpperCAmelCase       :		Optional[int] 		=    metrics.pop(A_	)

														self.log(A_	)
										else:
														_UpperCAmelCase       :		List[Any] 		=    {}

										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       :		Tuple 		=    self.callback_handler.on_evaluate(self.args						,					self.state						,					self.control						,					A_	)
										return metrics


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_=None						,					A_ = "test"	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    self.get_test_dataloader(A_	)

										# Temporarily disable metric computation, we will do it in the loop here.
										_UpperCAmelCase       :		int 		=    self.compute_metrics
										_UpperCAmelCase       :		Tuple 		=    None
										_UpperCAmelCase       :		int 		=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
										try:
														_UpperCAmelCase       :		Any 		=    eval_loop(
														    A_						,					description="Prediction"						,					prediction_loss_only=True if compute_metrics is None else None						,					ignore_keys=A_						,					)
										finally:
														_UpperCAmelCase       :		Dict 		=    compute_metrics

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

										_UpperCAmelCase       :		List[str] 		=    self.post_process_function(A_						,					A_						,					output.predictions						,					"predict"	)
										_UpperCAmelCase       :		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}_'	):
																		_UpperCAmelCase       :		Tuple 		=    metrics.pop(A_	)

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





						def        _UpperCAmelCase				(       self						,					A_="./"	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.eval_dataset
										_UpperCAmelCase       :		Dict 		=    self.get_eval_dataloader(A_	)

										_UpperCAmelCase       :		List[str] 		=    next(iter(A_	)	)

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

										# convert to tuple
										_UpperCAmelCase       :		Union[str, 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

										_UpperCAmelCase       :		str 		=    True

										_UpperCAmelCase       :		int 		=    self.model.to(A_	)

										model.eval()
										model.float()

										_UpperCAmelCase       :		Tuple 		=    model.module if hasattr(A_						,					"module"	) else model
										quant_trainer.configure_model(A_						,					self.quant_trainer_args	)

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

										_UpperCAmelCase       :		List[Any] 		=    {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"	)





import fire

from utils import calculate_rouge, save_json

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  Any )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()]
				_UpperCAmelCase       :		str 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()][: len(lowerCAmelCase )]
				_UpperCAmelCase       :		str 		=    calculate_rouge(lowerCAmelCase  ,      lowerCAmelCase  ,      **lowerCAmelCase )
				if save_path is not None:
								save_json(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase )
				return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)


