






'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       Tuple  ,		A  :       Union[str, Any]  ,		A  :       List[str]  ,		A  :       Any  )    -> Any:







   """simple docstring"""
   for attribute in key.split('.'  ):
      __snake_case				:      Optional[int]     							= getattr(A  ,		A  )

   if weight_type is not None:
      __snake_case				:      Any     							= getattr(A  ,		A  ).shape
   else:
      __snake_case				:      Any     							= hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      __snake_case				:      Optional[Any]     							= value
   elif weight_type == "weight_g":
      __snake_case				:      str     							= value
   elif weight_type == "weight_v":
      __snake_case				:      List[Any]     							= value
   elif weight_type == "bias":
      __snake_case				:      List[str]     							= value
   else:
      __snake_case				:      List[Any]     							= value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       List[str]  ,		A  :       Dict  )    -> int:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= []
   __snake_case				:      int     							= fairseq_model.state_dict()

   __snake_case				:      Optional[int]     							= hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case				:      str     							= False
      if "conv_layers" in name:
         load_conv_layer(
             A  ,		A  ,		A  ,		A  ,		hf_model.config.feat_extract_norm == 'group'  ,		)
         __snake_case				:      List[Any]     							= True
      else:
         for key, mapped_key in MAPPING.items():
            __snake_case				:      Tuple     							= 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

            if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
               __snake_case				:      List[str]     							= True
               if "*" in mapped_key:
                  __snake_case				:      int     							= name.split(A  )[0].split('.'  )[-2]
                  __snake_case				:      List[str]     							= mapped_key.replace('*'  ,		A  )
               if "weight_g" in name:
                  __snake_case				:      str     							= 'weight_g'
               elif "weight_v" in name:
                  __snake_case				:      Tuple     							= 'weight_v'
               elif "weight" in name:
                  __snake_case				:      Optional[int]     							= 'weight'
               elif "bias" in name:
                  __snake_case				:      str     							= 'bias'
               else:
                  __snake_case				:      List[str]     							= None
               set_recursively(A  ,		A  ,		A  ,		A  ,		A  )
            continue
      if not is_used:
         unused_weights.append(A  )

   logger.warning(F"""Unused weights: {unused_weights}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       Optional[int]  ,		A  :       List[str]  ,		A  :       int  ,		A  :       int  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Tuple     							= full_name.split('conv_layers.'  )[-1]
   __snake_case				:      Optional[int]     							= name.split('.'  )
   __snake_case				:      str     							= int(items[0]  )
   __snake_case				:      Tuple     							= int(items[1]  )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         __snake_case				:      Optional[int]     							= value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         __snake_case				:      Optional[Any]     							= value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         __snake_case				:      str     							= value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         __snake_case				:      Dict     							= value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
   else:
      unused_weights.append(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       int  )    -> Optional[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= SEWConfig()
   if is_finetuned:
      __snake_case				:      List[str]     							= model.wav_encoder.wav_model.cfg
   else:
      __snake_case				:      Dict     							= model.cfg

   __snake_case				:      Dict     							= fs_config.conv_bias
   __snake_case				:      Tuple     							= eval(fs_config.conv_feature_layers  )
   __snake_case				:      Optional[Any]     							= [x[0] for x in conv_layers]
   __snake_case				:      List[Any]     							= [x[1] for x in conv_layers]
   __snake_case				:      str     							= [x[2] for x in conv_layers]
   __snake_case				:      Tuple     							= 'gelu'
   __snake_case				:      Union[str, Any]     							= 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
   __snake_case				:      List[Any]     							= 0.0
   __snake_case				:      Tuple     							= fs_config.activation_fn.name
   __snake_case				:      List[Any]     							= fs_config.encoder_embed_dim
   __snake_case				:      Tuple     							= 0.02
   __snake_case				:      List[Any]     							= fs_config.encoder_ffn_embed_dim
   __snake_case				:      Optional[Any]     							= 1e-5
   __snake_case				:      Optional[Any]     							= fs_config.encoder_layerdrop
   __snake_case				:      Optional[Any]     							= fs_config.encoder_attention_heads
   __snake_case				:      Union[str, Any]     							= fs_config.conv_pos_groups
   __snake_case				:      Any     							= fs_config.conv_pos
   __snake_case				:      Any     							= len(A  )
   __snake_case				:      str     							= fs_config.encoder_layers
   __snake_case				:      Optional[int]     							= fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      __snake_case				:      int     							= model.cfg
      __snake_case				:      Dict     							= fs_config.final_dropout
      __snake_case				:      List[str]     							= fs_config.layerdrop
   __snake_case				:      Union[str, Any]     							= fs_config.activation_dropout
   __snake_case				:      int     							= fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   __snake_case				:      Tuple     							= fs_config.attention_dropout
   __snake_case				:      Tuple     							= fs_config.dropout_input
   __snake_case				:      Optional[Any]     							= fs_config.dropout
   __snake_case				:      int     							= fs_config.mask_channel_length
   __snake_case				:      Optional[Any]     							= fs_config.mask_channel_prob
   __snake_case				:      int     							= fs_config.mask_length
   __snake_case				:      Dict     							= fs_config.mask_prob

