

'''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


UpperCamelCase_    						=       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 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







				def __init__(       self     :		Optional[int]		,					*UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		int=None		,					UpperCAmelCase__     :		Tuple=None		,					UpperCAmelCase__     :		Optional[int]=None		,					**UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										super().__init__(*UpperCAmelCase__		,					**UpperCAmelCase__   )
										lowercase :							Any    		=eval_examples
										lowercase :							Optional[int]    		=post_process_function
										lowercase :							List[Any]    		=quant_trainer_args
										lowercase :							Optional[int]    		=128  # default number of calibration samples







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Dict=None   ):
										'''simple docstring'''
										if calib_dataset is None and self.calib_dataset is None:
																raise ValueError('''Trainer: calibration requires an calib_dataset.'''   )
										lowercase :							Tuple    		=calib_dataset if calib_dataset is not None else self.calib_dataset

										lowercase :							Union[str, Any]    		=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     :		Optional[int]		,					UpperCAmelCase__     :		List[Any]=None   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.train_dataset if calib_dataset is None else calib_dataset
										lowercase :							List[Any]    		=self.get_calib_dataloader(UpperCAmelCase__   )

										lowercase :							Optional[int]    		=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
																lowercase		,	lowercase		,	lowercase :							Optional[int]    		=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   )
										lowercase :							int    		=model







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		str=None		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		Tuple=None		,					UpperCAmelCase__     :		str = "eval"   ):
										'''simple docstring'''
										lowercase :							Tuple    		=self.eval_dataset if eval_dataset is None else eval_dataset
										lowercase :							str    		=self.get_eval_dataloader(UpperCAmelCase__   )
										lowercase :							str    		=self.eval_examples if eval_examples is None else eval_examples

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

										if self.post_process_function is not None and self.compute_metrics is not None:
																lowercase :							int    		=self.post_process_function(UpperCAmelCase__		,					UpperCAmelCase__		,					output.predictions   )
																lowercase :							Union[str, Any]    		=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}_'''   ):
																												lowercase :							Tuple    		=metrics.pop(UpperCAmelCase__   )

																self.log(UpperCAmelCase__   )
										else:
																lowercase :							int    		={}

										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()   )

										lowercase :							List[Any]    		=self.callback_handler.on_evaluate(self.args		,					self.state		,					self.control		,					UpperCAmelCase__   )
										return metrics







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]=None		,					UpperCAmelCase__     :		str = "test"   ):
										'''simple docstring'''
										lowercase :							str    		=self.get_test_dataloader(UpperCAmelCase__   )

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

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

										lowercase :							str    		=self.post_process_function(UpperCAmelCase__		,					UpperCAmelCase__		,					output.predictions		,					'''predict'''   )
										lowercase :							Dict    		=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}_'''   ):
																						lowercase :							Any    		=metrics.pop(UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		int="./"   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.eval_dataset
										lowercase :							Any    		=self.get_eval_dataloader(UpperCAmelCase__   )

										lowercase :							Tuple    		=next(iter(UpperCAmelCase__   )   )

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

										# convert to tuple
										lowercase :							Optional[Any]    		=tuple(v.to(UpperCAmelCase__   ) for k, v in batch.items()   )

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

										lowercase :							List[Any]    		=True

										lowercase :							Optional[int]    		=self.model.to(UpperCAmelCase__   )

										model.eval()
										model.float()

										lowercase :							List[Any]    		=model.module if hasattr(UpperCAmelCase__		,					'''module'''   ) else model
										quant_trainer.configure_model(UpperCAmelCase__		,					self.quant_trainer_args   )

										lowercase :							List[str]    		=os.path.join(UpperCAmelCase__		,					'''model.onnx'''   )
										logger.info(F'''exporting model to {output_model_file}'''   )

										lowercase :							Dict    		={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'''   )









def    __lowerCamelCase   (      A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						)     ->    float:
   lowerCamelCase_						:   List[str]		= [redshift, radiation_density, matter_density, dark_energy]
   if any(p < 0 for p in parameters					):
      raise ValueError("""All input parameters must be positive"""					)

   if any(p > 1 for p in parameters[1:4]					):
      raise ValueError("""Relative densities cannot be greater than one"""					)
   else:
      lowerCamelCase_						:   Union[str, Any]		= 1 - (matter_density + radiation_density + dark_energy)

      lowerCamelCase_						:   int		= (
          radiation_density * (redshift + 1) ** 4
          + matter_density * (redshift + 1) ** 3
          + curvature * (redshift + 1) ** 2
          + dark_energy
      )

      lowerCamelCase_						:   Dict		= hubble_constant * e_a ** (1 / 2)
      return hubble


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo LCDM approximation
      snake_case__				:	str					       =					0.3

      print(
          hubble_parameter(
              hubble_constant=6_8.3,
              radiation_density=1e-4,
              matter_density=matter_density,
              dark_energy=1 - matter_density,
              redshift=0,
          )
      )





