
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()


__UpperCAmelCase   						=     logging.getLogger(__name__)





@dataclass
class 				UpperCamelCase__		:






       """simple docstring"""


       UpperCAmelCase_				       =field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
       UpperCAmelCase_				       =field(
           default=__SCREAMING_SNAKE_CASE       ,    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=__SCREAMING_SNAKE_CASE       ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
       UpperCAmelCase_				       =field(default=__SCREAMING_SNAKE_CASE       ,    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=__SCREAMING_SNAKE_CASE       ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,    )







@dataclass
class 				UpperCamelCase__		:






       """simple docstring"""


       UpperCAmelCase_				       =field(
           metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}	)
       UpperCAmelCase_				       =field(
           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=__SCREAMING_SNAKE_CASE       ,    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.
   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(__lowerCamelCase,  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''',  __lowerCamelCase						)

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

   # 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=__lowerCamelCase,  idalabel=__lowerCamelCase,  labelaid={label: i for i, label in enumerate(__lowerCamelCase						)},  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=__lowerCamelCase,  cache_dir=model_args.cache_dir,  )

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

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

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

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

   # 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(__lowerCamelCase,  '''w'''						) as writer:
         logger.info('''***** Eval results *****'''						)

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

         results.update(__lowerCamelCase						)

    # Predict
   if training_args.do_predict:
      SCREAMING_SNAKE_CASE_	=      TFTokenClassificationDataset(
          token_classification_task=__lowerCamelCase,  data_dir=data_args.data_dir,  tokenizer=__lowerCamelCase,  labels=__lowerCamelCase,  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(__lowerCamelCase,  __lowerCamelCase						)
      SCREAMING_SNAKE_CASE_	=      classification_report(__lowerCamelCase,  __lowerCamelCase						)

      logger.info('''\n%s''',  __lowerCamelCase						)

      SCREAMING_SNAKE_CASE_	=      os.path.join(training_args.output_dir,  '''test_results.txt'''						)

      with open(__lowerCamelCase,  '''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(__lowerCamelCase,  '''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(__lowerCamelCase						)

                  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(__lowerCamelCase						)
               else:
                  logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''',  line.split()[0]						)

   return results


if __name__ == "__main__":
   main()




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__UpperCAmelCase   						=     datasets.utils.logging.get_logger(__name__)





class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilderConfig	):






       """simple docstring"""


       UpperCAmelCase_				       =None
       UpperCAmelCase_				       =None







class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilder	):






       """simple docstring"""


       UpperCAmelCase_				       =datasets.Image()
       UpperCAmelCase_				       ="image"
       UpperCAmelCase_				       =ImageFolderConfig
       UpperCAmelCase_				       =42  # definition at the bottom of the script
       UpperCAmelCase_				       =ImageClassification(image_column="image"       ,    label_column="label"	)







__UpperCAmelCase   						=     [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
__UpperCAmelCase   						=     IMAGE_EXTENSIONS


