



'''simple docstring'''

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE__     :						int	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

SCREAMING_SNAKE_CASE__     :						Any	=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 				a  (     UpperCamelCase_    :    List[str]			)       ->	str:


		for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
				if class_name in extractors:
						snake_case__	     =model_type_to_module_name(_lowercase			)

						snake_case__	     =importlib.import_module(f""".{module_name}"""      ,					'transformers.models'			)
						try:
								return getattr(_lowercase      ,					_lowercase			)
						except AttributeError:
								continue

		for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
				if getattr(_lowercase      ,					'__name__'      ,					_lowercase			) == class_name:
						return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
		snake_case__	     =importlib.import_module('transformers'			)
		if hasattr(_lowercase      ,					_lowercase			):
				return getattr(_lowercase      ,					_lowercase			)

		return None






def 				a  (     UpperCamelCase_    :    Optional[Any]      ,					UpperCamelCase_    :    Dict = None      ,					UpperCamelCase_    :    int = False      ,					UpperCamelCase_    :    List[str] = False      ,					UpperCamelCase_    :    str = None      ,					UpperCamelCase_    :    Optional[Any] = None      ,					UpperCamelCase_    :    List[str] = None      ,					UpperCamelCase_    :    Tuple = False      ,					**UpperCamelCase_    :    str      ,					)       ->	Dict:
		snake_case__	     =get_file_from_repo(
		    _lowercase      ,					_lowercase      ,					cache_dir=_lowercase      ,					force_download=_lowercase      ,					resume_download=_lowercase      ,					proxies=_lowercase      ,					use_auth_token=_lowercase      ,					revision=_lowercase      ,					local_files_only=_lowercase      ,					)
		if resolved_config_file is None:
				logger.info(
				    'Could not locate the feature extractor configuration file, will try to use the model config instead.'			)
				return {}

		with open(_lowercase      ,					encoding='utf-8'			) as reader:
				return json.load(_lowercase			)


class 							a__:






			def __init__(     self						)							->							Union[str, Any]:
					raise EnvironmentError(
					    'AutoFeatureExtractor is designed to be instantiated '
					    'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'						)






			@classmethod
			@replace_list_option_in_docstrings(A_						)
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Any:
					snake_case__	     =kwargs.pop('config'					,   A_						)
					snake_case__	     =kwargs.pop('trust_remote_code'					,   A_						)
					snake_case__	     =True

					snake_case__     ,	snake_case__	     =FeatureExtractionMixin.get_feature_extractor_dict(A_					,   **A_						)
					snake_case__	     =config_dict.get('feature_extractor_type'					,   A_						)
					snake_case__	     =None
					if "AutoFeatureExtractor" in config_dict.get('auto_map'					,   {}						):
							snake_case__	     =config_dict['auto_map']['AutoFeatureExtractor']

					# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
					if feature_extractor_class is None and feature_extractor_auto_map is None:
							if not isinstance(A_					,   A_						):
									snake_case__	     =AutoConfig.from_pretrained(A_					,   **A_						)
							# It could be in `config.feature_extractor_type``
							snake_case__	     =getattr(A_					,   'feature_extractor_type'					,   A_						)
							if hasattr(A_					,   'auto_map'						) and "AutoFeatureExtractor" in config.auto_map:
									snake_case__	     =config.auto_map['AutoFeatureExtractor']

					if feature_extractor_class is not None:
							snake_case__	     =feature_extractor_class_from_name(A_						)

					snake_case__	     =feature_extractor_auto_map is not None
					snake_case__	     =feature_extractor_class is not None or type(A_						) in FEATURE_EXTRACTOR_MAPPING
					snake_case__	     =resolve_trust_remote_code(
					    A_					,   A_					,   A_					,   A_						)

					if has_remote_code and trust_remote_code:
							snake_case__	     =get_class_from_dynamic_module(
							    A_					,   A_					,   **A_						)
							snake_case__	     =kwargs.pop('code_revision'					,   A_						)
							if os.path.isdir(A_						):
									feature_extractor_class.register_for_auto_class()
							return feature_extractor_class.from_dict(A_					,   **A_						)
					elif feature_extractor_class is not None:
							return feature_extractor_class.from_dict(A_					,   **A_						)
					# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
					elif type(A_						) in FEATURE_EXTRACTOR_MAPPING:
							snake_case__	     =FEATURE_EXTRACTOR_MAPPING[type(A_						)]
							return feature_extractor_class.from_dict(A_					,   **A_						)

					raise ValueError(
					    f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
					    f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
					    f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()						)}"""						)






