






'''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


lowerCamelCase_				   =   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   _UpperCAmelCase     (	SCREAMING_SNAKE_CASE__		):




   """simple docstring"""

   def __init__(						self					:      int , *__UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Union[str, Any]=None , __UpperCAmelCase					:      List[str]=None , __UpperCAmelCase					:      str=None , **__UpperCAmelCase					:      List[Any]						):





         '''simple docstring'''





         super().__init__(*_lowercase , **_lowercase						)
         _A     	=      eval_examples
         _A     	=      post_process_function
         _A     	=      quant_trainer_args
         _A     	=      128  # default number of calibration samples

   def 		lowerCAmelCase					(						self					:      Any , __UpperCAmelCase					:      Any=None						):





         '''simple docstring'''





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

         _A     	=      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 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      Optional[int]=None						):





         '''simple docstring'''





         _A     	=      self.train_dataset if calib_dataset is None else calib_dataset
         _A     	=      self.get_calib_dataloader(_lowercase						)

         _A     	=      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
               _A     	=      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						)
         _A     	=      model

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      List[Any]=None , __UpperCAmelCase					:      Tuple=None , __UpperCAmelCase					:      Optional[Any]=None , __UpperCAmelCase					:      str = "eval"						):





         '''simple docstring'''





         _A     	=      self.eval_dataset if eval_dataset is None else eval_dataset
         _A     	=      self.get_eval_dataloader(_lowercase						)
         _A     	=      self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         _A     	=      self.compute_metrics
         _A     	=      None
         _A     	=      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         try:
               _A     	=      eval_loop(
                   _lowercase , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowercase , )
         finally:
               _A     	=      compute_metrics

         if self.post_process_function is not None and self.compute_metrics is not None:
               _A     	=      self.post_process_function(_lowercase , _lowercase , output.predictions						)
               _A     	=      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}_'''						):
                           _A     	=      metrics.pop(_lowercase						)

               self.log(_lowercase						)
         else:
               _A     	=      {}

         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()						)

         _A     	=      self.callback_handler.on_evaluate(self.args , self.state , self.control , _lowercase						)
         return metrics

   def 		lowerCAmelCase					(						self					:      Union[str, Any] , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      List[str]=None , __UpperCAmelCase					:      str = "test"						):





         '''simple docstring'''





         _A     	=      self.get_test_dataloader(_lowercase						)

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

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

         _A     	=      self.post_process_function(_lowercase , _lowercase , output.predictions , "predict"						)
         _A     	=      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}_'''						):
                     _A     	=      metrics.pop(_lowercase						)

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

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      List[str]="./"						):





         '''simple docstring'''





         _A     	=      self.eval_dataset
         _A     	=      self.get_eval_dataloader(_lowercase						)

         _A     	=      next(iter(_lowercase						)						)

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

         # convert to tuple
         _A     	=      tuple(v.to(_lowercase						) for k, v in batch.items()						)

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

         _A     	=      True

         _A     	=      self.model.to(_lowercase						)

         model.eval()
         model.float()

         _A     	=      model.module if hasattr(_lowercase , "module"						) else model
         quant_trainer.configure_model(_lowercase , self.quant_trainer_args						)

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

         _A     	=      {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"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str		)							->					None:
									snake_case   :			Optional[int]						    = self._trie
									for char in text:
															if char not in trie:
																					snake_case   :			str						    = {}
															snake_case   :			Optional[Any]						    = trie[char]
									snake_case   :			str						    = True



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



