





import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase				:	Union[str, Any]      						=  logging.get_logger(__name__)

lowercase				:	Optional[int]      						=  {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowercase				:	str      						=  {
    """b0""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_2_4,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_4_0,
        """dropout_rate""": 0.2,
        """dw_padding""": [1_6],
    },
    """b2""": {
        """hidden_dim""": 1_4_0_8,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_6_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 1_6],
    },
    """b3""": {
        """hidden_dim""": 1_5_3_6,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_0_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 1_8],
    },
    """b4""": {
        """hidden_dim""": 1_7_9_2,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_8_0,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_0_4_8,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_5_6,
        """dropout_rate""": 0.4,
        """dw_padding""": [1_3, 2_7],
    },
    """b6""": {
        """hidden_dim""": 2_3_0_4,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_2_8,
        """dropout_rate""": 0.5,
        """dw_padding""": [3_1],
    },
    """b7""": {
        """hidden_dim""": 2_5_6_0,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_0_0,
        """dropout_rate""": 0.5,
        """dw_padding""": [1_8],
    },
}



def A_     (		A__    )  ->		List[Any]:
       a__						:     Dict			=    EfficientNetConfig()
       a__						:     List[str]			=    CONFIG_MAP[model_name]['hidden_dim']
       a__						:     List[str]			=    CONFIG_MAP[model_name]['width_coef']
       a__						:     Any			=    CONFIG_MAP[model_name]['depth_coef']
       a__						:     Union[str, Any]			=    CONFIG_MAP[model_name]['image_size']
       a__						:     List[str]			=    CONFIG_MAP[model_name]['dropout_rate']
       a__						:     Dict			=    CONFIG_MAP[model_name]['dw_padding']

       a__						:     Optional[int]			=    'huggingface/label-files'
       a__						:     List[str]			=    'imagenet-1k-id2label.json'
       a__						:     Any			=    1000
       a__						:     Optional[int]			=    json.load(open(hf_hub_download(A__		,				A__		,				repo_type='dataset'    )		,				'r'    )    )
       a__						:     int			=    {int(A__    ): v for k, v in idalabel.items()}

       a__						:     Any			=    idalabel
       a__						:     List[Any]			=    {v: k for k, v in idalabel.items()}
       return config



def A_     (		)  ->		List[Any]:
       a__						:     Any			=    'http://images.cocodataset.org/val2017/000000039769.jpg'
       a__						:     Optional[int]			=    Image.open(requests.get(A__		,				stream=A__    ).raw    )
       return im



def A_     (		A__    )  ->		int:
       a__						:     List[Any]			=    CONFIG_MAP[model_name]['image_size']
       a__						:     Union[str, Any]			=    EfficientNetImageProcessor(
           size={'height': size, 'width': size}		,				image_mean=[0.4_85, 0.4_56, 0.4_06]		,				image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]		,				do_center_crop=A__		,				)
       return preprocessor



def A_     (		A__    )  ->		Union[str, Any]:
       a__						:     Optional[int]			=    [v.split('_'    )[0].split('block'    )[1] for v in original_param_names if v.startswith('block'    )]
       a__						:     List[str]			=    sorted(set(A__    )    )
       a__						:     Optional[Any]			=    len(A__    )
       a__						:     List[Any]			=    {b: str(A__    ) for b, i in zip(A__		,				range(A__    )    )}

       a__						:     str			=    []
       rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')    )
       rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')    )
       rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')    )
       rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')    )
       rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')    )

       for b in block_names:
              a__						:     Tuple			=    block_name_mapping[b]
              rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight')    )
              rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight')    )
              rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias')    )
              rename_keys.append(
                  (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')    )
              rename_keys.append(
                  (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')    )
              rename_keys.append(
                  (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')    )
              rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')    )
              rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')    )
              rename_keys.append(
                  (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')    )
              rename_keys.append(
                  (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')    )

              rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')    )
              rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')    )
              rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')    )
              rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')    )
              rename_keys.append(
                  (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight')    )
              rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight')    )
              rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias')    )
              rename_keys.append(
                  (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean')    )
              rename_keys.append(
                  (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var')    )

       rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')    )
       rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')    )
       rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')    )
       rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')    )
       rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')    )

       a__						:     Tuple			=    {}
       for item in rename_keys:
              if item[0] in original_param_names:
                     a__						:     Any			=    'efficientnet.' + item[1]

