

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    A__ :
    lowercase		   =						field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
    lowercase		   =						field(
        default=lowerCamelCase__  ,  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=lowerCamelCase__  ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
    lowercase		   =						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.
    lowercase		   =						field(
        default=lowerCamelCase__  ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,  )


@dataclass
class    A__ :
    lowercase		   =						field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}     )
    lowercase		   =						field(
        default=lowerCamelCase__  ,  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=lowerCamelCase__  ,  metadata={"help": "Overwrite the cached training and evaluation sets"}     )

def  UpperCAmelCase__      ( )     ->     str:
       A_		   = 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.
              A_		   = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
       else:
              A_		   = 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."""			)

       A_		   = import_module("""tasks"""			)
       try:
              A_		   = getattr(lowerCamelCase_,    model_args.task_type			)
              A_		   = 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
       A_		   = token_classification_task.get_labels(data_args.labels			)
       A_		   = dict(enumerate(lowerCamelCase_			)			)
       A_		   = len(lowerCamelCase_			)

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

       A_		   = 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,    )
       A_		   = 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,    )
       A_		   = 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
       A_		   = (
           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
       )
       A_		   = (
           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(UpperCAmelCase__,    UpperCAmelCase__			) -> Tuple[List[int], List[int]]:
              A_		   = np.argmax(lowerCamelCase_,    axis=2			)

              A_		   = preds.shape

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

       # Initialize our Trainer
       A_		   = 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
       A_		   = {}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""			)

              A_		   = trainer.evaluate()

              A_		   = 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:
              A_		   = 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,    )

              A_		   = trainer.predict(lowerCamelCase_			)
              A_		   = align_predictions(lowerCamelCase_,    lowerCamelCase_			)

              A_		   = 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
              A_		   = 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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[int]:
       main()


if __name__ == "__main__":
      main()



'''simple docstring'''







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase						    =      get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						ReformerTokenizer
    lowercase		   =						ReformerTokenizerFast
    lowercase		   =						True
    lowercase		   =						False
    lowercase		   =						True



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           super().setUp()

           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )
           tokenizer.save_pretrained(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = """<s>"""
           A_		   = 1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__    )						,			UpperCamelCase__    )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__    )						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = list(self.get_tokenizer().get_vocab().keys()    )

           self.assertEqual(vocab_keys[0]						,			"""<unk>"""    )
           self.assertEqual(vocab_keys[1]						,			"""<s>"""    )
           self.assertEqual(vocab_keys[-1]						,			"""j"""    )
           self.assertEqual(len(UpperCamelCase__    )						,			1000    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           self.assertEqual(self.get_tokenizer().vocab_size						,			1000    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           if not self.test_rust_tokenizer:
                  return

           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()

           A_		   = """I was born in 92000, and this is falsé."""

           A_		   = tokenizer.tokenize(UpperCamelCase__    )
           A_		   = rust_tokenizer.tokenize(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = self.get_rust_tokenizer()
           A_		   = tokenizer.encode(UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__=15    )  -> int:





           '''simple docstring'''
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         # Simple input
                         A_		   = """This is a simple input"""
                         A_		   = ["""This is a simple input 1""", """This is a simple input 2"""]
                         A_		   = ("""This is a simple input""", """This is a pair""")
                         A_		   = [
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )

           A_		   = tokenizer.tokenize("""This is a test"""    )
           self.assertListEqual(UpperCamelCase__						,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__    )						,			[285, 46, 10, 170, 382]						,			)

           A_		   = tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
           self.assertListEqual(
               UpperCamelCase__						,			[
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """9""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """é""",
                   """.""",
               ]						,			)
           A_		   = tokenizer.convert_tokens_to_ids(UpperCamelCase__    )
           self.assertListEqual(
               UpperCamelCase__						,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,			)

           A_		   = tokenizer.convert_ids_to_tokens(UpperCamelCase__    )
           self.assertListEqual(
               UpperCamelCase__						,			[
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """<unk>""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """<unk>""",
                   """.""",
               ]						,			)



    @cached_property
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""    )



    @slow
    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = """Hello World!"""
           A_		   = [126, 32, 262, 152, 38, 72, 287]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @slow
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = (
               """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
               """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
           )
           A_		   = [
               108,
               265,
               24,
               111,
               4,
               258,
               156,
               35,
               28,
               275,
               3,
               259,
               297,
               260,
               84,
               4,
               35,
               110,
               44,
               8,
               259,
               91,
               268,
               21,
               11,
               209,
               274,
               109,
               266,
               277,
               117,
               86,
               93,
               315,
               258,
               278,
               258,
               277,
               258,
               0,
               258,
               288,
               258,
               319,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               287,
               258,
               315,
               258,
               289,
               258,
               278,
               99,
               269,
               266,
               262,
               8,
               259,
               241,
               4,
               217,
               230,
               268,
               266,
               55,
               168,
               106,
               75,
               193,
               266,
               223,
               27,
               49,
               26,
               282,
               25,
               264,
               299,
               19,
               26,
               0,
               258,
               277,
               117,
               86,
               93,
               176,
               183,
               270,
               11,
               262,
               42,
               61,
               265,
           ]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @require_torch
    @slow
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           A_		   = list(self.big_tokenizer.get_vocab().keys()    )[:10]
           A_		   = """ """.join(UpperCamelCase__    )
           A_		   = self.big_tokenizer.encode_plus(UpperCamelCase__						,			return_tensors="""pt"""    )
           A_		   = self.big_tokenizer.batch_encode_plus([sequence, sequence]						,			return_tensors="""pt"""    )

           A_		   = ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           A_		   = encoded_sequence["""input_ids"""].shape
           A_		   = ReformerModel(UpperCamelCase__    )

           # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                  model(**UpperCamelCase__    )
                  model(**UpperCamelCase__    )







    @slow
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           # fmt: off
           A_		   = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # This tokenizer does not know some characters like ")".
           # That is the reason why we use very simple texts here.
           # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
           A_		   = [
               """This is a very simple sentence.""",
               """The quick brown fox jumps over the lazy dog.""",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__						,			model_name="""google/reformer-crime-and-punishment"""						,			revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""						,			padding=UpperCamelCase__						,			sequences=UpperCamelCase__						,			)
