




'''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()


lowerCamelCase_	 =		logging.getLogger(__name__)







@dataclass
class 	lowercase_					:


   """simple docstring"""

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







@dataclass
class 	lowercase_					:


   """simple docstring"""

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


def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   # 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.
   _SCREAMING_SNAKE_CASE =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
   _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   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."						)

   _SCREAMING_SNAKE_CASE =   import_module("tasks"						)

   try:
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  model_args.task_type						)
      _SCREAMING_SNAKE_CASE =   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" ,  __A						)

   # Prepare Token Classification task
   _SCREAMING_SNAKE_CASE =   token_classification_task.get_labels(data_args.labels						)
   _SCREAMING_SNAKE_CASE =   dict(enumerate(__A						)						)
   _SCREAMING_SNAKE_CASE =   len(__A						)

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

   _SCREAMING_SNAKE_CASE =   AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path ,  num_labels=__A ,  idalabel=__A ,  labelaid={label: i for i, label in enumerate(__A						)} ,  cache_dir=model_args.cache_dir ,  )
   _SCREAMING_SNAKE_CASE =   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():
      _SCREAMING_SNAKE_CASE =   TFAutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path ,  from_pt=bool(".bin" in model_args.model_name_or_path						) ,  config=__A ,  cache_dir=model_args.cache_dir ,  )

   # Get datasets
   _SCREAMING_SNAKE_CASE =   (
       TFTokenClassificationDataset(
           token_classification_task=__A ,  data_dir=data_args.data_dir ,  tokenizer=__A ,  labels=__A ,  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
   )
   _SCREAMING_SNAKE_CASE =   (
       TFTokenClassificationDataset(
           token_classification_task=__A ,  data_dir=data_args.data_dir ,  tokenizer=__A ,  labels=__A ,  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(__A       :				np.ndarray ,  __A       :				np.ndarray						) -> Tuple[List[int], List[int]]:
      _SCREAMING_SNAKE_CASE =   np.argmax(__A ,  axis=2						)
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   preds.shape
      _SCREAMING_SNAKE_CASE =   [[] for _ in range(__A						)]
      _SCREAMING_SNAKE_CASE =   [[] for _ in range(__A						)]

      for i in range(__A						):
         for j in range(__A						):
            if label_ids[i, j] != -1_00:
               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(__A       :				EvalPrediction						) -> Dict:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   align_predictions(p.predictions ,  p.label_ids						)

      return {
          "precision": precision_score(__A ,  __A						),
          "recall": recall_score(__A ,  __A						),
          "f1": fa_score(__A ,  __A						),
      }

   # Initialize our Trainer
   _SCREAMING_SNAKE_CASE =   TFTrainer(
       model=__A ,  args=__A ,  train_dataset=train_dataset.get_dataset() if train_dataset else None ,  eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,  compute_metrics=__A ,  )

   # Training
   if training_args.do_train:
      trainer.train()
      trainer.save_model()
      tokenizer.save_pretrained(training_args.output_dir						)

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

      _SCREAMING_SNAKE_CASE =   trainer.evaluate()
      _SCREAMING_SNAKE_CASE =   os.path.join(training_args.output_dir ,  "eval_results.txt"						)

      with open(__A ,  "w"						) as writer:
         logger.info("***** Eval results *****"						)

         for key, value in result.items():
            logger.info("  %s = %s" ,  __A ,  __A						)
            writer.write("%s = %s\n" % (key, value)						)

         results.update(__A						)

    # Predict
   if training_args.do_predict:
      _SCREAMING_SNAKE_CASE =   TFTokenClassificationDataset(
          token_classification_task=__A ,  data_dir=data_args.data_dir ,  tokenizer=__A ,  labels=__A ,  model_type=config.model_type ,  max_seq_length=data_args.max_seq_length ,  overwrite_cache=data_args.overwrite_cache ,  mode=Split.test ,  )

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   trainer.predict(test_dataset.get_dataset()						)
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   align_predictions(__A ,  __A						)
      _SCREAMING_SNAKE_CASE =   classification_report(__A ,  __A						)

      logger.info("\n%s" ,  __A						)

      _SCREAMING_SNAKE_CASE =   os.path.join(training_args.output_dir ,  "test_results.txt"						)

      with open(__A ,  "w"						) as writer:
         writer.write("%s\n" % report						)

      # Save predictions
      _SCREAMING_SNAKE_CASE =   os.path.join(training_args.output_dir ,  "test_predictions.txt"						)

      with open(__A ,  "w"						) as writer:
         with open(os.path.join(data_args.data_dir ,  "test.txt"						) ,  "r"						) as f:
            _SCREAMING_SNAKE_CASE =   0

            for line in f:
               if line.startswith("-DOCSTART-"						) or line == "" or line == "\n":
                  writer.write(__A						)

                  if not preds_list[example_id]:
                     example_id += 1
               elif preds_list[example_id]:
                  _SCREAMING_SNAKE_CASE =   line.split()[0] + " " + preds_list[example_id].pop(0						) + "\n"

                  writer.write(__A						)
               else:
                  logger.warning("Maximum sequence length exceeded: No prediction for '%s'." ,  line.split()[0]						)

   return results


if __name__ == "__main__":
 main()







'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @patch("diffusers.utils.hub_utils.get_full_repo_name"					)
   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Mock					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "full_repo_name"
      with TemporaryDirectory() as tmpdir:
         # Dummy args values
         _SCREAMING_SNAKE_CASE =   Mock()
         _SCREAMING_SNAKE_CASE =   tmpdir
         _SCREAMING_SNAKE_CASE =   0
         _SCREAMING_SNAKE_CASE =   "hub_token"
         _SCREAMING_SNAKE_CASE =   "dataset_name"
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0_0
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   0.0_2
         _SCREAMING_SNAKE_CASE =   0.0_3
         _SCREAMING_SNAKE_CASE =   0.0_0_0_5
         _SCREAMING_SNAKE_CASE =   0.0_0_0_0_0_1
         _SCREAMING_SNAKE_CASE =   1
         _SCREAMING_SNAKE_CASE =   1_0
         _SCREAMING_SNAKE_CASE =   0.0_0_1
         _SCREAMING_SNAKE_CASE =   0.1
         _SCREAMING_SNAKE_CASE =   0.2
         _SCREAMING_SNAKE_CASE =   True

         # Model card mush be rendered and saved
         diffusers.utils.hub_utils.create_model_card(__lowerCamelCase						,   model_name="model_name"					)
         self.assertTrue((Path(__lowerCamelCase					) / "README.md").is_file()					)