			@staticmethod
			def 						_lowercase  (     _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					FEATURE_EXTRACTOR_MAPPING.register(A_					,   A_						)








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


__snake_case   							=						logging.getLogger(__name__)



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



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






def 		_A		(		)					-> str:


     """simple docstring"""


     __UpperCamelCase =			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.
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
     else:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			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.' )

     __UpperCamelCase =			import_module('tasks' )
     try:
          __UpperCamelCase =			getattr(_lowercase					,  model_args.task_type )
          __UpperCamelCase =			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'					,  _lowercase )

     # Set seed
     set_seed(training_args.seed )

     # Prepare CONLL-2003 task
     __UpperCamelCase =			token_classification_task.get_labels(data_args.labels )
     __UpperCamelCase =			dict(enumerate(_lowercase ) )
     __UpperCamelCase =			len(_lowercase )

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

     __UpperCamelCase =			AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path					,  num_labels=_lowercase					,  idalabel=_lowercase					,  labelaid={label: i for i, label in enumerate(_lowercase )}					,  cache_dir=model_args.cache_dir					,  )
     __UpperCamelCase =			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					,  )
     __UpperCamelCase =			AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path					,  from_tf=bool('.ckpt' in model_args.model_name_or_path )					,  config=_lowercase					,  cache_dir=model_args.cache_dir					,  )

     # Get datasets
     __UpperCamelCase =			(
         TokenClassificationDataset(
             token_classification_task=_lowercase					,  data_dir=data_args.data_dir					,  tokenizer=_lowercase					,  labels=_lowercase					,  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
     )
     __UpperCamelCase =			(
         TokenClassificationDataset(
             token_classification_task=_lowercase					,  data_dir=data_args.data_dir					,  tokenizer=_lowercase					,  labels=_lowercase					,  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(_lowercase					,  _lowercase ) -> Tuple[List[int], List[int]]:
          __UpperCamelCase =			np.argmax(_lowercase					,  axis=2 )

          __UpperCamelCase,    __UpperCamelCase =			preds.shape

          __UpperCamelCase =			[[] for _ in range(_lowercase )]
          __UpperCamelCase =			[[] for _ in range(_lowercase )]

          for i in range(_lowercase ):
               for j in range(_lowercase ):
                    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(_lowercase ) -> Dict:
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(p.predictions					,  p.label_ids )
          return {
              "accuracy_score": accuracy_score(_lowercase					,  _lowercase ),
              "precision": precision_score(_lowercase					,  _lowercase ),
              "recall": recall_score(_lowercase					,  _lowercase ),
              "f1": fa_score(_lowercase					,  _lowercase ),
          }

     # Data collator
     __UpperCamelCase =			DataCollatorWithPadding(_lowercase					,  pad_to_multiple_of=8 ) if training_args.fpaa else None

     # Initialize our Trainer
     __UpperCamelCase =			Trainer(
         model=_lowercase					,  args=_lowercase					,  train_dataset=_lowercase					,  eval_dataset=_lowercase					,  compute_metrics=_lowercase					,  data_collator=_lowercase					,  )

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

          __UpperCamelCase =			trainer.evaluate()

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

               results.update(_lowercase )

    # Predict
     if training_args.do_predict:
          __UpperCamelCase =			TokenClassificationDataset(
              token_classification_task=_lowercase					,  data_dir=data_args.data_dir					,  tokenizer=_lowercase					,  labels=_lowercase					,  model_type=config.model_type					,  max_seq_length=data_args.max_seq_length					,  overwrite_cache=data_args.overwrite_cache					,  mode=Split.test					,  )

          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			trainer.predict(_lowercase )
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(_lowercase					,  _lowercase )

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

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

     return results






def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


     main()


if __name__ == "__main__":
    main()


