


'''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				: 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       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			*__lowercase       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			**__lowercase		):
										super().__init__(*__lowercase       ,			**__lowercase		)
										__lowerCAmelCase							       =				eval_examples
										__lowerCAmelCase							       =				post_process_function
										__lowerCAmelCase							       =				quant_trainer_args
										__lowerCAmelCase							       =				1_28  # default number of calibration samples




				def 					_snake_case  (self       ,			__lowercase=None		):
										if calib_dataset is None and self.calib_dataset is None:
																raise ValueError('''Trainer: calibration requires an calib_dataset.'''		)
										__lowerCAmelCase							       =				calib_dataset if calib_dataset is not None else self.calib_dataset

										__lowerCAmelCase							       =				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 					_snake_case  (self       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.train_dataset if calib_dataset is None else calib_dataset
										__lowerCAmelCase							       =				self.get_calib_dataloader(__lowercase		)

										__lowerCAmelCase							       =				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
																__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				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		)
										__lowerCAmelCase							       =				model




				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase = "eval"		):
										__lowerCAmelCase							       =				self.eval_dataset if eval_dataset is None else eval_dataset
										__lowerCAmelCase							       =				self.get_eval_dataloader(__lowercase		)
										__lowerCAmelCase							       =				self.eval_examples if eval_examples is None else eval_examples

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

										if self.post_process_function is not None and self.compute_metrics is not None:
																__lowerCAmelCase							       =				self.post_process_function(__lowercase       ,			__lowercase       ,			output.predictions		)
																__lowerCAmelCase							       =				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}_"""		):
																												__lowerCAmelCase							       =				metrics.pop(__lowercase		)

																self.log(__lowercase		)
										else:
																__lowerCAmelCase							       =				{}

										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()		)

										__lowerCAmelCase							       =				self.callback_handler.on_evaluate(self.args       ,			self.state       ,			self.control       ,			__lowercase		)
										return metrics




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase = "test"		):
										__lowerCAmelCase							       =				self.get_test_dataloader(__lowercase		)

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

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

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

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




				def 					_snake_case  (self       ,			__lowercase="./"		):
										__lowerCAmelCase							       =				self.eval_dataset
										__lowerCAmelCase							       =				self.get_eval_dataloader(__lowercase		)

										__lowerCAmelCase							       =				next(iter(__lowercase		)		)

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

										# convert to tuple
										__lowerCAmelCase							       =				tuple(v.to(__lowercase		) for k, v in batch.items()		)

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

										__lowerCAmelCase							       =				True

										__lowerCAmelCase							       =				self.model.to(__lowercase		)

										model.eval()
										model.float()

										__lowerCAmelCase							       =				model.module if hasattr(__lowercase       ,			'''module'''		) else model
										quant_trainer.configure_model(__lowercase       ,			self.quant_trainer_args		)

										__lowerCAmelCase							       =				os.path.join(__lowercase       ,			'''model.onnx'''		)
										logger.info(F"""exporting model to {output_model_file}"""		)

										__lowerCAmelCase							       =				{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 string import ascii_uppercase

_UpperCAmelCase				: Optional[int]							    =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				: Optional[int]							    =				dict(enumerate(ascii_uppercase))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				len(lowerCamelCase)
						__lowerCAmelCase							       =				0
						while True:
												if x == i:
																		__lowerCAmelCase							       =				0
												if len(lowerCamelCase) == len(lowerCamelCase):
																		break
												key += key[i]
												i += 1
						return key

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''THE GERMAN ATTACK'''
						__lowerCAmelCase							       =				'''SECRET'''
						__lowerCAmelCase							       =				generate_key(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				cipher_text(lowerCamelCase,				lowerCamelCase)
						print(F"""Encrypted Text = {s}""")
						print(F"""Original Text = {original_text(lowerCamelCase,				lowerCamelCase)}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