   __snake_case				:      Tuple     							= 'Wav2Vec2FeatureExtractor'
   __snake_case				:      List[str]     							= 'Wav2Vec2CTCTokenizer'

   return config




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       List[str]  ,		A  :       List[str]=None  ,		A  :       List[Any]=None  ,		A  :       Optional[int]=True  )    -> List[Any]:







   """simple docstring"""

   if is_finetuned:
      __snake_case							,__snake_case							,__snake_case				:      Optional[int]     							= fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]  ,		arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
   else:
      __snake_case							,__snake_case							,__snake_case				:      int     							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

   if config_path is not None:
      __snake_case				:      List[str]     							= SEWConfig.from_pretrained(A  )
   else:
      __snake_case				:      Optional[Any]     							= convert_config(model[0]  ,		A  )
   __snake_case				:      Optional[int]     							= model[0].eval()

   __snake_case				:      Tuple     							= True if config.feat_extract_norm == 'layer' else False
   __snake_case				:      str     							= WavaVecaFeatureExtractor(
       feature_size=1  ,		sampling_rate=1_60_00  ,		padding_value=0  ,		do_normalize=A  ,		return_attention_mask=A  ,		)

   if is_finetuned:
      if dict_path:
         __snake_case				:      Union[str, Any]     							= Dictionary.load(A  )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __snake_case				:      str     							= target_dict.pad_index
         __snake_case				:      Union[str, Any]     							= target_dict.bos_index
         __snake_case				:      List[str]     							= target_dict.pad_index
         __snake_case				:      Tuple     							= target_dict.bos_index
         __snake_case				:      List[str]     							= target_dict.eos_index
         __snake_case				:      Union[str, Any]     							= len(target_dict.symbols  )
         __snake_case				:      int     							= os.path.join(A  ,		'vocab.json'  )
         if not os.path.isdir(A  ):
            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A  )  )
            return
         os.makedirs(A  ,		exist_ok=A  )
         with open(A  ,		'w'  ,		encoding='utf-8'  ) as vocab_handle:
            json.dump(target_dict.indices  ,		A  )
         __snake_case				:      List[str]     							= WavaVecaCTCTokenizer(
             A  ,		unk_token=target_dict.unk_word  ,		pad_token=target_dict.pad_word  ,		bos_token=target_dict.bos_word  ,		eos_token=target_dict.eos_word  ,		word_delimiter_token='|'  ,		do_lower_case=A  ,		)
         __snake_case				:      Optional[int]     							= WavaVecaProcessor(feature_extractor=A  ,		tokenizer=A  )
         processor.save_pretrained(A  )

      __snake_case				:      Optional[int]     							= SEWForCTC(A  )
   else:
      __snake_case				:      Any     							= SEWModel(A  )
      feature_extractor.save_pretrained(A  )

   recursively_load_weights(A  ,		A  ,		A  )

   hf_model.save_pretrained(A  )


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
      parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
      parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      parser.add_argument(
          '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
      )
      __A    	=   parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )







'''simple docstring'''






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


__A    	=   logging.getLogger(__name__)




@dataclass
class a_    :
   _snake_case        =							field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
   _snake_case        =							field(
       default="""NER"""		,      metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}							)
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
   _snake_case        =							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.
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,      )




@dataclass
class a_    :
   _snake_case        =							field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}							)
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}		,      )
   _snake_case        =							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."""
           )
       }		,      )
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   # 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.

   __snake_case				:      List[Any]     							= 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.
      __snake_case							,__snake_case							,__snake_case				:      List[str]     							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      __snake_case							,__snake_case							,__snake_case				:      int     							= 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.'  )

   __snake_case				:      List[str]     							= import_module('tasks'  )
   try:
      __snake_case				:      Any     							= getattr(A  ,		model_args.task_type  )
      __snake_case				:      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
   __snake_case				:      Optional[Any]     							= token_classification_task.get_labels(data_args.labels  )
   __snake_case				:      Dict[int, str]     							= dict(enumerate(A  )  )
   __snake_case				:      Optional[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.

   __snake_case				:      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  ,		)
   __snake_case				:      List[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  ,		)
   __snake_case				:      Optional[int]     							= 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
   __snake_case				:      List[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.train  ,		)
       if training_args.do_train
       else None
   )
   __snake_case				:      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  :       np.ndarray  ,		A  :       np.ndarray  ) -> Tuple[List[int], List[int]]:
      __snake_case				:      str     							= np.argmax(A  ,		axis=2  )

      __snake_case							,__snake_case				:      int     							= preds.shape

      __snake_case				:      Dict     							= [[] for _ in range(A  )]
      __snake_case				:      Union[str, Any]     							= [[] 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  :       EvalPrediction  ) -> Dict:
      __snake_case							,__snake_case				:      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
   __snake_case				:      Optional[int]     							= DataCollatorWithPadding(A  ,		pad_to_multiple_of=8  ) if training_args.fpaa else None

   # Initialize our Trainer
   __snake_case				:      Optional[Any]     							= 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
   __snake_case				:      List[Any]     							= {}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'  )

      __snake_case				:      List[str]     							= trainer.evaluate()

      __snake_case				:      Tuple     							= 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:
      __snake_case				:      str     							= 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  ,		)

      __snake_case							,__snake_case							,__snake_case				:      str     							= trainer.predict(A  )
      __snake_case							,__snake_case				:      List[str]     							= align_predictions(A  ,		A  )

      __snake_case				:      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
      __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Any:







   """simple docstring"""
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
      main()