


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 					lowerCAmelCase__	:




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







@dataclass
class 					lowerCAmelCase__	:




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




def 							_UpperCAmelCase    ():
       # 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.
       _A				:	Dict       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
       _A				:	str       =			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				:	Dict       =			import_module("tasks"						)

       try:
              _A				:	List[str]       =			getattr(UpperCamelCase__    ,							model_args.task_type						)
              _A				:	TokenClassificationTask       =			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"    ,							UpperCamelCase__						)

       # Prepare Token Classification task
       _A				:	Dict       =			token_classification_task.get_labels(data_args.labels						)
       _A				:	Dict[int, str]       =			dict(enumerate(UpperCamelCase__						)						)
       _A				:	str       =			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				:	List[Any]       =			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				:	Tuple       =			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():
              _A				:	Optional[Any]       =			TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path    ,							from_pt=bool(".bin" in model_args.model_name_or_path						)    ,							config=UpperCamelCase__    ,							cache_dir=model_args.cache_dir    ,							)

       # Get datasets
       _A				:	Optional[Any]       =			(
           TFTokenClassificationDataset(
               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				:	str       =			(
           TFTokenClassificationDataset(
               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				:	str       =			np.argmax(UpperCamelCase__    ,							axis=2						)
              _A				:	str       =			preds.shape
              _A				:	Tuple       =			[[] for _ in range(UpperCamelCase__						)]
              _A				:	List[str]       =			[[] for _ in range(UpperCamelCase__						)]

              for i in range(UpperCamelCase__						):
                     for j in range(UpperCamelCase__						):
                            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(UpperCamelCase__					:  EvalPrediction						) -> Dict:
              _A				:	List[str]       =			align_predictions(p.predictions    ,							p.label_ids						)

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

       # Initialize our Trainer
       _A				:	Union[str, Any]       =			TFTrainer(
           model=UpperCamelCase__    ,							args=UpperCamelCase__    ,							train_dataset=train_dataset.get_dataset() if train_dataset else None    ,							eval_dataset=eval_dataset.get_dataset() if eval_dataset else None    ,							compute_metrics=UpperCamelCase__    ,							)

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

       # Evaluation
       _A				:	Union[str, Any]       =			{}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"						)

              _A				:	Optional[Any]       =			trainer.evaluate()
              _A				:	Any       =			os.path.join(training_args.output_dir    ,							"eval_results.txt"						)

              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				:	Tuple       =			TFTokenClassificationDataset(
                  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				:	Optional[Any]       =			trainer.predict(test_dataset.get_dataset()						)
              _A				:	Union[str, Any]       =			align_predictions(UpperCamelCase__    ,							UpperCamelCase__						)
              _A				:	Optional[Any]       =			classification_report(UpperCamelCase__    ,							UpperCamelCase__						)

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

              _A				:	int       =			os.path.join(training_args.output_dir    ,							"test_results.txt"						)

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

              # Save predictions
              _A				:	Optional[int]       =			os.path.join(training_args.output_dir    ,							"test_predictions.txt"						)

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

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

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

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

       return results


if __name__ == "__main__":
    main()




import warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."					,   a					,   )
