code: 
  
  
  """simple docstring"""
  
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from importlib import import_module
  from typing import Dict, List, Optional, Tuple
  
  import numpy as np
  from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
  from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask
  
  from transformers import (
      AutoConfig,
      AutoTokenizer,
      EvalPrediction,
      HfArgumentParser,
      TFAutoModelForTokenClassification,
      TFTrainer,
      TFTrainingArguments,
  )
  from transformers.utils import logging as hf_logging
  
  
  hf_logging.set_verbosity_info()
  hf_logging.enable_default_handler()
  hf_logging.enable_explicit_format()
  
  
  __magic_name__          =							logging.getLogger(__name__)
  
  
  
  
  @dataclass
  class  __snake_case      :
  							__a				   =					field(
  							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
  							__a				   =					field(
  							    default=lowerCamelCase ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
  							__a				   =					field(
  							    default='''NER''' ,		metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}     )
  							__a				   =					field(
  							    default=lowerCamelCase ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
  							__a				   =					field(default=lowerCamelCase ,		metadata={'''help''': '''Set this flag to use fast tokenization.'''}     )
  							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  							# or just modify its tokenizer_config.json.
  							__a				   =					field(
  							    default=lowerCamelCase ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,		)
  
  
  
  
  @dataclass
  class  __snake_case      :
  							__a				   =					field(
  							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}     )
  							__a				   =					field(
  							    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}     )
  							__a				   =					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				   =					field(
  							    default=lowerCamelCase ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )
  
  
  
  
  
  def 			a_   (  ):
  			# See all possible arguments in src/transformers/training_args.py
  			# or by passing the --help flag to this script.
  			# We now keep distinct sets of args, for a cleaner separation of concerns.
  			__lowerCamelCase        =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
  			__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			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."""    )
  
  			__lowerCamelCase        =			import_module("""tasks"""    )
  
  			try:
  						__lowerCamelCase        =			getattr(lowercase__, model_args.task_type    )
  						__lowerCamelCase        =			token_classification_task_clazz()
  			except AttributeError:
  						raise ValueError(
  						    f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
  						    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'    )
  
  			# Setup logging
  			logging.basicConfig(
  			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO, )
  			logger.info(
  			    """n_replicas: %s, distributed training: %s, 16-bits training: %s""", training_args.n_replicas, bool(training_args.n_replicas > 1    ), training_args.fpaa, )
  			logger.info("""Training/evaluation parameters %s""", lowercase__    )
  
  			# Prepare Token Classification task
  			__lowerCamelCase        =			token_classification_task.get_labels(data_args.labels    )
  			__lowerCamelCase        =			dict(enumerate(lowercase__    )    )
  			__lowerCamelCase        =			len(lowercase__    )
  
  			# Load pretrained model and tokenizer
  			#
  			# Distributed training:
  			# The .from_pretrained methods guarantee that only one local process can concurrently
  			# download model & vocab.
  
  			__lowerCamelCase        =			AutoConfig.from_pretrained(
  			    model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase__, idalabel=lowercase__, labelaid={label: i for i, label in enumerate(lowercase__    )}, cache_dir=model_args.cache_dir, )
  			__lowerCamelCase        =			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, use_fast=model_args.use_fast, )
  
  			with training_args.strategy.scope():
  						__lowerCamelCase        =			TFAutoModelForTokenClassification.from_pretrained(
  						    model_args.model_name_or_path, from_pt=bool(""".bin""" in model_args.model_name_or_path    ), config=lowercase__, cache_dir=model_args.cache_dir, )
  
  			# Get datasets
  			__lowerCamelCase        =			(
  			    TFTokenClassificationDataset(
  			        token_classification_task=lowercase__, data_dir=data_args.data_dir, tokenizer=lowercase__, labels=lowercase__, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
  			    if training_args.do_train
  			    else None
  			)
  			__lowerCamelCase        =			(
  			    TFTokenClassificationDataset(
  			        token_classification_task=lowercase__, data_dir=data_args.data_dir, tokenizer=lowercase__, labels=lowercase__, model_type=config.model_type, 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 align_predictions(lowercase__   :np.ndarray, lowercase__   :np.ndarray    ) -> Tuple[List[int], List[int]]:
  						__lowerCamelCase        =			np.argmax(lowercase__, axis=2    )
  						__lowerCamelCase    ,__lowerCamelCase        =			preds.shape
  						__lowerCamelCase        =			[[] for _ in range(lowercase__    )]
  						__lowerCamelCase        =			[[] for _ in range(lowercase__    )]
  
