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


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





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





def        lowercase(     )						->     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.

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

 # Set seed
 set_seed(training_args.seed    )

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

 # 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=UpperCamelCase_				, idalabel=UpperCamelCase_				, labelaid={label: i for i, label in enumerate(UpperCamelCase_    )}				, 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=UpperCamelCase_				, cache_dir=model_args.cache_dir				, )

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

  UpperCamelCase          =	preds.shape

  UpperCamelCase          =	[[] for _ in range(UpperCamelCase_    )]
  UpperCamelCase          =	[[] for _ in range(UpperCamelCase_    )]

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

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

 # Initialize our Trainer
 UpperCamelCase          =	Trainer(
     model=UpperCamelCase_				, args=UpperCamelCase_				, train_dataset=UpperCamelCase_				, eval_dataset=UpperCamelCase_				, compute_metrics=UpperCamelCase_				, data_collator=UpperCamelCase_				, )

 # 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(UpperCamelCase_				, """w"""    ) as writer:
    logger.info("""***** Eval results *****"""    )
    for key, value in result.items():
     logger.info("""  %s = %s"""				, UpperCamelCase_				, UpperCamelCase_    )
     writer.write("""%s = %s\n""" % (key, value)    )

   results.update(UpperCamelCase_    )

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

  UpperCamelCase          =	trainer.predict(UpperCamelCase_    )
  UpperCamelCase          =	align_predictions(UpperCamelCase_				, UpperCamelCase_    )

  UpperCamelCase          =	os.path.join(training_args.output_dir				, """test_results.txt"""    )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_				, """w"""    ) as writer:
    for key, value in metrics.items():
     logger.info("""  %s = %s"""				, UpperCamelCase_				, UpperCamelCase_    )
     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(UpperCamelCase_				, """w"""    ) as writer:
    with open(os.path.join(data_args.data_dir				, """test.txt"""    )				, """r"""    ) as f:
     token_classification_task.write_predictions_to_file(UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )

 return results





def        lowercase(     UpperCamelCase_    )						->     Union[str, Any]:







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


if __name__ == "__main__":
     main()

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_    					=       logging.get_logger(__name__)

a_    					=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   'megatron-bert'

 def __init__( self	:							List[str]   ,      snake_case	:							Tuple=2_9_0_5_6   ,      snake_case	:							Dict=1_0_2_4   ,      snake_case	:							Dict=2_4   ,      snake_case	:							Union[str, Any]=1_6   ,      snake_case	:							Optional[int]=4_0_9_6   ,      snake_case	:							Optional[int]="gelu"   ,      snake_case	:							Any=0.1   ,      snake_case	:							Tuple=0.1   ,      snake_case	:							Optional[int]=5_1_2   ,      snake_case	:							List[Any]=2   ,      snake_case	:							Tuple=0.02   ,      snake_case	:							Optional[Any]=1e-12   ,      snake_case	:							str=0   ,      snake_case	:							Optional[int]="absolute"   ,      snake_case	:							Union[str, Any]=True   ,      **snake_case	:							Any   ,      )   ->						List[Any]:

  """simple docstring"""





  super().__init__(pad_token_id=snake_case   ,      **snake_case			)

  UpperCamelCase_					:						Optional[Any] =					vocab_size
  UpperCamelCase_					:						Any =					hidden_size
  UpperCamelCase_					:						Union[str, Any] =					num_hidden_layers
  UpperCamelCase_					:						List[Any] =					num_attention_heads
  UpperCamelCase_					:						str =					hidden_act
  UpperCamelCase_					:						List[str] =					intermediate_size
  UpperCamelCase_					:						List[Any] =					hidden_dropout_prob
  UpperCamelCase_					:						Any =					attention_probs_dropout_prob
  UpperCamelCase_					:						Tuple =					max_position_embeddings
  UpperCamelCase_					:						Dict =					type_vocab_size
  UpperCamelCase_					:						Optional[int] =					initializer_range
  UpperCamelCase_					:						Optional[Any] =					layer_norm_eps
  UpperCamelCase_					:						Dict =					position_embedding_type
  UpperCamelCase_					:						List[str] =					use_cache



