



'''simple docstring'''
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


__SCREAMING_SNAKE_CASE			:   Tuple							   =logging.getLogger(__name__)


class 	SCREAMING_SNAKE_CASE__					( SCREAMING_SNAKE_CASE_       ):
	"""simple docstring"""


	A__     :     Dict    =					"token-classification"


	def __init__(		self       , A      )			->				List[Any]:
				if type(lowercase_      ) == dict:
							A:   List[str]								= Namespace(**lowercase_      )
				A:   Optional[Any]								= import_module("""tasks"""      )
				try:
							A:   Union[str, Any]								= getattr(lowercase_       , hparams.task_type      )
							A:   List[str]								= 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__()}'      )
				A:   Optional[int]								= self.token_classification_task.get_labels(hparams.labels      )
				A:   Optional[int]								= CrossEntropyLoss().ignore_index
				super().__init__(lowercase_       , len(self.labels      )       , self.mode      )


	def   a__			(		self       , **A      )			->				Union[str, Any]:
				return self.model(**lowercase_      )


	def   a__			(		self       , A       , A      )			->				Union[str, Any]:
				A:   Dict								= {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
				if self.config.model_type != "distilbert":
							A:   str								= (
							    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
							)  # XLM and RoBERTa don"t use token_type_ids

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


	def   a__			(		self      )			->				Optional[Any]:
				A:   str								= self.hparams
				for mode in ["train", "dev", "test"]:
							A:   Optional[Any]								= self._feature_file(lowercase_      )
							if os.path.exists(lowercase_      ) and not args.overwrite_cache:
										logger.info("""Loading features from cached file %s"""       , lowercase_      )
										A:   Optional[Any]								= torch.load(lowercase_      )
							else:
										logger.info("""Creating features from dataset file at %s"""       , args.data_dir      )
										A:   Union[str, Any]								= self.token_classification_task.read_examples_from_file(args.data_dir       , lowercase_      )
										A:   Dict								= self.token_classification_task.convert_examples_to_features(
										    lowercase_       , 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=lowercase_       , 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"""       , lowercase_      )
										torch.save(lowercase_       , lowercase_      )


	def   a__			(		self       , A       , A       , A = False      )			->				DataLoader:
				A:   Union[str, Any]								= self._feature_file(lowercase_      )
				logger.info("""Loading features from cached file %s"""       , lowercase_      )
				A:   int								= torch.load(lowercase_      )
				A:   Optional[Any]								= torch.tensor([f.input_ids for f in features]       , dtype=torch.long      )
				A:   Optional[int]								= torch.tensor([f.attention_mask for f in features]       , dtype=torch.long      )
				if features[0].token_type_ids is not None:
							A:   List[Any]								= torch.tensor([f.token_type_ids for f in features]       , dtype=torch.long      )
				else:
							A:   List[Any]								= torch.tensor([0 for f in features]       , dtype=torch.long      )
							# HACK(we will not use this anymore soon)
				A:   Tuple								= torch.tensor([f.label_ids for f in features]       , dtype=torch.long      )
				return DataLoader(
				    TensorDataset(lowercase_       , lowercase_       , lowercase_       , lowercase_      )       , batch_size=lowercase_      )


	def   a__			(		self       , A       , A      )			->				int:
				"""Compute validation""" ""
				A:   int								= {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
				if self.config.model_type != "distilbert":
							A:   int								= (
							    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
							)  # XLM and RoBERTa don"t use token_type_ids
				A:   Tuple								= self(**lowercase_      )
				A				,		A:   Optional[int]								= outputs[:2]
				A:   Optional[int]								= logits.detach().cpu().numpy()
				A:   str								= inputs["""labels"""].detach().cpu().numpy()
				return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


	def   a__			(		self       , A      )			->				int:
				A:   List[Any]								= torch.stack([x["""val_loss"""] for x in outputs]      ).mean()
				A:   str								= np.concatenate([x["""pred"""] for x in outputs]       , axis=0      )
				A:   Optional[Any]								= np.argmax(lowercase_       , axis=2      )
				A:   Any								= np.concatenate([x["""target"""] for x in outputs]       , axis=0      )

				A:   Dict								= dict(enumerate(self.labels      )      )
				A:   Tuple								= [[] for _ in range(out_label_ids.shape[0]      )]
				A:   Union[str, Any]								= [[] 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]]      )

				A:   int								= {
				    """val_loss""": val_loss_mean,
				    """accuracy_score""": accuracy_score(lowercase_       , lowercase_      ),
				    """precision""": precision_score(lowercase_       , lowercase_      ),
				    """recall""": recall_score(lowercase_       , lowercase_      ),
				    """f1""": fa_score(lowercase_       , lowercase_      ),
				}

				A:   str								= dict(results.items()      )
				A:   Tuple								= results
				return ret, preds_list, out_label_list


	def   a__			(		self       , A      )			->				int:
				# when stable
				A				,		A				,		A:   Any								= self._eval_end(lowercase_      )
				A:   str								= ret["""log"""]
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


	def   a__			(		self       , A      )			->				int:
				# updating to test_epoch_end instead of deprecated test_end
				A				,		A				,		A:   Union[str, Any]								= self._eval_end(lowercase_      )

				# Converting to the dict required by pl
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
				# pytorch_lightning/trainer/logging.py#L139
				A:   Optional[Any]								= 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   a__			(		A       , A      )			->				Optional[int]:
				# Add NER specific options
				BaseTransformer.add_model_specific_args(lowercase_       , lowercase_      )
				parser.add_argument(
				    """--task_type"""       , default="""NER"""       , type=lowercase_       , help="""Task type to fine tune in training (e.g. NER, POS, etc)"""      )
				parser.add_argument(
				    """--max_seq_length"""       , default=1_28       , type=lowercase_       , 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=lowercase_       , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""       , )
				parser.add_argument(
				    """--gpus"""       , default=0       , type=lowercase_       , 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__":
					__SCREAMING_SNAKE_CASE			:   Any							   =argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					__SCREAMING_SNAKE_CASE			:   Optional[int]							   =NERTransformer.add_model_specific_args(parser, os.getcwd())
					__SCREAMING_SNAKE_CASE			:   Dict							   =parser.parse_args()
					__SCREAMING_SNAKE_CASE			:   List[str]							   =NERTransformer(args)
					__SCREAMING_SNAKE_CASE			:   List[str]							   =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
										__SCREAMING_SNAKE_CASE			:   Tuple							   =sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
										__SCREAMING_SNAKE_CASE			:   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_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

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