       a__						:     List[str]			=    'classifier.weight'
       a__						:     List[Any]			=    'classifier.bias'
       return key_mapping



def A_     (		A__		,				A__		,				A__    )  ->		List[Any]:
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              a__						:     Any			=    key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     a__						:     Optional[int]			=    torch.from_numpy(A__    ).permute(3		,				2		,				0		,				1    )
              elif "depthwise_kernel" in key:
                     a__						:     str			=    torch.from_numpy(A__    ).permute(2		,				3		,				0		,				1    )
              elif "kernel" in key:
                     a__						:     Optional[Any]			=    torch.from_numpy(np.transpose(A__    )    )
              else:
                     a__						:     str			=    torch.from_numpy(A__    )

              # Replace HF parameters with original TF model parameters
              assert hf_params[hf_key].shape == new_hf_value.shape
              hf_params[hf_key].copy_(A__    )



@torch.no_grad()
def A_     (		A__		,				A__		,				A__		,				A__    )  ->		Dict:
       a__						:     List[Any]			=    model_classes[model_name](
           include_top=A__		,				weights='imagenet'		,				input_tensor=A__		,				input_shape=A__		,				pooling=A__		,				classes=1000		,				classifier_activation='softmax'		,				)

       a__						:     Union[str, Any]			=    original_model.trainable_variables
       a__						:     Optional[int]			=    original_model.non_trainable_variables
       a__						:     Union[str, Any]			=    {param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              a__						:     Tuple			=    param.numpy()
       a__						:     str			=    list(tf_params.keys()    )

       # Load HuggingFace model
       a__						:     Any			=    get_efficientnet_config(A__    )
       a__						:     str			=    EfficientNetForImageClassification(A__    ).eval()
       a__						:     List[str]			=    hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print('Converting parameters...'    )
       a__						:     Optional[int]			=    rename_keys(A__    )
       replace_params(A__		,				A__		,				A__    )

       # Initialize preprocessor and preprocess input image
       a__						:     Optional[Any]			=    convert_image_processor(A__    )
       a__						:     List[Any]			=    preprocessor(images=prepare_img()		,				return_tensors='pt'    )

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              a__						:     Tuple			=    hf_model(**A__    )
       a__						:     Union[str, Any]			=    outputs.logits.detach().numpy()

       # Original model inference
       a__						:     Tuple			=    False
       a__						:     List[Any]			=    CONFIG_MAP[model_name]['image_size']
       a__						:     Union[str, Any]			=    prepare_img().resize((image_size, image_size)		,				resample=PIL.Image.NEAREST    )
       a__						:     Optional[Any]			=    image.img_to_array(A__    )
       a__						:     Tuple			=    np.expand_dims(A__		,				axis=0    )
       a__						:     Optional[int]			=    original_model.predict(A__    )

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(A__		,				A__		,				atol=1E-3    ), "The predicted logits are not the same."
       print('Model outputs match!'    )

       if save_model:
              # Create folder to save model
              if not os.path.isdir(A__    ):
                     os.mkdir(A__    )
              # Save converted model and image processor
              hf_model.save_pretrained(A__    )
              preprocessor.save_pretrained(A__    )

       if push_to_hub:
              # Push model and image processor to hub
              print(F'Pushing converted {model_name} to the hub...'    )
              a__						:     Any			=    F'efficientnet-{model_name}'
              preprocessor.push_to_hub(A__    )
              hf_model.push_to_hub(A__    )


if __name__ == "__main__":
    lowercase				:	List[str]      						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--model_name""",
        default="""b0""",
        type=str,
        help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""hf_model""",
        type=str,
        help="""Path to the output PyTorch model directory.""",
    )
    parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
    parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

    lowercase				:	Optional[int]      						=  parser.parse_args()
    convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase				:	Union[str, Any]      						=  logging.getLogger(__name__)





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    str	  =  field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
   __A       :    Optional[str]	  =  field(
       default='''NER'''			,  metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
   __A       :    bool	  =  field(default=__UpperCAmelCase			,  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.
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,  )





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    str	  =  field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}			,  )
   __A       :    int	  =  field(
       default=1_2_8			,  metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }			,  )
   __A       :    bool	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )



def A_     (		)  ->		Dict:
       # 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.

