



import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

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


A_		:     int        =     logging.getLogger(__name__)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			field(
       default='''NER''',   metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			field(default=UpperCAmelCase__,   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.
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},   )



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''},   )
   _UpperCAmelCase     	=			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.'''
           )
       },   )
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
 if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  a		,  a		,  a						=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  a		,  a		,  a						=		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."					)

 a						=		import_module("tasks"					)
 try:
  a						=		getattr(UpperCAmelCase__ ,					model_args.task_type					)
  a						=		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 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" ,					UpperCAmelCase__					)

 # Set seed
 set_seed(training_args.seed					)

 # Prepare CONLL-2003 task
 a						=		token_classification_task.get_labels(data_args.labels					)
 a						=		dict(enumerate(UpperCAmelCase__					)					)
 a						=		len(UpperCAmelCase__					)

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

 a						=		AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path ,					num_labels=UpperCAmelCase__ ,					idalabel=UpperCAmelCase__ ,					labelaid={label: i for i, label in enumerate(UpperCAmelCase__					)} ,					cache_dir=model_args.cache_dir ,					)
 a						=		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 ,					)
 a						=		AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path ,					from_tf=bool(".ckpt" in model_args.model_name_or_path					) ,					config=UpperCAmelCase__ ,					cache_dir=model_args.cache_dir ,					)

 # Get datasets
 a						=		(
     TokenClassificationDataset(
         token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					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
 )
 a						=		(
     TokenClassificationDataset(
         token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					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(UpperCAmelCase__ :np.ndarray ,					UpperCAmelCase__ :np.ndarray					) -> Tuple[List[int], List[int]]:
  a						=		np.argmax(UpperCAmelCase__ ,					axis=2					)

  a		,  a						=		preds.shape

  a						=		[[] for _ in range(UpperCAmelCase__					)]
  a						=		[[] for _ in range(UpperCAmelCase__					)]

  for i in range(UpperCAmelCase__					):
   for j in range(UpperCAmelCase__					):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     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(UpperCAmelCase__ :EvalPrediction					) -> Dict:
  a		,  a						=		align_predictions(p.predictions ,					p.label_ids					)
  return {
      "accuracy_score": accuracy_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "precision": precision_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "recall": recall_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "f1": fa_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
  }

 # Data collator
 a						=		DataCollatorWithPadding(UpperCAmelCase__ ,					pad_to_multiple_of=8					) if training_args.fpaa else None

 # Initialize our Trainer
 a						=		Trainer(
     model=UpperCAmelCase__ ,					args=UpperCAmelCase__ ,					train_dataset=UpperCAmelCase__ ,					eval_dataset=UpperCAmelCase__ ,					compute_metrics=UpperCAmelCase__ ,					data_collator=UpperCAmelCase__ ,					)

 # 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_process_zero():
   tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
 a						=		{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)

  a						=		trainer.evaluate()

  a						=		os.path.join(training_args.output_dir ,					"eval_results.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    logger.info("***** Eval results *****"					)
    for key, value in result.items():
     logger.info("  %s = %s" ,					UpperCAmelCase__ ,					UpperCAmelCase__					)
     writer.write("%s = %s\n" % (key, value)					)

   results.update(UpperCAmelCase__					)

    # Predict
 if training_args.do_predict:
  a						=		TokenClassificationDataset(
      token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.test ,					)

  a		,  a		,  a						=		trainer.predict(UpperCAmelCase__					)
  a		,  a						=		align_predictions(UpperCAmelCase__ ,					UpperCAmelCase__					)

  a						=		os.path.join(training_args.output_dir ,					"test_results.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    for key, value in metrics.items():
     logger.info("  %s = %s" ,					UpperCAmelCase__ ,					UpperCAmelCase__					)
     writer.write("%s = %s\n" % (key, value)					)

        # Save predictions
  a						=		os.path.join(training_args.output_dir ,					"test_predictions.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    with open(os.path.join(data_args.data_dir ,					"test.txt"					) ,					"r"					) as f:
     token_classification_task.write_predictions_to_file(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 return results


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 main()


if __name__ == "__main__":
     main()






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

