

'''simple docstring'''




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )



class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_=1_3 ,			a_=7 ,			a_=True ,			a_=True ,			a_=True ,			a_=True ,			a_=9_9 ,			a_=3_2 ,			a_=5 ,			a_=4 ,			a_=3_7 ,			a_="gelu" ,			a_=0.1 ,			a_=0.1 ,			a_=5_1_2 ,			a_=1_6 ,			a_=2 ,			a_=0.02 ,			a_=4 ,			)				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						parent
    UpperCAmelCase    				=						batch_size
    UpperCAmelCase    				=						seq_length
    UpperCAmelCase    				=						is_training
    UpperCAmelCase    				=						use_attention_mask
    UpperCAmelCase    				=						use_token_type_ids
    UpperCAmelCase    				=						use_labels
    UpperCAmelCase    				=						vocab_size
    UpperCAmelCase    				=						hidden_size
    UpperCAmelCase    				=						num_hidden_layers
    UpperCAmelCase    				=						num_attention_heads
    UpperCAmelCase    				=						intermediate_size
    UpperCAmelCase    				=						hidden_act
    UpperCAmelCase    				=						hidden_dropout_prob
    UpperCAmelCase    				=						attention_probs_dropout_prob
    UpperCAmelCase    				=						max_position_embeddings
    UpperCAmelCase    				=						type_vocab_size
    UpperCAmelCase    				=						type_sequence_label_size
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						num_choices
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

    UpperCAmelCase    				=						None
    if self.use_attention_mask:
      UpperCAmelCase    				=						random_attention_mask([self.batch_size, self.seq_length]  )

    UpperCAmelCase    				=						None
    if self.use_token_type_ids:
      UpperCAmelCase    				=						ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size  )

    UpperCAmelCase    				=						RoFormerConfig(
        vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			is_decoder=a_ ,			initializer_range=self.initializer_range ,			)

    return config, input_ids, token_type_ids, attention_mask





  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						self.prepare_config_and_inputs()
    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						config_and_inputs
    UpperCAmelCase    				=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
    return config, inputs_dict




@require_flax
class 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[Any]     	= True

  __lowerCAmelCase       :			Dict     	= (
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						FlaxRoFormerModelTester(self  )





  @slow
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    for model_class_name in self.all_model_classes:
      UpperCAmelCase    				=						model_class_name.from_pretrained('junnyu/roformer_chinese_small' ,			from_pt=a_  )
      UpperCAmelCase    				=						model(np.ones((1, 1)  )  )
      self.assertIsNotNone(a_  )




@require_flax
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  @slow
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'  )
    UpperCAmelCase    				=						jnp.array([[0, 1, 2, 3, 4, 5]]  )
    UpperCAmelCase    				=						model(a_  )[0]

    UpperCAmelCase    				=						5_0_0_0_0

    UpperCAmelCase    				=						(1, 6, vocab_size)
    self.assertEqual(output.shape ,			a_  )

    UpperCAmelCase    				=						jnp.array(
        [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]  )

    self.assertTrue(jnp.allclose(output[:, :3, :3] ,			a_ ,			atol=1E-4  )  )





'''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					:			List[Any]							=    logging.getLogger(__name__)



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default="NER"			,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  __lowerCAmelCase       :			bool     	= 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.
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,							)




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= field(
      metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}			,							)
  __lowerCAmelCase       :			int     	= field(
      default=1_28			,							metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,							)
  __lowerCAmelCase       :			bool     	= field(
      default=a			,							metadata={"help": "Overwrite the cached training and evaluation sets"}			)




def    lowerCamelCase__		(    ):
  # 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.

  UpperCAmelCase    				=						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.
    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
  else:
    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						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.'    )

  UpperCAmelCase    				=						import_module('tasks'    )
  try:
    UpperCAmelCase    				=						getattr(SCREAMING_SNAKE_CASE							, model_args.task_type    )
    UpperCAmelCase    				=						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'							, SCREAMING_SNAKE_CASE    )

  # Set seed
  set_seed(training_args.seed    )

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

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

  UpperCAmelCase    				=						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE    )}							, cache_dir=model_args.cache_dir							, )
  UpperCAmelCase    				=						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							, )
  UpperCAmelCase    				=						AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path							, from_tf=bool('.ckpt' in model_args.model_name_or_path    )							, config=SCREAMING_SNAKE_CASE							, cache_dir=model_args.cache_dir							, )

  # Get datasets
  UpperCAmelCase    				=						(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, 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
  )
  UpperCAmelCase    				=						(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, 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(SCREAMING_SNAKE_CASE   : np.ndarray							, SCREAMING_SNAKE_CASE   : np.ndarray    ) -> Tuple[List[int], List[int]]:
    UpperCAmelCase    				=						np.argmax(SCREAMING_SNAKE_CASE							, axis=2    )

    UpperCAmelCase				,   UpperCAmelCase    				=						preds.shape

    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]
    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]

    for i in range(SCREAMING_SNAKE_CASE    ):
      for j in range(SCREAMING_SNAKE_CASE    ):
        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(SCREAMING_SNAKE_CASE   : EvalPrediction    ) -> Dict:
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(p.predictions							, p.label_ids    )
    return {
        "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "precision": precision_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "recall": recall_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
        "f1": fa_score(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ),
    }

  # Data collator
  UpperCAmelCase    				=						DataCollatorWithPadding(SCREAMING_SNAKE_CASE							, pad_to_multiple_of=8    ) if training_args.fpaa else None

  # Initialize our Trainer
  UpperCAmelCase    				=						Trainer(
      model=SCREAMING_SNAKE_CASE							, args=SCREAMING_SNAKE_CASE							, train_dataset=SCREAMING_SNAKE_CASE							, eval_dataset=SCREAMING_SNAKE_CASE							, compute_metrics=SCREAMING_SNAKE_CASE							, data_collator=SCREAMING_SNAKE_CASE							, )

  # 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
  UpperCAmelCase    				=						{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'    )

    UpperCAmelCase    				=						trainer.evaluate()

    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'eval_results.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        logger.info('***** Eval results *****'    )
        for key, value in result.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          writer.write('%s = %s\n' % (key, value)    )

      results.update(SCREAMING_SNAKE_CASE    )

    # Predict
  if training_args.do_predict:
    UpperCAmelCase    				=						TokenClassificationDataset(
        token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, model_type=config.model_type							, max_seq_length=data_args.max_seq_length							, overwrite_cache=data_args.overwrite_cache							, mode=Split.test							, )

    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						trainer.predict(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'test_results.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        for key, value in metrics.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          writer.write('%s = %s\n' % (key, value)    )

        # Save predictions
    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'test_predictions.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        with open(os.path.join(data_args.data_dir							, 'test.txt'    )							, 'r'    ) as f:
          token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  return results


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict    ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()


