






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    :   Tuple        =			logging.getLogger(__name__)







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



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







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



      A          =    field(
          metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
      A          =    field(
          default=_A				,							metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}				,							)
      A          =    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.'''
              )
          }				,							)
      A          =    field(
          default=_A				,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def 				snake_case__							(							):
    # 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.

    lowerCAmelCase__   :Optional[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.
        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Union[str, Any]      							=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
    else:
        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				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."       )

    lowerCAmelCase__   :Tuple      							=				import_module("tasks"       )
    try:
        lowerCAmelCase__   :Tuple      							=				getattr(UpperCAmelCase    ,			model_args.task_type       )
        lowerCAmelCase__   :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"    ,			UpperCAmelCase       )

    # Set seed
    set_seed(training_args.seed       )

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

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

    lowerCAmelCase__   :Any      							=				AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,			num_labels=UpperCAmelCase    ,			idalabel=UpperCAmelCase    ,			labelaid={label: i for i, label in enumerate(UpperCAmelCase       )}    ,			cache_dir=model_args.cache_dir    ,			)
    lowerCAmelCase__   :Any      							=				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    ,			)
    lowerCAmelCase__   :Optional[int]      							=				AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path    ,			from_tf=bool(".ckpt" in model_args.model_name_or_path       )    ,			config=UpperCAmelCase    ,			cache_dir=model_args.cache_dir    ,			)

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

        lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				preds.shape

        lowerCAmelCase__   :List[str]      							=				[[] for _ in range(UpperCAmelCase       )]
        lowerCAmelCase__   :List[Any]      							=				[[] for _ in range(UpperCAmelCase       )]

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

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

    # Initialize our Trainer
    lowerCAmelCase__   :List[str]      							=				Trainer(
        model=UpperCAmelCase    ,			args=UpperCAmelCase    ,			train_dataset=UpperCAmelCase    ,			eval_dataset=UpperCAmelCase    ,			compute_metrics=UpperCAmelCase    ,			data_collator=UpperCAmelCase    ,			)

    # 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
    lowerCAmelCase__   :Optional[Any]      							=				{}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"       )

        lowerCAmelCase__   :Tuple      							=				trainer.evaluate()

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

            results.update(UpperCAmelCase       )

    # Predict
    if training_args.do_predict:
        lowerCAmelCase__   :Union[str, Any]      							=				TokenClassificationDataset(
            token_classification_task=UpperCAmelCase    ,			data_dir=data_args.data_dir    ,			tokenizer=UpperCAmelCase    ,			labels=UpperCAmelCase    ,			model_type=config.model_type    ,			max_seq_length=data_args.max_seq_length    ,			overwrite_cache=data_args.overwrite_cache    ,			mode=Split.test    ,			)

        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Optional[Any]      							=				trainer.predict(UpperCAmelCase       )
        lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				align_predictions(UpperCAmelCase    ,			UpperCAmelCase       )

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

        # Save predictions
        lowerCAmelCase__   :Union[str, Any]      							=				os.path.join(training_args.output_dir    ,			"test_predictions.txt"       )
        if trainer.is_world_process_zero():
            with open(UpperCAmelCase    ,			"w"       ) as writer:
                with open(os.path.join(data_args.data_dir    ,			"test.txt"       )    ,			"r"       ) as f:
                    token_classification_task.write_predictions_to_file(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )

    return results


def 				snake_case__							(							UpperCAmelCase							:  List[Any]       ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()











from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel







@require_tf
class 	_UpperCAmelCase  :




      """simple docstring"""



      A          =    MBartConfig
      A          =    {}
      A          =    '''gelu'''
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=99    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase=37    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=20    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=0    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				parent
          lowerCAmelCase__   :Dict      							=				batch_size
          lowerCAmelCase__   :Optional[int]      							=				seq_length
          lowerCAmelCase__   :Any      							=				is_training
          lowerCAmelCase__   :List[Any]      							=				use_labels
          lowerCAmelCase__   :Optional[int]      							=				vocab_size
          lowerCAmelCase__   :Optional[int]      							=				hidden_size
          lowerCAmelCase__   :Optional[Any]      							=				num_hidden_layers
          lowerCAmelCase__   :List[str]      							=				num_attention_heads
          lowerCAmelCase__   :List[str]      							=				intermediate_size
          lowerCAmelCase__   :List[str]      							=				hidden_dropout_prob
          lowerCAmelCase__   :Optional[Any]      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :Tuple      							=				max_position_embeddings
          lowerCAmelCase__   :Optional[Any]      							=				eos_token_id
          lowerCAmelCase__   :int      							=				pad_token_id
          lowerCAmelCase__   :Any      							=				bos_token_id
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				ids_tensor([self.batch_size, self.seq_length - 1]    ,	self.vocab_size		)
          lowerCAmelCase__   :List[Any]      							=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				tf.concat([input_ids, eos_tensor]    ,	axis=1		)

