


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   3_8_4
   if "tiny" in model_name:
      UpperCAmelCase_							:      Any						=   [3, 3, 9, 3]
      UpperCAmelCase_							:      Union[str, Any]						=   [9_6, 1_9_2, 3_8_4, 7_6_8]
   if "small" in model_name:
      UpperCAmelCase_							:      Optional[int]						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      Dict						=   [9_6, 1_9_2, 3_8_4, 7_6_8]
   if "base" in model_name:
      UpperCAmelCase_							:      List[str]						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      Optional[Any]						=   [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
      UpperCAmelCase_							:      Any						=   5_1_2
   if "large" in model_name:
      UpperCAmelCase_							:      Optional[int]						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      Dict						=   [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
      UpperCAmelCase_							:      int						=   7_6_8
   if "xlarge" in model_name:
      UpperCAmelCase_							:      Any						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      int						=   [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
      UpperCAmelCase_							:      Optional[int]						=   1_0_2_4

   # set label information
   UpperCAmelCase_							:      Dict						=   1_5_0
   UpperCAmelCase_							:      Any						=   '''huggingface/label-files'''
   UpperCAmelCase_							:      Tuple						=   '''ade20k-id2label.json'''
   UpperCAmelCase_							:      Optional[int]						=   json.load(open(hf_hub_download(__lowercase			,       __lowercase			,       repo_type='''dataset'''       )			,       '''r'''       )       )
   UpperCAmelCase_							:      Union[str, Any]						=   {int(__lowercase       ): v for k, v in idalabel.items()}
   UpperCAmelCase_							:      List[str]						=   {v: k for k, v in idalabel.items()}

   UpperCAmelCase_							:      int						=   ConvNextConfig(
       depths=__lowercase			,       hidden_sizes=__lowercase			,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
   UpperCAmelCase_							:      List[Any]						=   UperNetConfig(
       backbone_config=__lowercase			,       auxiliary_in_channels=__lowercase			,       num_labels=__lowercase			,       idalabel=__lowercase			,       labelaid=__lowercase			,       )

   return config
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   []

   # fmt: off
   # stem
   rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')       )
   rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')       )
   rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')       )
   rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''')       )
   # stages
   for i in range(len(config.backbone_config.depths       )       ):
      for j in range(config.backbone_config.depths[i]       ):
         rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')       )
         rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')       )
      if i > 0:
         rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')       )
         rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')       )
         rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')       )
         rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')       )

      rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')       )
      rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')       )

   # decode head
   rename_keys.extend(
       [
           ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
           ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
           ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
           ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
       ]       )
   # fmt: on

   return rename_keys
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   dct.pop(__lowercase       )
   UpperCAmelCase_							:      List[Any]						=   val
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      str						=   {
       '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
       '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
       '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
       '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
       '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
   }
   UpperCAmelCase_							:      List[str]						=   model_name_to_url[model_name]
   UpperCAmelCase_							:      List[Any]						=   torch.hub.load_state_dict_from_url(__lowercase			,       map_location='''cpu'''       )['''state_dict''']

   UpperCAmelCase_							:      Optional[Any]						=   get_upernet_config(__lowercase       )
   UpperCAmelCase_							:      List[Any]						=   UperNetForSemanticSegmentation(__lowercase       )
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      UpperCAmelCase_							:      str						=   state_dict.pop(__lowercase       )
      if "bn" in key:
         UpperCAmelCase_							:      Tuple						=   key.replace('''bn'''			,       '''batch_norm'''       )
      UpperCAmelCase_							:      Optional[int]						=   val

   # rename keys
   UpperCAmelCase_							:      List[Any]						=   create_rename_keys(__lowercase       )
   for src, dest in rename_keys:
      rename_key(__lowercase			,       __lowercase			,       __lowercase       )

   model.load_state_dict(__lowercase       )

   # verify on image
   UpperCAmelCase_							:      Optional[int]						=   '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
   UpperCAmelCase_							:      Union[str, Any]						=   Image.open(requests.get(__lowercase			,       stream=__lowercase       ).raw       ).convert('''RGB'''       )

   UpperCAmelCase_							:      List[Any]						=   SegformerImageProcessor()
   UpperCAmelCase_							:      int						=   processor(__lowercase			,       return_tensors='''pt'''       ).pixel_values

   with torch.no_grad():
      UpperCAmelCase_							:      Tuple						=   model(__lowercase       )

   if model_name == "upernet-convnext-tiny":
      UpperCAmelCase_							:      Optional[Any]						=   torch.tensor(
          [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]       )
   elif model_name == "upernet-convnext-small":
      UpperCAmelCase_							:      Union[str, Any]						=   torch.tensor(
          [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]       )
   elif model_name == "upernet-convnext-base":
      UpperCAmelCase_							:      List[Any]						=   torch.tensor(
          [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]       )
   elif model_name == "upernet-convnext-large":
      UpperCAmelCase_							:      Dict						=   torch.tensor(
          [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]       )
   elif model_name == "upernet-convnext-xlarge":
      UpperCAmelCase_							:      Any						=   torch.tensor(
          [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]       )
   print('''Logits:'''			,       outputs.logits[0, 0, :3, :3]       )
   assert torch.allclose(outputs.logits[0, 0, :3, :3]			,       __lowercase			,       atol=1e-4       )
   print('''Looks ok!'''       )

   if pytorch_dump_folder_path is not None:
      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
      model.save_pretrained(__lowercase       )
      print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
      processor.save_pretrained(__lowercase       )

   if push_to_hub:
      print(F'''Pushing model and processor for {model_name} to hub'''       )
      model.push_to_hub(F'''openmmlab/{model_name}'''       )
      processor.push_to_hub(F'''openmmlab/{model_name}'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='upernet-convnext-tiny',
           type=str,
           choices=[F'upernet-convnext-{size}' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
           help='Name of the ConvNext UperNet model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       __UpperCamelCase				:  Union[str, Any]      					=							parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



