






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							:   str							=  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 					_snake_case						(			lowercase__ ):






	def __init__(		self					,*UpperCamelCase					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,**UpperCamelCase       )						->    List[str]:
					super().__init__(*UpperCAmelCase__					,**UpperCAmelCase__       )
					snake_case__      :Dict         =   eval_examples
					snake_case__      :Union[str, Any]         =   post_process_function
					snake_case__      :Optional[Any]         =   quant_trainer_args
					snake_case__      :Tuple         =   128  # default number of calibration samples






	def    lowerCAmelCase_							(		self					,UpperCamelCase=None       )						->    List[str]:
					if calib_dataset is None and self.calib_dataset is None:
									raise ValueError("Trainer: calibration requires an calib_dataset."       )
					snake_case__      :Dict         =   calib_dataset if calib_dataset is not None else self.calib_dataset

					snake_case__      :str         =   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					,UpperCamelCase=None       )						->    Union[str, Any]:
					snake_case__      :int         =   self.train_dataset if calib_dataset is None else calib_dataset
					snake_case__      :Any         =   self.get_calib_dataloader(UpperCAmelCase__       )

					snake_case__      :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
									snake_case__      :Any         =   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       )
					snake_case__      :Union[str, Any]         =   model






	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase = "eval"       )						->    str:
					snake_case__      :str         =   self.eval_dataset if eval_dataset is None else eval_dataset
					snake_case__      :List[Any]         =   self.get_eval_dataloader(UpperCAmelCase__       )
					snake_case__      :Optional[int]         =   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__      :Optional[int]         =   self.compute_metrics
					snake_case__      :Dict         =   None
					snake_case__      :int         =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									snake_case__      :List[str]         =   eval_loop(
									    UpperCAmelCase__					,description="Evaluation"					,prediction_loss_only=True if compute_metrics is None else None					,ignore_keys=UpperCAmelCase__					,)
					finally:
									snake_case__      :Tuple         =   compute_metrics

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

									self.log(UpperCAmelCase__       )
					else:
									snake_case__      :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()       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None					,UpperCamelCase = "test"       )						->    Any:
					snake_case__      :Any         =   self.get_test_dataloader(UpperCAmelCase__       )

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

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

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

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase="./"       )						->    Optional[Any]:
					snake_case__      :str         =   self.eval_dataset
					snake_case__      :List[str]         =   self.get_eval_dataloader(UpperCAmelCase__       )

					snake_case__      :Union[str, Any]         =   next(iter(UpperCAmelCase__       )       )

					# saving device - to make it consistent
					snake_case__      :Optional[Any]         =   torch.device("cuda" if torch.cuda.is_available() else "cpu"       )

					# convert to tuple
					snake_case__      :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

					snake_case__      :str         =   True

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

					model.eval()
					model.float()

					snake_case__      :Optional[int]         =   model.module if hasattr(UpperCAmelCase__					,"module"       ) else model
					quant_trainer.configure_model(UpperCAmelCase__					,self.quant_trainer_args       )

					snake_case__      :Dict         =   os.path.join(UpperCAmelCase__					,"model.onnx"       )
					logger.info(f'exporting model to {output_model_file}'       )

					snake_case__      :Any         =   {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"       )







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


				if depth < 0:
								raise ValueError("Depth cannot be less than 0"      )

				if len(__snake_case      ) == 0:
								raise ValueError("Scores cannot be empty"      )

				if depth == height:
								return scores[node_index]

				if is_max:
								return max(
								    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

				return min(
				    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()