






'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							LEDTokenizer
   _snake_case        =							LEDTokenizerFast
   _snake_case        =							True







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      super().setUp()
      __snake_case				:      Optional[int]     							= [
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
      ]
      __snake_case				:      Any     							= dict(zip(__a					,   range(len(__a))))
      __snake_case				:      str     							= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      __snake_case				:      int     							= {'unk_token': '<unk>'}

      __snake_case				:      Dict     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file'])
      __snake_case				:      List[str]     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['merges_file'])
      with open(self.vocab_file					,   'w'					,   encoding='utf-8') as fp:
         fp.write(json.dumps(__a) + '\n')
      with open(self.merges_file					,   'w'					,   encoding='utf-8') as fp:
         fp.write('\n'.join(__a))







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      Union[str, Any]:
      """simple docstring"""






      kwargs.update(self.special_tokens_map)
      return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **__a)







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






      kwargs.update(self.special_tokens_map)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,   **__a)







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






      return "lower newer", "lower newer"







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






      return LEDTokenizer.from_pretrained('allenai/led-base-16384')







   @cached_property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return LEDTokenizerFast.from_pretrained('allenai/led-base-16384')







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      __snake_case				:      int     							= [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Any     							= tokenizer(__a					,   max_length=len(__a)					,   padding=__a					,   return_tensors='pt')
         self.assertIsInstance(__a					,   __a)

         self.assertEqual((2, 9)					,   batch.input_ids.shape)
         self.assertEqual((2, 9)					,   batch.attention_mask.shape)
         __snake_case				:      List[Any]     							= batch.input_ids.tolist()[0]
         self.assertListEqual(__a					,   __a)







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      str     							= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Tuple     							= tokenizer(__a					,   padding=__a					,   return_tensors='pt')
         self.assertIn('input_ids'					,   __a)
         self.assertIn('attention_mask'					,   __a)
         self.assertNotIn('labels'					,   __a)
         self.assertNotIn('decoder_attention_mask'					,   __a)







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      str     							= [
          'Summary of the text.',
          'Another summary.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Any     							= tokenizer(text_target=__a					,   max_length=3_2					,   padding='max_length'					,   return_tensors='pt')
         self.assertEqual(3_2					,   targets['input_ids'].shape[1])







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Optional[Any]     							= tokenizer(
             ['I am a small frog' * 1_0_2_4, 'I am a small frog']					,   padding=__a					,   truncation=__a					,   return_tensors='pt')
         self.assertIsInstance(__a					,   __a)
         self.assertEqual(batch.input_ids.shape					,   (2, 5_1_2_2))







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






      __snake_case				:      int     							= ['A long paragraph for summarization.']
      __snake_case				:      List[str]     							= [
          'Summary of the text.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      str     							= tokenizer(__a					,   return_tensors='pt')
         __snake_case				:      Union[str, Any]     							= tokenizer(text_target=__a					,   return_tensors='pt')
         __snake_case				:      Any     							= inputs['input_ids']
         __snake_case				:      List[Any]     							= targets['input_ids']
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      List[Any]     							= ['Summary of the text.', 'Another summary.']
         __snake_case				:      List[str]     							= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

         __snake_case				:      List[str]     							= tokenizer(__a					,   padding=__a)
         __snake_case				:      Optional[Any]     							= [[0] * len(__a) for x in encoded_output['input_ids']]
         __snake_case				:      Dict     							= tokenizer.pad(__a)
         self.assertSequenceEqual(outputs['global_attention_mask']					,   __a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      pass







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






      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
            __snake_case				:      Optional[int]     							= self.rust_tokenizer_class.from_pretrained(__a					,   **__a)
            __snake_case				:      Union[str, Any]     							= self.tokenizer_class.from_pretrained(__a					,   **__a)
            __snake_case				:      List[str]     							= 'A, <mask> AllenNLP sentence.'
            __snake_case				:      int     							= tokenizer_r.encode_plus(__a					,   add_special_tokens=__a					,   return_token_type_ids=__a)
            __snake_case				:      Optional[int]     							= tokenizer_p.encode_plus(__a					,   add_special_tokens=__a					,   return_token_type_ids=__a)
            self.assertEqual(sum(tokens_r['token_type_ids'])					,   sum(tokens_p['token_type_ids']))
            self.assertEqual(
                sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])					,   sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])					,   )

            __snake_case				:      Optional[int]     							= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
            __snake_case				:      Dict     							= tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])
            self.assertSequenceEqual(tokens_p['input_ids']					,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
            self.assertSequenceEqual(tokens_r['input_ids']					,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

            self.assertSequenceEqual(
                __a					,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
            self.assertSequenceEqual(
                __a					,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])







'''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()