






'''simple docstring'''






import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


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




@require_sentencepiece
@require_tokenizers
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
			_snake_case        =							XLNetTokenizer
			_snake_case        =							XLNetTokenizerFast
			_snake_case        =							True
			_snake_case        =							True







			def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						__snake_case				:      int     							= XLNetTokenizer(__a					,   keep_accents=__a)
						tokenizer.sanitize_special_tokens()
						tokenizer.save_pretrained(self.tmpdirname)







			def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
						"""simple docstring"""






						__snake_case				:      Optional[int]     							= '<s>'
						__snake_case				:      Tuple     							= 1

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







			def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
						"""simple docstring"""






						__snake_case				:      Tuple     							= list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]					,   '<unk>')
						self.assertEqual(vocab_keys[1]					,   '<s>')
						self.assertEqual(vocab_keys[-1]					,   '<eod>')
						self.assertEqual(len(__a)					,   1_0_0_6)







			def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size					,   1_0_0_0)







			def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
						"""simple docstring"""






						__snake_case				:      Dict     							= XLNetTokenizer(__a					,   keep_accents=__a)

						__snake_case				:      List[Any]     							= tokenizer.tokenize('This is a test')
						self.assertListEqual(__a					,   ['▁This', '▁is', '▁a', '▁t', 'est'])

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__a)					,   [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])

						__snake_case				:      List[Any]     							= tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    __a					,   [
						        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',
						        '.',
						    ]					,   )
						__snake_case				:      Tuple     							= tokenizer.convert_tokens_to_ids(__a)
						self.assertListEqual(__a					,   [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])

						__snake_case				:      Union[str, Any]     							= tokenizer.convert_ids_to_tokens(__a)
						self.assertListEqual(
						    __a					,   [
						        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>',
						        '.',
						    ]					,   )







			def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
						"""simple docstring"""






						__snake_case				:      List[Any]     							= XLNetTokenizer(__a					,   do_lower_case=__a)
						__snake_case				:      Optional[int]     							= tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    __a					,   [
						        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',
						        'se',
						        '.',
						    ]					,   )
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo')					,   ['▁he', 'll', 'o'])







			def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
						"""simple docstring"""






						__snake_case				:      int     							= XLNetTokenizer(__a					,   do_lower_case=__a)
						__snake_case				:      Tuple     							= tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    __a					,   [
						        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',
						        'se',
						        '.',
						    ]					,   )







			@slow
			def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
						"""simple docstring"""






						__snake_case				:      List[Any]     							= XLNetTokenizer.from_pretrained('xlnet-base-cased')

						__snake_case				:      List[str]     							= tokenizer.encode('sequence builders'					,   add_special_tokens=__a)
						__snake_case				:      List[Any]     							= tokenizer.encode('multi-sequence build'					,   add_special_tokens=__a)

						__snake_case				:      Dict     							= tokenizer.build_inputs_with_special_tokens(__a)
						__snake_case				:      Dict     							= tokenizer.build_inputs_with_special_tokens(__a					,   __a)

						assert encoded_sentence == text + [4, 3]
						assert encoded_pair == text + [4] + text_a + [4, 3]







			@slow
			def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
						"""simple docstring"""






						__snake_case				:      List[str]     							= {'input_ids': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '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, 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, 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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__a					,   model_name='xlnet-base-cased'					,   revision='c841166438c31ec7ca9a106dee7bb312b73ae511'					,   )







'''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


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




def        _SCREAMING_SNAKE_CASE  (				)    -> 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.

   __snake_case				:      List[Any]     							= 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.
      __snake_case							,__snake_case							,__snake_case				:      List[str]     							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      __snake_case							,__snake_case							,__snake_case				:      int     							= 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.'  )

   __snake_case				:      List[str]     							= import_module('tasks'  )
   try:
      __snake_case				:      Any     							= getattr(A  ,		model_args.task_type  )
      __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'  ,		A  )

   # Set seed
   set_seed(training_args.seed  )

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

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

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

      __snake_case							,__snake_case				:      int     							= preds.shape

      __snake_case				:      Dict     							= [[] for _ in range(A  )]
      __snake_case				:      Union[str, Any]     							= [[] 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  :       EvalPrediction  ) -> Dict:
      __snake_case							,__snake_case				:      Any     							= 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
   __snake_case				:      Optional[int]     							= DataCollatorWithPadding(A  ,		pad_to_multiple_of=8  ) if training_args.fpaa else None

   # Initialize our Trainer
   __snake_case				:      Optional[Any]     							= 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
   __snake_case				:      List[Any]     							= {}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'  )

      __snake_case				:      List[str]     							= trainer.evaluate()

      __snake_case				:      Tuple     							= 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:
      __snake_case				:      str     							= 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  ,		)

      __snake_case							,__snake_case							,__snake_case				:      str     							= trainer.predict(A  )
      __snake_case							,__snake_case				:      List[str]     							= align_predictions(A  ,		A  )

      __snake_case				:      Optional[int]     							= 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
      __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Any:







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


if __name__ == "__main__":
      main()