          lowerCAmelCase__   :str      							=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size		)

          lowerCAmelCase__   :Dict      							=				self.config_cls(
              vocab_size=self.vocab_size    ,	d_model=self.hidden_size    ,	encoder_layers=self.num_hidden_layers    ,	decoder_layers=self.num_hidden_layers    ,	encoder_attention_heads=self.num_attention_heads    ,	decoder_attention_heads=self.num_attention_heads    ,	encoder_ffn_dim=self.intermediate_size    ,	decoder_ffn_dim=self.intermediate_size    ,	dropout=self.hidden_dropout_prob    ,	attention_dropout=self.attention_probs_dropout_prob    ,	max_position_embeddings=self.max_position_embeddings    ,	eos_token_ids=[2]    ,	bos_token_id=self.bos_token_id    ,	pad_token_id=self.pad_token_id    ,	decoder_start_token_id=self.pad_token_id    ,	**self.config_updates    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				prepare_mbart_inputs_dict(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)
          return config, inputs_dict



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				TFMBartModel(config=_lowerCAmelCase		).get_decoder()
          lowerCAmelCase__   :Tuple      							=				inputs_dict["input_ids"]

          lowerCAmelCase__   :Dict      							=				input_ids[:1, :]
          lowerCAmelCase__   :Dict      							=				inputs_dict["attention_mask"][:1, :]
          lowerCAmelCase__   :List[str]      							=				inputs_dict["head_mask"]
          lowerCAmelCase__   :Optional[Any]      							=				1

          # first forward pass
          lowerCAmelCase__   :List[Any]      							=				model(_lowerCAmelCase    ,	attention_mask=_lowerCAmelCase    ,	head_mask=_lowerCAmelCase    ,	use_cache=_lowerCAmelCase		)

          lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				outputs.to_tuple()
          lowerCAmelCase__   :Tuple      							=				past_key_values[1]





def 				snake_case__							(							UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  Optional[Any]    ,			UpperCAmelCase							:  Optional[int]    ,			UpperCAmelCase							:  int=None    ,			UpperCAmelCase							:  Optional[Any]=None    ,			UpperCAmelCase							:  Any=None    ,			UpperCAmelCase							:  Dict=None    ,			UpperCAmelCase							:  Dict=None    ,			):
    if attention_mask is None:
        lowerCAmelCase__   :str      							=				tf.cast(tf.math.not_equal(UpperCAmelCase    ,			config.pad_token_id       )    ,			tf.inta       )
    if decoder_attention_mask is None:
        lowerCAmelCase__   :int      							=				tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape    ,			dtype=tf.inta       ),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,			config.pad_token_id       )    ,			tf.inta       ),
            ]    ,			axis=-1    ,			)
    if head_mask is None:
        lowerCAmelCase__   :str      							=				tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        lowerCAmelCase__   :Tuple      							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        lowerCAmelCase__   :Optional[Any]      							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }







@require_tf
class 	_UpperCAmelCase  (  _A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
      A          =    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
      A          =    (
          {
              '''conversational''': TFMBartForConditionalGeneration,
              '''feature-extraction''': TFMBartModel,
              '''summarization''': TFMBartForConditionalGeneration,
              '''text2text-generation''': TFMBartForConditionalGeneration,
              '''translation''': TFMBartForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      A          =    True
      A          =    False
      A          =    False
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
              # Exception encountered when calling layer '...'
              return True

          return False
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				TFMBartModelTester(self		)
          lowerCAmelCase__   :Union[str, Any]      							=				ConfigTester(self    ,	config_class=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.config_tester.run_common_tests()



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)







@require_sentencepiece
@require_tokenizers
@require_tf
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      A          =    [
          ''' UN Chief Says There Is No Military Solution in Syria''',
      ]
      A          =    [
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
      ]
      A          =    '''facebook/mbart-large-en-ro'''
      @cached_property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return AutoTokenizer.from_pretrained(self.model_name		)
      @cached_property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
          return model
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.translate_src_text(**_lowerCAmelCase		)
          self.assertListEqual(self.expected_text    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.tokenizer(self.src_text    ,	**_lowerCAmelCase    ,	return_tensors="tf"		)
          lowerCAmelCase__   :str      							=				self.model.generate(
              model_inputs.input_ids    ,	attention_mask=model_inputs.attention_mask    ,	num_beams=2		)
          lowerCAmelCase__   :int      							=				self.tokenizer.batch_decode(_lowerCAmelCase    ,	skip_special_tokens=_lowerCAmelCase		)
          return generated_words



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._assert_generated_batch_equal_expected()



