






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


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__						(					a_						):





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		*__A					:  Optional[int]							,		__A					:  Optional[int]=None							,		__A					:  List[Any]=None							,		**__A					:  Optional[Any]	):
						super().__init__(*lowercase_							,		**lowercase_	)
						snake_case__			:				Union[str, Any]       =  eval_examples
						snake_case__			:				Union[str, Any]       =  post_process_function



					def 	_lowercase					( self					:  Dict							,		__A					:  List[str] = None							,		__A					:  Union[str, Any]=None							,		__A					:  str = None							,		__A					:  Tuple = "eval"							,		**__A					:  Any							,		):
						snake_case__			:				Dict       =  gen_kwargs.copy()
						snake_case__			:				Tuple       =  (
						    gen_kwargs["max_length"] if gen_kwargs.get("max_length"	) is not None else self.args.generation_max_length
						)
						snake_case__			:				str       =  (
						    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"	) is not None else self.args.generation_num_beams
						)
						snake_case__			:				Optional[Any]       =  gen_kwargs

						snake_case__			:				Optional[int]       =  self.eval_dataset if eval_dataset is None else eval_dataset
						snake_case__			:				List[Any]       =  self.get_eval_dataloader(lowercase_	)
						snake_case__			:				Union[str, Any]       =  self.eval_examples if eval_examples is None else eval_examples

						# Temporarily disable metric computation, we will do it in the loop here.
						snake_case__			:				Any       =  self.compute_metrics
						snake_case__			:				Union[str, Any]       =  None
						snake_case__			:				Optional[int]       =  time.time()
						snake_case__			:				List[Any]       =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
							snake_case__			:				List[Any]       =  eval_loop(
							    lowercase_							,		description="Evaluation"							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowercase_							,		metric_key_prefix=lowercase_							,		)
						finally:
							snake_case__			:				int       =  compute_metrics
						snake_case__			:				Optional[Any]       =  self.args.eval_batch_size * self.args.world_size
						if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
							start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
						output.metrics.update(
						    speed_metrics(
						        lowercase_							,		lowercase_							,		num_samples=output.num_samples							,		num_steps=math.ceil(output.num_samples / total_batch_size	)							,		)	)

						if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
							# Only the main node write the results by default
							snake_case__			:				Dict       =  self.post_process_function(lowercase_							,		lowercase_							,		lowercase_	)
							snake_case__			:				Any       =  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}_'''	):
									snake_case__			:				str       =  metrics.pop(lowercase_	)

							metrics.update(output.metrics	)
						else:
							snake_case__			:				Optional[int]       =  output.metrics

						if self.args.should_log:
							# Only the main node log the results by default
							self.log(lowercase_	)

						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()	)

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






					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]							,		__A					:  Dict							,		__A					:  Optional[int]=None							,		__A					:  Union[str, Any] = "test"							,		**__A					:  Optional[Any]	):
						snake_case__			:				Any       =  gen_kwargs.copy()

						snake_case__			:				List[str]       =  self.get_test_dataloader(lowercase_	)

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

						snake_case__			:				int       =  self.args.eval_batch_size * self.args.world_size
						if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
							start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
						output.metrics.update(
						    speed_metrics(
						        lowercase_							,		lowercase_							,		num_samples=output.num_samples							,		num_steps=math.ceil(output.num_samples / total_batch_size	)							,		)	)
						if self.post_process_function is None or self.compute_metrics is None:
							return output

						snake_case__			:				int       =  self.post_process_function(lowercase_							,		lowercase_							,		lowercase_							,		"predict"	)
						snake_case__			:				Tuple       =  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}_'''	):
								snake_case__			:				str       =  metrics.pop(lowercase_	)
						metrics.update(output.metrics	)
						return PredictionOutput(predictions=predictions.predictions							,		label_ids=predictions.label_ids							,		metrics=lowercase_	)











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



