



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


a__   :					Optional[Any] 						=      logging.getLogger(__name__)



class    UpperCAmelCase__(				A__						):

   '''simple docstring'''




   A     :	List[str]												=     "token-classification"


   def __init__(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple)						->					List[str]:




         """simple docstring"""

         if type(_lowerCamelCase) == dict:
               lowercase__  =	Namespace(**_lowerCamelCase)
         lowercase__  =	import_module('tasks')
         try:
               lowercase__  =	getattr(_lowerCamelCase      ,					hparams.task_type)
               lowercase__  =	token_classification_task_clazz()
         except AttributeError:
               raise ValueError(
                   f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                   f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''')
         lowercase__  =	self.token_classification_task.get_labels(hparams.labels)
         lowercase__  =	CrossEntropyLoss().ignore_index
         super().__init__(_lowerCamelCase      ,					len(self.labels)      ,					self.mode)


   def       UpperCAmelCase		(    self	:      int      ,					**lowerCAmelCase	:      List[str])						->					Dict:




         """simple docstring"""

         return self.model(**_lowerCamelCase)


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str])						->					str:




         """simple docstring"""

         lowercase__  =	{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
         if self.config.model_type != "distilbert":
               lowercase__  =	(
                   batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
               )  # XLM and RoBERTa don"t use token_type_ids

         lowercase__  =	self(**_lowerCamelCase)
         lowercase__  =	outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.hparams
         for mode in ["train", "dev", "test"]:
               lowercase__  =	self._feature_file(_lowerCamelCase)
               if os.path.exists(_lowerCamelCase) and not args.overwrite_cache:
                     logger.info('Loading features from cached file %s'      ,					_lowerCamelCase)
                     lowercase__  =	torch.load(_lowerCamelCase)
               else:
                     logger.info('Creating features from dataset file at %s'      ,					args.data_dir)
                     lowercase__  =	self.token_classification_task.read_examples_from_file(args.data_dir      ,					_lowerCamelCase)
                     lowercase__  =	self.token_classification_task.convert_examples_to_features(
                         _lowerCamelCase      ,					self.labels      ,					args.max_seq_length      ,					self.tokenizer      ,					cls_token_at_end=bool(self.config.model_type in ['xlnet'])      ,					cls_token=self.tokenizer.cls_token      ,					cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0      ,					sep_token=self.tokenizer.sep_token      ,					sep_token_extra=_lowerCamelCase      ,					pad_on_left=bool(self.config.model_type in ['xlnet'])      ,					pad_token=self.tokenizer.pad_token_id      ,					pad_token_segment_id=self.tokenizer.pad_token_type_id      ,					pad_token_label_id=self.pad_token_label_id      ,					)
                     logger.info('Saving features into cached file %s'      ,					_lowerCamelCase)
                     torch.save(_lowerCamelCase      ,					_lowerCamelCase)


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      bool = False)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	self._feature_file(_lowerCamelCase)
         logger.info('Loading features from cached file %s'      ,					_lowerCamelCase)
         lowercase__  =	torch.load(_lowerCamelCase)
         lowercase__  =	torch.tensor([f.input_ids for f in features]      ,					dtype=torch.long)
         lowercase__  =	torch.tensor([f.attention_mask for f in features]      ,					dtype=torch.long)
         if features[0].token_type_ids is not None:
               lowercase__  =	torch.tensor([f.token_type_ids for f in features]      ,					dtype=torch.long)
         else:
               lowercase__  =	torch.tensor([0 for f in features]      ,					dtype=torch.long)
               # HACK(we will not use this anymore soon)
         lowercase__  =	torch.tensor([f.label_ids for f in features]      ,					dtype=torch.long)
         return DataLoader(
             TensorDataset(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)      ,					batch_size=_lowerCamelCase)


