"""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


lowerCAmelCase_:					Tuple     =					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=_a					,					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=_a					,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
   snake_case_							=      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.
   snake_case_							=      field(
       default=_a					,					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=_a					,					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=_a					,					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(A    ,				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"    ,				A      )

     # Set seed
     set_seed(training_args.seed      )

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

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

     # Get datasets
     lowercase__				 =							(
         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
     )
     lowercase__				 =							(
         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    ,				A      ) -> Tuple[List[int], List[int]]:
          lowercase__				 =							np.argmax(A    ,				axis=2      )

          lowercase__ ,						lowercase__				 =							preds.shape

          lowercase__				 =							[[] for _ in range(A      )]
          lowercase__				 =							[[] 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      ) -> Dict:
          lowercase__ ,						lowercase__				 =							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
     lowercase__				 =							DataCollatorWithPadding(A    ,				pad_to_multiple_of=8      ) if training_args.fpaa else None

     # Initialize our Trainer
     lowercase__				 =							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
     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(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:
          lowercase__				 =							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    ,				)

          lowercase__ ,						lowercase__ ,						lowercase__				 =							trainer.predict(A      )
          lowercase__ ,						lowercase__				 =							align_predictions(A    ,				A      )

          lowercase__				 =							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
          lowercase__				 =							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 					__a   (		A      ):






     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




