





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch





class A_      (    _lowerCamelCase     ):
   lowerCAmelCase__       =				["""image_processor""", """tokenizer"""]
   lowerCAmelCase__       =				"""OneFormerImageProcessor"""
   lowerCAmelCase__       =				("""CLIPTokenizer""", """CLIPTokenizerFast""")




   def __init__(self			:Union[str, Any]					,     _UpperCamelCase			:Dict=None					,     _UpperCamelCase			:Optional[int]=None					,     _UpperCamelCase			:int = 77					,     _UpperCamelCase			:int = 77					,     **_UpperCamelCase			:Optional[int]				)->      Union[str, Any]:
         if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''				)
         if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''				)

         __A      =       max_seq_length
         __A      =       task_seq_length

         super().__init__(_UpperCamelCase					,     _UpperCamelCase				)




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:str=None					,     _UpperCamelCase			:Tuple=77				)->      Tuple:
         if text_list is None:
               raise ValueError('''tokens cannot be None.'''				)

         __A      =       self.tokenizer(_UpperCamelCase					,     padding='''max_length'''					,     max_length=_UpperCamelCase					,     truncation=_UpperCamelCase				)

         __A     ,		__A      =       tokens['''attention_mask'''], tokens['''input_ids''']

         __A      =       []
         for attn_mask, input_id in zip(_UpperCamelCase					,     _UpperCamelCase				):
               __A      =       torch.tensor(_UpperCamelCase				) * torch.tensor(_UpperCamelCase				)
               token_inputs.append(token.unsqueeze(0				)				)

         __A      =       torch.cat(_UpperCamelCase					,     dim=0				)
         return token_inputs




   def __call__(self			:Any					,     _UpperCamelCase			:Optional[int]=None					,     _UpperCamelCase			:str=None					,     _UpperCamelCase			:Union[str, Any]=None					,     **_UpperCamelCase			:Tuple				)->      str:

         if task_inputs is None:
               raise ValueError('''You have to specify the task_input. Found None.'''				)
         elif images is None:
               raise ValueError('''You have to specify the image. Found None.'''				)

         if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs				):
               raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''				)

         __A      =       self.image_processor(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     **_UpperCamelCase				)

         if isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               __A      =       [task_inputs]

         if isinstance(_UpperCamelCase					,     _UpperCamelCase				) and all(isinstance(_UpperCamelCase					,     _UpperCamelCase				) for task_input in task_inputs				):
               __A      =       []
               for task in task_inputs:
                     __A      =       f"""the task is {task}"""
                     task_token_inputs.append(_UpperCamelCase				)
               __A      =       self._preprocess_text(_UpperCamelCase					,     max_length=self.task_seq_length				)
         else:
               raise TypeError('''Task Inputs should be a string or a list of strings.'''				)

         if hasattr(_UpperCamelCase					,     '''text_inputs'''				):
               __A      =       encoded_inputs.text_inputs

               __A      =       []
               for texts in texts_list:
                     __A      =       self._preprocess_text(_UpperCamelCase					,     max_length=self.max_seq_length				)
                     text_inputs.append(text_input_list.unsqueeze(0				)				)

               __A      =       torch.cat(_UpperCamelCase					,     dim=0				)

         return encoded_inputs




   def _lowerCAmelCase   (self			:Tuple					,     _UpperCamelCase			:Optional[int]=None					,     _UpperCamelCase			:Optional[int]=None					,     _UpperCamelCase			:Optional[Any]=None					,     **_UpperCamelCase			:Tuple				)->      Tuple:

         if task_inputs is None:
               raise ValueError('''You have to specify the task_input. Found None.'''				)
         elif images is None:
               raise ValueError('''You have to specify the image. Found None.'''				)

         if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs				):
               raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''				)

         __A      =       self.image_processor.encode_inputs(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     **_UpperCamelCase				)

         if isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               __A      =       [task_inputs]

         if isinstance(_UpperCamelCase					,     _UpperCamelCase				) and all(isinstance(_UpperCamelCase					,     _UpperCamelCase				) for task_input in task_inputs				):
               __A      =       []
               for task in task_inputs:
                     __A      =       f"""the task is {task}"""
                     task_token_inputs.append(_UpperCamelCase				)
               __A      =       self._preprocess_text(_UpperCamelCase					,     max_length=self.task_seq_length				)
         else:
               raise TypeError('''Task Inputs should be a string or a list of strings.'''				)

         if hasattr(_UpperCamelCase					,     '''text_inputs'''				):
               __A      =       encoded_inputs.text_inputs

               __A      =       []
               for texts in texts_list:
                     __A      =       self._preprocess_text(_UpperCamelCase					,     max_length=self.max_seq_length				)
                     text_inputs.append(text_input_list.unsqueeze(0				)				)

               __A      =       torch.cat(_UpperCamelCase					,     dim=0				)

         return encoded_inputs




   def _lowerCAmelCase   (self			:int					,     *_UpperCamelCase			:Union[str, Any]					,     **_UpperCamelCase			:Union[str, Any]				)->      Tuple:
         return self.image_processor.post_process_semantic_segmentation(*_UpperCamelCase					,     **_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Optional[Any]					,     *_UpperCamelCase			:int					,     **_UpperCamelCase			:Any				)->      str:
         return self.image_processor.post_process_instance_segmentation(*_UpperCamelCase					,     **_UpperCamelCase				)






   def _lowerCAmelCase   (self			:Tuple					,     *_UpperCamelCase			:List[Any]					,     **_UpperCamelCase			:List[Any]				)->      str:
         return self.image_processor.post_process_panoptic_segmentation(*_UpperCamelCase					,     **_UpperCamelCase				)












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()


snake_case__      :							str									=  logging.getLogger(__name__)





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





@dataclass
class A_      :
   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=_lowerCamelCase				,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )





def _a       (			)			->		int:







      '''simple docstring'''






      __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(lowerCamelCase      ,       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'''      ,       lowerCamelCase				)

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

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

      # Get datasets
      __A      =       (
          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
      )
      __A      =       (
          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:     np.ndarray      ,       lowerCamelCase:     np.ndarray				) -> Tuple[List[int], List[int]]:
            __A      =       np.argmax(lowerCamelCase      ,       axis=2				)
            __A     ,		__A      =       preds.shape
            __A      =       [[] for _ in range(lowerCamelCase				)]
            __A      =       [[] 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:     EvalPrediction				) -> Dict:
            __A     ,		__A      =       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
      __A      =       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
      __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(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:
            __A      =       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      ,       )

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

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

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

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

            # Save predictions
            __A      =       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:
                        __A      =       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]:
                                    __A      =       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()