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         """Compute validation""" ""
         lowercase__  =	{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
         if self.config.model_type != "distilbert":
               lowercase__  =	(
                   batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
               )  # XLM and RoBERTa don"t use token_type_ids
         lowercase__  =	self(**_lowerCamelCase)
         lowercase__  =	outputs[:2]
         lowercase__  =	logits.detach().cpu().numpy()
         lowercase__  =	inputs['labels'].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Union[str, Any])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	torch.stack([x['val_loss'] for x in outputs]).mean()
         lowercase__  =	np.concatenate([x['pred'] for x in outputs]      ,					axis=0)
         lowercase__  =	np.argmax(_lowerCamelCase      ,					axis=2)
         lowercase__  =	np.concatenate([x['target'] for x in outputs]      ,					axis=0)

         lowercase__  =	dict(enumerate(self.labels))
         lowercase__  =	[[] for _ in range(out_label_ids.shape[0])]
         lowercase__  =	[[] for _ in range(out_label_ids.shape[0])]

         for i in range(out_label_ids.shape[0]):
               for j in range(out_label_ids.shape[1]):
                     if out_label_ids[i, j] != self.pad_token_label_id:
                           out_label_list[i].append(label_map[out_label_ids[i][j]])
                           preds_list[i].append(label_map[preds[i][j]])

         lowercase__  =	{
             'val_loss': val_loss_mean,
             'accuracy_score': accuracy_score(_lowerCamelCase      ,					_lowerCamelCase),
             'precision': precision_score(_lowerCamelCase      ,					_lowerCamelCase),
             'recall': recall_score(_lowerCamelCase      ,					_lowerCamelCase),
             'f1': fa_score(_lowerCamelCase      ,					_lowerCamelCase),
         }

         lowercase__  =	dict(results.items())
         lowercase__  =	results
         return ret, preds_list, out_label_list


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      int)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	self._eval_end(_lowerCamelCase)
         lowercase__  =	ret['log']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      List[str])						->					int:




         """simple docstring"""

         lowercase__  =	self._eval_end(_lowerCamelCase)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowercase__  =	ret['log']
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



   @staticmethod
   def       UpperCAmelCase		(    lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Any)						->					Any:




         """simple docstring"""

         BaseTransformer.add_model_specific_args(_lowerCamelCase      ,					_lowerCamelCase)
         parser.add_argument(
             '--task_type'      ,					default='NER'      ,					type=_lowerCamelCase      ,					help='Task type to fine tune in training (e.g. NER, POS, etc)')
         parser.add_argument(
             '--max_seq_length'      ,					default=1_28      ,					type=_lowerCamelCase      ,					help=(
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )      ,					)

         parser.add_argument(
             '--labels'      ,					default=''      ,					type=_lowerCamelCase      ,					help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'      ,					)
         parser.add_argument(
             '--gpus'      ,					default=0      ,					type=_lowerCamelCase      ,					help='The number of GPUs allocated for this, it is by default 0 meaning none'      ,					)

         parser.add_argument(
             '--overwrite_cache'      ,					action='store_true'      ,					help='Overwrite the cached training and evaluation sets')

         return parser


if __name__ == "__main__":
       a__   :					List[str] 						=      argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       a__   :					Dict 						=      NERTransformer.add_model_specific_args(parser, os.getcwd())
       a__   :					Any 						=      parser.parse_args()
       a__   :					str 						=      NERTransformer(args)
       a__   :					Any 						=      generic_train(model, args)

       if args.do_predict:
              # See https://github.com/huggingface/transformers/issues/3159
              # pl use this default format to create a checkpoint:
              # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
              # /pytorch_lightning/callbacks/model_checkpoint.py#L322
              a__   :					int 						=      sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
              a__   :					Union[str, Any] 						=      model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE							=     {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     ['WhisperTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'WhisperForConditionalGeneration',
     'WhisperModel',
     'WhisperPreTrainedModel',
     'WhisperForAudioClassification',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFWhisperForConditionalGeneration',
     'TFWhisperModel',
     'TFWhisperPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'FlaxWhisperForConditionalGeneration',
     'FlaxWhisperModel',
     'FlaxWhisperPreTrainedModel',
     'FlaxWhisperForAudioClassification',
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
