






'''simple docstring'''






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__A    	=   '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__A    	=   direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__A    	=   re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__A    	=   re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A    	=   re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A    	=   [
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> Optional[int]:







			"""simple docstring"""
			__snake_case				:      Dict     							= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'  ,		A  )
			return [m.group(0  ) for m in matches]




def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







			"""simple docstring"""
			__snake_case				:      List[str]     							= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__snake_case				:      Optional[Any]     							= {
			    config.replace('Config'  ,		''  ): model_type for model_type, config in config_maping_names.items()
			}

			# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
			__snake_case				:      Optional[Any]     							= collections.defaultdict(A  )
			__snake_case				:      List[str]     							= collections.defaultdict(A  )
			__snake_case				:      Optional[Any]     							= collections.defaultdict(A  )

			# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
			for attr_name in dir(A  ):
						__snake_case				:      Tuple     							= None
						if _re_tf_models.match(A  ) is not None:
									__snake_case				:      Tuple     							= tf_models
									__snake_case				:      str     							= _re_tf_models.match(A  ).groups()[0]
						elif _re_flax_models.match(A  ) is not None:
									__snake_case				:      int     							= flax_models
									__snake_case				:      Dict     							= _re_flax_models.match(A  ).groups()[0]
						elif _re_pt_models.match(A  ) is not None:
									__snake_case				:      int     							= pt_models
									__snake_case				:      int     							= _re_pt_models.match(A  ).groups()[0]

						if lookup_dict is not None:
									while len(A  ) > 0:
												if attr_name in model_prefix_to_model_type:
															__snake_case				:      Optional[int]     							= True
															break
												# Try again after removing the last word in the name
												__snake_case				:      Tuple     							= ''.join(camel_case_split(A  )[:-1]  )

			__snake_case				:      Optional[Any]     							= set(list(pt_models.keys()  ) + list(tf_models.keys()  ) + list(flax_models.keys()  )  )
			__snake_case				:      List[Any]     							= list(A  )
			all_models.sort()

			__snake_case				:      List[Any]     							= {'model_type': all_models}
			__snake_case				:      str     							= [pt_models[t] for t in all_models]
			__snake_case				:      List[str]     							= [tf_models[t] for t in all_models]
			__snake_case				:      Optional[int]     							= [flax_models[t] for t in all_models]

			# Now let's use the auto-mapping names to make sure
			__snake_case				:      Optional[Any]     							= {}
			for t in all_models:
						if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
									__snake_case				:      Any     							= 'AutoProcessor'
						elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
									__snake_case				:      Optional[Any]     							= 'AutoTokenizer'
						elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
									__snake_case				:      Optional[int]     							= 'AutoFeatureExtractor'
						else:
									# Default to AutoTokenizer if a model has nothing, for backward compatibility.
									__snake_case				:      Optional[Any]     							= 'AutoTokenizer'

			__snake_case				:      Optional[int]     							= [processors[t] for t in all_models]

			return pd.DataFrame(A  )




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







			"""simple docstring"""
			__snake_case				:      str     							= [
			    transformers_module.models.auto.modeling_auto,
			    transformers_module.models.auto.modeling_tf_auto,
			    transformers_module.models.auto.modeling_flax_auto,
			]
			for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
						__snake_case				:      Any     							= [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
						__snake_case				:      Optional[Any]     							= [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
						# Loop through all three frameworks
						for module, cls, mapping in zip(A  ,		A  ,		A  ):
									# The type of pipeline may not exist in this framework
									if not hasattr(A  ,		A  ):
												continue
									# First extract all model_names
									__snake_case				:      Any     							= []
									for name in getattr(A  ,		A  ).values():
												if isinstance(A  ,		A  ):
															model_names.append(A  )
												else:
															model_names.extend(list(A  )  )

												# Add pipeline tag and auto model class for those models
									table.update({model_name: (pipeline_tag, cls) for model_name in model_names}  )

			return table




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







			"""simple docstring"""
			__snake_case				:      Optional[Any]     							= get_frameworks_table()
			__snake_case				:      Optional[Any]     							= Dataset.from_pandas(A  )

			__snake_case				:      Optional[int]     							= hf_hub_download(
			    'huggingface/transformers-metadata'  ,		'pipeline_tags.json'  ,		repo_type='dataset'  ,		token=A  )
			__snake_case				:      Optional[int]     							= Dataset.from_json(A  )
			__snake_case				:      int     							= {
			    tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
			    for i in range(len(A  )  )
			}
			__snake_case				:      int     							= update_pipeline_and_auto_class_table(A  )

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			__snake_case				:      Tuple     							= sorted(table.keys()  )
			__snake_case				:      Tuple     							= pd.DataFrame(
			    {
			        'model_class': model_classes,
			        'pipeline_tag': [table[m][0] for m in model_classes],
			        'auto_class': [table[m][1] for m in model_classes],
			    }  )
			__snake_case				:      Any     							= Dataset.from_pandas(A  )

			with tempfile.TemporaryDirectory() as tmp_dir:
						frameworks_dataset.to_json(os.path.join(A  ,		'frameworks.json'  )  )
						tags_dataset.to_json(os.path.join(A  ,		'pipeline_tags.json'  )  )

						if commit_sha is not None:
									__snake_case				:      int     							= (
									    F"""Update with commit {commit_sha}\n\nSee: """
									    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
									)
						else:
									__snake_case				:      Tuple     							= 'Update'

						upload_folder(
						    repo_id='huggingface/transformers-metadata'  ,		folder_path=A  ,		repo_type='dataset'  ,		token=A  ,		commit_message=A  ,		)




def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[int]:







			"""simple docstring"""
			__snake_case				:      Optional[int]     							= {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
			__snake_case				:      Any     							= transformers_module.pipelines.SUPPORTED_TASKS
			__snake_case				:      List[Any]     							= []
			for key in pipeline_tasks:
						if key not in in_table:
									__snake_case				:      str     							= pipeline_tasks[key]['pt']
									if isinstance(A  ,		(list, tuple)  ):
												__snake_case				:      List[str]     							= model[0]
									__snake_case				:      Dict     							= model.__name__
									if model not in in_table.values():
												missing.append(A  )

			if len(A  ) > 0:
						__snake_case				:      Union[str, Any]     							= ', '.join(A  )
						raise ValueError(
						    'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
						    F"""`utils/update_metadata.py`: {msg}. Please add them!"""  )


if __name__ == "__main__":
						__A    	=   argparse.ArgumentParser()
						parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
						parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
						parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
						__A    	=   parser.parse_args()

						if args.check_only:
												check_pipeline_tags()
						else:
												update_metadata(args.token, args.commit_sha)







'''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()