  						for i in range(lowercase__    ):
  									for j in range(lowercase__    ):
  												if label_ids[i, j] != -100:
  															out_label_list[i].append(label_map[label_ids[i][j]]    )
  															preds_list[i].append(label_map[preds[i][j]]    )
  
  						return preds_list, out_label_list
  
  			def compute_metrics(lowercase__   :EvalPrediction    ) -> Dict:
  						__lowerCamelCase    ,__lowerCamelCase        =			align_predictions(p.predictions, p.label_ids    )
  
  						return {
  						    "precision": precision_score(lowercase__, lowercase__    ),
  						    "recall": recall_score(lowercase__, lowercase__    ),
  						    "f1": fa_score(lowercase__, lowercase__    ),
  						}
  
  			# Initialize our Trainer
  			__lowerCamelCase        =			TFTrainer(
  			    model=lowercase__, args=lowercase__, train_dataset=train_dataset.get_dataset() if train_dataset else None, eval_dataset=eval_dataset.get_dataset() if eval_dataset else None, compute_metrics=lowercase__, )
  
  			# Training
  			if training_args.do_train:
  						trainer.train()
  						trainer.save_model()
  						tokenizer.save_pretrained(training_args.output_dir    )
  
  			# Evaluation
  			__lowerCamelCase        =			{}
  			if training_args.do_eval:
  						logger.info("""*** Evaluate ***"""    )
  
  						__lowerCamelCase        =			trainer.evaluate()
  						__lowerCamelCase        =			os.path.join(training_args.output_dir, """eval_results.txt"""    )
  
  						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__    )
  
      # Predict
  			if training_args.do_predict:
  						__lowerCamelCase        =			TFTokenClassificationDataset(
  						    token_classification_task=lowercase__, data_dir=data_args.data_dir, tokenizer=lowercase__, labels=lowercase__, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.test, )
  
  						__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			trainer.predict(test_dataset.get_dataset()    )
  						__lowerCamelCase    ,__lowerCamelCase        =			align_predictions(lowercase__, lowercase__    )
  						__lowerCamelCase        =			classification_report(lowercase__, lowercase__    )
  
  						logger.info("""\n%s""", lowercase__    )
  
  						__lowerCamelCase        =			os.path.join(training_args.output_dir, """test_results.txt"""    )
  
  						with open(lowercase__, """w"""    ) as writer:
  									writer.write("""%s\n""" % report    )
  
  						# Save predictions
  						__lowerCamelCase        =			os.path.join(training_args.output_dir, """test_predictions.txt"""    )
  
  						with open(lowercase__, """w"""    ) as writer:
  									with open(os.path.join(data_args.data_dir, """test.txt"""    ), """r"""    ) as f:
  												__lowerCamelCase        =			0
  
  												for line in f:
  															if line.startswith("""-DOCSTART-"""    ) or line == "" or line == "\n":
  																		writer.write(lowercase__    )
  
  																		if not preds_list[example_id]:
  																					example_id += 1
  															elif preds_list[example_id]:
  																		__lowerCamelCase        =			line.split()[0] + """ """ + preds_list[example_id].pop(0    ) + """\n"""
  
  																		writer.write(lowercase__    )
  															else:
  																		logger.warning("""Maximum sequence length exceeded: No prediction for '%s'.""", line.split()[0]    )
  
  			return results
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  def 			a_   (  lowercase__   :str, lowercase__   :int = 13    ):
  			__lowerCamelCase        =			""""""
  			for c in s:
  						if "A" <= c <= "Z":
  									out += chr(ord("""A"""    ) + (ord(lowercase__    ) - ord("""A"""    ) + n) % 26    )
  						elif "a" <= c <= "z":
  									out += chr(ord("""a"""    ) + (ord(lowercase__    ) - ord("""a"""    ) + n) % 26    )
  						else:
  									out += c
  			return out
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			input("""Enter message: """    )
  
  			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
  			print("""Encryption:""", lowercase__    )
  
  			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
  			print("""Decryption: """, lowercase__    )
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()
  					main()
  
  
  
  
  