       a__						:     List[str]			=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
       if len(sys.argv    ) == 2 and sys.argv[1].endswith('.json'    ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              a__			,							a__			,							a__						:     int			=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a__			,							a__			,							a__						:     List[Any]			=    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__						:     Optional[Any]			=    import_module('tasks'    )
       try:
              a__						:     List[Any]			=    getattr(A__		,				model_args.task_type    )
              a__						:     TokenClassificationTask			=    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 if training_args.local_rank in [-1, 0] else logging.WARN		,				)
       logger.warning(
           'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'		,				training_args.local_rank		,				training_args.device		,				training_args.n_gpu		,				bool(training_args.local_rank != -1    )		,				training_args.fpaa		,				)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank    ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s'		,				A__    )

       # Set seed
       set_seed(training_args.seed    )

       # Prepare CONLL-2003 task
       a__						:     Tuple			=    token_classification_task.get_labels(data_args.labels    )
       a__						:     Dict[int, str]			=    dict(enumerate(A__    )    )
       a__						:     Union[str, Any]			=    len(A__    )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       a__						:     Optional[Any]			=    AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path		,				num_labels=A__		,				idalabel=A__		,				labelaid={label: i for i, label in enumerate(A__    )}		,				cache_dir=model_args.cache_dir		,				)
       a__						:     str			=    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		,				)
       a__						:     List[Any]			=    AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path		,				from_tf=bool('.ckpt' in model_args.model_name_or_path    )		,				config=A__		,				cache_dir=model_args.cache_dir		,				)

       # Get datasets
       a__						:     int			=    (
           TokenClassificationDataset(
               token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				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__						:     Optional[int]			=    (
           TokenClassificationDataset(
               token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				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(A__		,				A__    ) -> Tuple[List[int], List[int]]:
              a__						:     Union[str, Any]			=    np.argmax(A__		,				axis=2    )

              a__			,							a__						:     Dict			=    preds.shape

              a__						:     Union[str, Any]			=    [[] for _ in range(A__    )]
              a__						:     Optional[int]			=    [[] for _ in range(A__    )]

              for i in range(A__    ):
                     for j in range(A__    ):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   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(A__    ) -> Dict:
              a__			,							a__						:     Union[str, Any]			=    align_predictions(p.predictions		,				p.label_ids    )
              return {
                  "accuracy_score": accuracy_score(A__		,				A__    ),
                  "precision": precision_score(A__		,				A__    ),
                  "recall": recall_score(A__		,				A__    ),
                  "f1": fa_score(A__		,				A__    ),
              }

       # Data collator
       a__						:     Union[str, Any]			=    DataCollatorWithPadding(A__		,				pad_to_multiple_of=8    ) if training_args.fpaa else None

       # Initialize our Trainer
       a__						:     List[str]			=    Trainer(
           model=A__		,				args=A__		,				train_dataset=A__		,				eval_dataset=A__		,				compute_metrics=A__		,				data_collator=A__		,				)

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
       a__						:     Any			=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'    )

              a__						:     Optional[Any]			=    trainer.evaluate()

              a__						:     List[Any]			=    os.path.join(training_args.output_dir		,				'eval_results.txt'    )
              if trainer.is_world_process_zero():
                     with open(A__		,				'w'    ) as writer:
                            logger.info('***** Eval results *****'    )
                            for key, value in result.items():
                                   logger.info('  %s = %s'		,				A__		,				A__    )
                                   writer.write('%s = %s\n' % (key, value)    )

                     results.update(A__    )

    # Predict
       if training_args.do_predict:
              a__						:     Optional[Any]			=    TokenClassificationDataset(
                  token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				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__						:     Any			=    trainer.predict(A__    )
              a__			,							a__						:     Union[str, Any]			=    align_predictions(A__		,				A__    )

              a__						:     Optional[int]			=    os.path.join(training_args.output_dir		,				'test_results.txt'    )
              if trainer.is_world_process_zero():
                     with open(A__		,				'w'    ) as writer:
                            for key, value in metrics.items():
                                   logger.info('  %s = %s'		,				A__		,				A__    )
                                   writer.write('%s = %s\n' % (key, value)    )

        # Save predictions
              a__						:     Tuple			=    os.path.join(training_args.output_dir		,				'test_predictions.txt'    )
              if trainer.is_world_process_zero():
                     with open(A__		,				'w'    ) as writer:
                            with open(os.path.join(data_args.data_dir		,				'test.txt'    )		,				'r'    ) as f:
                                   token_classification_task.write_predictions_to_file(A__		,				A__		,				A__    )

       return results



def A_     (		A__    )  ->		Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()


