
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 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
	a     :Optional[str]       	=      field(
	    default=__lowerCAmelCase					,		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=__lowerCAmelCase					,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
	a     :bool       	=      field(default=__lowerCAmelCase					,		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=__lowerCAmelCase					,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,		)

@dataclass
class 					lowercase__:



	"""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=__lowerCAmelCase					,		metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}					,		)
	a     :int       	=      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.'
	        )
	    }					,		)
	a     :bool       	=      field(
	    default=__lowerCAmelCase					,		metadata={'help': 'Overwrite the cached training and evaluation sets'}				)




def 							a		(				):


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

				lowercase_	    =   import_module('''tasks'''   )
				try:
								lowercase_	    =   getattr(_lowerCamelCase		,  model_args.task_type   )
								lowercase_	    =   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'''		,  _lowerCamelCase   )

				# Set seed
				set_seed(training_args.seed   )

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

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

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

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

								lowercase_    ,      lowercase_	    =   preds.shape

								lowercase_	    =   [[] for _ in range(_lowerCamelCase   )]
								lowercase_	    =   [[] for _ in range(_lowerCamelCase   )]

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

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

				# Initialize our Trainer
				lowercase_	    =   Trainer(
				    model=_lowerCamelCase		,  args=_lowerCamelCase		,  train_dataset=_lowerCamelCase		,  eval_dataset=_lowerCamelCase		,  compute_metrics=_lowerCamelCase		,  data_collator=_lowerCamelCase		,  )

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

								lowercase_	    =   trainer.evaluate()

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

												results.update(_lowerCamelCase   )

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

								lowercase_    ,      lowercase_    ,      lowercase_	    =   trainer.predict(_lowerCamelCase   )
								lowercase_    ,      lowercase_	    =   align_predictions(_lowerCamelCase		,  _lowerCamelCase   )

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

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

				return results




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				main()


if __name__ == "__main__":
						main()










'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def       _UpperCamelCase			(*_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Union[Dict, Any]] = None ,						_lowerCamelCase				:  List[Any]=True ,						_lowerCamelCase				:  str=2						)-> str:




     '''simple docstring'''





     from .. import __version__

     __snake_case            =	take_from
     __snake_case            =	()
     if not isinstance(args[0] ,						_lowerCamelCase						):
          __snake_case            =	(args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(_lowerCamelCase						).base_version						) >= version.parse(_lowerCamelCase						):
               raise ValueError(
                   f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
                   f''' version {__version__} is >= {version_name}'''						)

          __snake_case            =	None
          if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
          elif hasattr(_lowerCamelCase ,						_lowerCamelCase						):
               values += (getattr(_lowerCamelCase ,						_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
          elif deprecated_kwargs is None:
               __snake_case            =	f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

          if warning is not None:
               __snake_case            =	warning + ''' ''' if standard_warn else ''''''
               warnings.warn(warning + message ,						_lowerCamelCase ,						stacklevel=_lowerCamelCase						)

     if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and len(_lowerCamelCase						) > 0:
          __snake_case            =	inspect.getouterframes(inspect.currentframe()						)[1]
          __snake_case            =	call_frame.filename
          __snake_case            =	call_frame.lineno
          __snake_case            =	call_frame.function
          __snake_case       ,     __snake_case            =	next(iter(deprecated_kwargs.items()						)						)
          raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''						)

     if len(_lowerCamelCase						) == 0:
          return
     elif len(_lowerCamelCase						) == 1:
          return values[0]
     return values






