



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


_lowerCamelCase						    =  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=UpperCamelCase			,    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=UpperCamelCase			,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
     lowerCAmelCase					=			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.
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    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=UpperCamelCase			,    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=UpperCamelCase			,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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.
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
 else:
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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."     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	import_module("tasks"     )
 try:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	getattr(__lowerCamelCase		,	model_args.task_type     )
  __SCREAMING_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"		,	__lowerCamelCase     )

 # Set seed
 set_seed(training_args.seed     )

 # Prepare CONLL-2003 task
 __SCREAMING_SNAKE_CASE       :    str       =	token_classification_task.get_labels(data_args.labels     )
 __SCREAMING_SNAKE_CASE       :    Dict[int, str]       =	dict(enumerate(__lowerCamelCase     )     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	len(__lowerCamelCase     )

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

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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		,	)
 __SCREAMING_SNAKE_CASE       :    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		,	)
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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
 __SCREAMING_SNAKE_CASE       :    Dict       =	(
     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
 )
 __SCREAMING_SNAKE_CASE       :    str       =	(
     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(__lowerCamelCase   :     np.ndarray		,	__lowerCamelCase   :     np.ndarray     ) -> Tuple[List[int], List[int]]:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	np.argmax(__lowerCamelCase		,	axis=2     )

  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	preds.shape

  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[[] for _ in range(__lowerCamelCase     )]
  __SCREAMING_SNAKE_CASE       :    Dict       =	[[] 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(__lowerCamelCase   :     EvalPrediction     ) -> Dict:
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	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
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	DataCollatorWithPadding(__lowerCamelCase		,	pad_to_multiple_of=8     ) if training_args.fpaa else None

 # Initialize our Trainer
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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
 __SCREAMING_SNAKE_CASE       :    List[str]       =	{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"     )

  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	trainer.evaluate()

  __SCREAMING_SNAKE_CASE       :    List[Any]       =	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:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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		,	)

  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	trainer.predict(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	align_predictions(__lowerCamelCase		,	__lowerCamelCase     )

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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
  __SCREAMING_SNAKE_CASE       :    Tuple       =	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 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 main()


if __name__ == "__main__":
      main()











from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
      from .deepspeed import (
          DeepSpeedEngineWrapper,
          DeepSpeedOptimizerWrapper,
          DeepSpeedSchedulerWrapper,
          DummyOptim,
          DummyScheduler,
          HfDeepSpeedConfig,
      )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers






