




'''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


A     =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  (	UpperCamelCase__ ):
       def __init__(	self						:	Any   ,				*lowercase						:	Optional[Any]   ,				lowercase						:	Any=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	List[Any]=None   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           super().__init__(*lowercase_   ,				**lowercase_				)
           UpperCAmelCase			       =		eval_examples
           UpperCAmelCase			       =		post_process_function
           UpperCAmelCase			       =		quant_trainer_args
           UpperCAmelCase			       =		128  # default number of calibration samples
       def     A      (	self						:	Optional[int]   ,				lowercase						:	List[str]=None				):



           '''simple docstring'''

           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(lowercase_   ,				description='''Calibration'''				)

           return DataLoader(
               lowercase_   ,				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=lowercase_   ,				)
       def     A      (	self						:	List[str]   ,				lowercase						:	Any=None				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.train_dataset if calib_dataset is None else calib_dataset
           UpperCAmelCase			       =		self.get_calib_dataloader(lowercase_				)

           UpperCAmelCase			       =		self.model
           quant_trainer.configure_model(lowercase_   ,				self.quant_trainer_args   ,				calib=lowercase_				)
           model.eval()
           quant_trainer.enable_calibration(lowercase_				)

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

           quant_trainer.finish_calibration(lowercase_   ,				self.quant_trainer_args				)
           UpperCAmelCase			       =		model
       def     A      (	self						:	Any   ,				lowercase						:	List[str]=None   ,				lowercase						:	Dict=None   ,				lowercase						:	Any=None   ,				lowercase						:	str = "eval"				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.eval_dataset if eval_dataset is None else eval_dataset
           UpperCAmelCase			       =		self.get_eval_dataloader(lowercase_				)
           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(
                   lowercase_   ,				description='''Evaluation'''   ,				prediction_loss_only=True if compute_metrics is None else None   ,				ignore_keys=lowercase_   ,				)
           finally:
               UpperCAmelCase			       =		compute_metrics

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

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

               self.log(lowercase_				)
           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   ,				lowercase_				)
           return metrics
       def     A      (	self						:	List[str]   ,				lowercase						:	str   ,				lowercase						:	Optional[Any]   ,				lowercase						:	str=None   ,				lowercase						:	str = "test"				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_test_dataloader(lowercase_				)

           # 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(
                   lowercase_   ,				description='''Prediction'''   ,				prediction_loss_only=True if compute_metrics is None else None   ,				ignore_keys=lowercase_   ,				)
           finally:
               UpperCAmelCase			       =		compute_metrics

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

           UpperCAmelCase			       =		self.post_process_function(lowercase_   ,				lowercase_   ,				output.predictions   ,				'''predict'''				)
           UpperCAmelCase			       =		self.compute_metrics(lowercase_				)

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

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







       def     A      (	self						:	Any   ,				lowercase						:	Dict="./"				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.eval_dataset
           UpperCAmelCase			       =		self.get_eval_dataloader(lowercase_				)

           UpperCAmelCase			       =		next(iter(lowercase_				)				)

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

           # convert to tuple
           UpperCAmelCase			       =		tuple(v.to(lowercase_				) 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(lowercase_				)

           model.eval()
           model.float()

           UpperCAmelCase			       =		model.module if hasattr(lowercase_   ,				'''module'''				) else model
           quant_trainer.configure_model(lowercase_   ,				self.quant_trainer_args				)

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

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

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











'''simple docstring'''

def 						UpperCamelCase				(							_lowerCamelCase					:					int | float | str       ):
    try:
        A__				=			float(_lowerCamelCase       )
    except ValueError:
        raise ValueError("Please enter a valid number"       )
    A__				=			decimal - int(_lowerCamelCase       )
    if fractional_part == 0:
        return int(_lowerCamelCase       ), 1
    else:
        A__				=			len(str(_lowerCamelCase       ).split("."       )[1]       )
        A__				=			int(decimal * (10**number_of_frac_digits)       )
        A__				=			10**number_of_frac_digits
        A__,	A__				=			denominator, numerator
        while True:
            A__				=			dividend % divisor
            if remainder == 0:
                break
            A__,	A__				=			divisor, remainder
        A__,	A__				=			numerator / divisor, denominator / divisor
        return int(_lowerCamelCase       ), int(_lowerCamelCase       )


if __name__ == "__main__":
  print(f"""{decimal_to_fraction(2) = }""")
  print(f"""{decimal_to_fraction(89.0) = }""")
  print(f"""{decimal_to_fraction("67") = }""")
  print(f"""{decimal_to_fraction("45.0") = }""")
  print(f"""{decimal_to_fraction(1.5) = }""")
  print(f"""{decimal_to_fraction("6.25") = }""")
  print(f"""{decimal_to_fraction("78td") = }""")



