






'''simple docstring'''







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def     _UpperCamelCase				(  snake_case__				,      **snake_case__				):
      return RunBeamCommand(
          args.dataset				,      args.name				,      args.cache_dir				,      args.beam_pipeline_options				,      args.data_dir				,      args.all_configs				,      args.save_info or args.save_infos				,      args.ignore_verifications				,      args.force_redownload				,      **snake_case__				,      )






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     @staticmethod
     def      _UpperCamelCase (lowercase__     :						ArgumentParser       )	->							List[Any]:
           __A		 =   parser.add_parser("run_beam"					,					help="Run a Beam dataset processing pipeline"       )
           run_beam_parser.add_argument("dataset"					,					type=lowercase__					,					help="Name of the dataset to download"       )
           run_beam_parser.add_argument("--name"					,					type=lowercase__					,					default=lowercase__					,					help="Dataset config name"       )
           run_beam_parser.add_argument(
               "--cache_dir"					,					type=lowercase__					,					default=lowercase__					,					help="Cache directory where the datasets are stored"					,					)
           run_beam_parser.add_argument(
               "--beam_pipeline_options"					,					type=lowercase__					,					default=""					,					help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"					,					)
           run_beam_parser.add_argument(
               "--data_dir"					,					type=lowercase__					,					default=lowercase__					,					help="Can be used to specify a manual directory to get the files from"					,					)
           run_beam_parser.add_argument("--all_configs"					,					action="store_true"					,					help="Test all dataset configurations"       )
           run_beam_parser.add_argument("--save_info"					,					action="store_true"					,					help="Save the dataset infos file"       )
           run_beam_parser.add_argument(
               "--ignore_verifications"					,					action="store_true"					,					help="Run the test without checksums and splits checks"       )
           run_beam_parser.add_argument("--force_redownload"					,					action="store_true"					,					help="Force dataset redownload"       )
           # aliases
           run_beam_parser.add_argument("--save_infos"					,					action="store_true"					,					help="alias for save_info"       )
           run_beam_parser.set_defaults(func=lowercase__       )






     def __init__(self     :						Any					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						bool					,					lowercase__     :						bool					,					lowercase__     :						bool					,					lowercase__     :						bool					,					**lowercase__     :						Tuple					,					)	->							List[Any]:
           __A		 =   dataset
           __A		 =   name
           __A		 =   cache_dir
           __A		 =   beam_pipeline_options
           __A		 =   data_dir
           __A		 =   all_configs
           __A		 =   save_infos
           __A		 =   ignore_verifications
           __A		 =   force_redownload
           __A		 =   config_kwargs






     def      _UpperCamelCase (self     :						int       )	->							Dict:
           import apache_beam as beam

           if self._name is not None and self._all_configs:
                 print("Both parameters `name` and `all_configs` can't be used at once."       )
                 exit(1       )
           __A ,					__A		 =   self._dataset, self._name
           __A		 =   dataset_module_factory(lowercase__       )
           __A		 =   import_main_class(dataset_module.module_path       )
           __A		 =   []
           if self._beam_pipeline_options:
                 __A		 =   beam.options.pipeline_options.PipelineOptions(
                     flags=[f"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","       ) if opt]       )
           else:
                 __A		 =   None
           if self._all_configs and len(builder_cls.BUILDER_CONFIGS       ) > 0:
                 for builder_config in builder_cls.BUILDER_CONFIGS:
                       builders.append(
                           builder_cls(
                               config_name=builder_config.name					,					data_dir=self._data_dir					,					hash=dataset_module.hash					,					beam_options=lowercase__					,					cache_dir=self._cache_dir					,					base_path=dataset_module.builder_kwargs.get("base_path"       )					,					)       )
           else:
                 builders.append(
                     builder_cls(
                         config_name=lowercase__					,					data_dir=self._data_dir					,					beam_options=lowercase__					,					cache_dir=self._cache_dir					,					base_path=dataset_module.builder_kwargs.get("base_path"       )					,					**self._config_kwargs					,					)       )

           for builder in builders:
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD					,					download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH       )					,					verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS					,					try_from_hf_gcs=lowercase__					,					)
                 if self._save_infos:
                       builder._save_infos()

           print("Apache beam run successful."       )

           # If save_infos=True, the dataset infos file is created next to the loaded module file.
           # Let's move it to the original directory of the dataset script, to allow the user to
           # upload them on S3 at the same time afterwards.
           if self._save_infos:
                 __A		 =   os.path.join(builder_cls.get_imported_module_dir()					,					config.DATASETDICT_INFOS_FILENAME       )

                 __A		 =   Path(lowercase__       ).name + ".py"

                 __A		 =   os.path.join(lowercase__					,					lowercase__       )
                 if os.path.isfile(lowercase__       ):
                       __A		 =   os.path.dirname(lowercase__       )
                 elif os.path.isfile(lowercase__       ):
                       __A		 =   path
                 else:  # in case of a remote dataset
                       print(f"Dataset Infos file saved at {dataset_infos_path}"       )
                       exit(1       )

                 # Move datasetinfo back to the user
                 __A		 =   os.path.join(lowercase__					,					config.DATASETDICT_INFOS_FILENAME       )
                 copyfile(lowercase__					,					lowercase__       )
                 print(f"Dataset Infos file saved at {user_dataset_infos_path}"       )










'''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()


__A   :							List[str]     	=						logging.getLogger(__name__)






@dataclass
class        a__     :
     '''simple docstring'''







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







@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}    )
     _snake_case							   =	field(
         metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    )
     _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.'
             )
         }			,    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    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		 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __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(snake_case__				,      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				,      )
      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"				,      snake_case__				)

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

      # 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=snake_case__				,      idalabel=snake_case__				,      labelaid={label: i for i, label in enumerate(snake_case__				)}				,      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				,      )

      with training_args.strategy.scope():
            __A		 =   TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path				,      from_pt=bool(".bin" in model_args.model_name_or_path				)				,      config=snake_case__				,      cache_dir=model_args.cache_dir				,      )

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

            for i in range(snake_case__				):
                  for j in range(snake_case__				):
                        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(snake_case__				) -> Dict:
            __A ,					__A		 =   align_predictions(p.predictions				,      p.label_ids				)

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

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

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            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"				)

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

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

                  results.update(snake_case__				)

    # Predict
      if training_args.do_predict:
            __A		 =   TFTokenClassificationDataset(
                token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      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(test_dataset.get_dataset()				)
            __A ,					__A		 =   align_predictions(snake_case__				,      snake_case__				)
            __A		 =   classification_report(snake_case__				,      snake_case__				)

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

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

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

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

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

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

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

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

      return results


if __name__ == "__main__":
       main()