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


__UpperCamelCase				:  Dict      					=							logging.getLogger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Optional[int]			= 'token-classification'





    def __init__(	self				:   Any       ,   __snake_case				:   Optional[Any]	):





       '''simple docstring'''
       if type(__snake_case	) == dict:
          UpperCAmelCase_							:      Tuple						=   Namespace(**__snake_case	)
       UpperCAmelCase_							:      Dict						=   import_module('''tasks'''	)
       try:
          UpperCAmelCase_							:      int						=   getattr(__snake_case       ,   hparams.task_type	)
          UpperCAmelCase_							:      TokenClassificationTask						=   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__()}'''	)
       UpperCAmelCase_							:      int						=   self.token_classification_task.get_labels(hparams.labels	)
       UpperCAmelCase_							:      Dict						=   CrossEntropyLoss().ignore_index
       super().__init__(__snake_case       ,   len(self.labels	)       ,   self.mode	)





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   **__snake_case				:   Optional[Any]	):





       '''simple docstring'''
       return self.model(**__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Optional[Any]	):





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

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





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.hparams
       for mode in ["train", "dev", "test"]:
          UpperCAmelCase_							:      Optional[Any]						=   self._feature_file(__snake_case	)
          if os.path.exists(__snake_case	) and not args.overwrite_cache:
             logger.info('''Loading features from cached file %s'''       ,   __snake_case	)
             UpperCAmelCase_							:      Any						=   torch.load(__snake_case	)
          else:
             logger.info('''Creating features from dataset file at %s'''       ,   args.data_dir	)
             UpperCAmelCase_							:      Optional[Any]						=   self.token_classification_task.read_examples_from_file(args.data_dir       ,   __snake_case	)
             UpperCAmelCase_							:      List[str]						=   self.token_classification_task.convert_examples_to_features(
                 __snake_case       ,   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=__snake_case       ,   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'''       ,   __snake_case	)
             torch.save(__snake_case       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   int       ,   __snake_case				:   bool = False	):





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





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]	):





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





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   torch.stack([x['''val_loss'''] for x in outputs]	).mean()
       UpperCAmelCase_							:      Dict						=   np.concatenate([x['''pred'''] for x in outputs]       ,   axis=0	)
       UpperCAmelCase_							:      Any						=   np.argmax(__snake_case       ,   axis=2	)
       UpperCAmelCase_							:      int						=   np.concatenate([x['''target'''] for x in outputs]       ,   axis=0	)

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

       UpperCAmelCase_							:      Union[str, Any]						=   {
           '''val_loss''': val_loss_mean,
           '''accuracy_score''': accuracy_score(__snake_case       ,   __snake_case	),
           '''precision''': precision_score(__snake_case       ,   __snake_case	),
           '''recall''': recall_score(__snake_case       ,   __snake_case	),
           '''f1''': fa_score(__snake_case       ,   __snake_case	),
       }

       UpperCAmelCase_							:      str						=   dict(results.items()	)
       UpperCAmelCase_							:      List[Any]						=   results
       return ret, preds_list, out_label_list





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   int	):





       '''simple docstring'''
       # when stable
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      str						=   self._eval_end(__snake_case	)
       UpperCAmelCase_							:      int						=   ret['''log''']
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       # updating to test_epoch_end instead of deprecated test_end
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   self._eval_end(__snake_case	)

       # Converting to the dict required by pl
       # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
       # pytorch_lightning/trainer/logging.py#L139
       UpperCAmelCase_							:      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 					_lowerCamelCase (	__snake_case				:   str       ,   __snake_case				:   Optional[int]	):





       '''simple docstring'''
       # Add NER specific options
       BaseTransformer.add_model_specific_args(__snake_case       ,   __snake_case	)
       parser.add_argument(
           '''--task_type'''       ,   default='''NER'''       ,   type=__snake_case       ,   help='''Task type to fine tune in training (e.g. NER, POS, etc)'''	)
       parser.add_argument(
           '''--max_seq_length'''       ,   default=128       ,   type=__snake_case       ,   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=__snake_case       ,   help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''       ,   )
       parser.add_argument(
           '''--gpus'''       ,   default=0       ,   type=__snake_case       ,   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__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       __UpperCamelCase				:  Dict      					=							NERTransformer.add_model_specific_args(parser, os.getcwd())
       __UpperCamelCase				:  Tuple      					=							parser.parse_args()
       __UpperCamelCase				:  Optional[Any]      					=							NERTransformer(args)
       __UpperCamelCase				:  int      					=							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
              __UpperCamelCase				:  Optional[int]      					=							sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
              __UpperCamelCase				:  List[Any]      					=							model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)