





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase_							=				logging.getLogger(__name__)





def  __magic_name__  (				lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							return (preds == labels).mean()
@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       str       =	field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				, )




@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       str       =	field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()				)}				)
		__a       :       str       =	field(metadata={"""help""": """Should contain the data files for the task."""}				)
		__a       :       int       =	field(
		    default=128				, metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }				, )
		__a       :       bool       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





def  __magic_name__  (				)				-> Optional[int]:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
							lowercase_				,		lowercase_				,		lowercase_	:   Optional[Any]  				=     parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir				)
							    and os.listdir(training_args.output_dir				)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    """ --overwrite_output_dir to overcome."""				)

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,       datefmt="""%m/%d/%Y %H:%M:%S"""				,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,       )
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				,       training_args.local_rank				,       training_args.device				,       training_args.n_gpu				,       bool(training_args.local_rank != -1				)				,       training_args.fpaa				,       )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank				):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""				,       lowercase				)

							# Set seed
							set_seed(training_args.seed				)

							try:
														lowercase_	:   List[str]  				=     processors[data_args.task_name]()
														lowercase_	:   Union[str, Any]  				=     processor.get_labels()
														lowercase_	:   Optional[Any]  				=     len(lowercase				)
							except KeyError:
														raise ValueError("""Task not found: %s""" % (data_args.task_name)				)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							lowercase_	:   List[str]  				=     AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       num_labels=lowercase				,       finetuning_task=data_args.task_name				,       cache_dir=model_args.cache_dir				,       )
							lowercase_	:   List[str]  				=     AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,       cache_dir=model_args.cache_dir				,       )
							lowercase_	:   Dict  				=     AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path				,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)				,       config=lowercase				,       cache_dir=model_args.cache_dir				,       )

							# Get datasets
							lowercase_	:   int  				=     (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir				,       tokenizer=lowercase				,       task=data_args.task_name				,       max_seq_length=data_args.max_seq_length				,       overwrite_cache=data_args.overwrite_cache				,       mode=Split.train				,       )
							    if training_args.do_train
							    else None
							)
							lowercase_	:   Tuple  				=     (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir				,       tokenizer=lowercase				,       task=data_args.task_name				,       max_seq_length=data_args.max_seq_length				,       overwrite_cache=data_args.overwrite_cache				,       mode=Split.dev				,       )
							    if training_args.do_eval
							    else None
							)

							def compute_metrics(lowercase				) -> Dict:
														lowercase_	:   Dict  				=     np.argmax(p.predictions				,       axis=1				)
														return {"acc": simple_accuracy(lowercase				,       p.label_ids				)}

							# Data collator
							lowercase_	:   Optional[Any]  				=     DataCollatorWithPadding(lowercase				,       pad_to_multiple_of=8				) if training_args.fpaa else None

							# Initialize our Trainer
							lowercase_	:   str  				=     Trainer(
							    model=lowercase				,       args=lowercase				,       train_dataset=lowercase				,       eval_dataset=lowercase				,       compute_metrics=lowercase				,       data_collator=lowercase				,       )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
							lowercase_	:   List[Any]  				=     {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""				)

														lowercase_	:   int  				=     trainer.evaluate()

														lowercase_	:   List[Any]  				=     os.path.join(training_args.output_dir				,       """eval_results.txt"""				)
														if trainer.is_world_master():
																					with open(lowercase				,       """w"""				) as writer:
																												logger.info("""***** Eval results *****"""				)
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""				,       lowercase				,       lowercase				)
																																			writer.write("""%s = %s\n""" % (key, value)				)

																												results.update(lowercase				)

							return results





def  __magic_name__  (				lowercase				)				-> int:

							"""simple docstring"""



							main()


if __name__ == "__main__":
							main()






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()