




import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


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







@dataclass
class 		__UpperCAmelCase:





     """simple docstring"""



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







@dataclass
class 		__UpperCAmelCase:





     """simple docstring"""



     __UpperCAmelCase						:			str							=     field(
         metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
     __UpperCAmelCase						:			Optional[str]							=     field(
         metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}			)
     __UpperCAmelCase						:			int							=     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."""
             )
         }							,						)
     __UpperCAmelCase						:			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.
    a__  			=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
    a__			,		a__			,		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(UpperCAmelCase_ ,     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 ,     )
    logger.info(
        """n_replicas: %s, distributed training: %s, 16-bits training: %s""" ,     training_args.n_replicas ,     bool(training_args.n_replicas > 1		) ,     training_args.fpaa ,     )
    logger.info("""Training/evaluation parameters %s""" ,     UpperCAmelCase_		)

    # Prepare Token Classification task
    a__  			=							token_classification_task.get_labels(data_args.labels		)
    a__  			=							dict(enumerate(UpperCAmelCase_		)		)
    a__  			=							len(UpperCAmelCase_		)

    # 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=UpperCAmelCase_ ,     idalabel=UpperCAmelCase_ ,     labelaid={label: i for i, label in enumerate(UpperCAmelCase_		)} ,     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 ,     )

    with training_args.strategy.scope():
        a__  			=							TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path ,     from_pt=bool(""".bin""" in model_args.model_name_or_path		) ,     config=UpperCAmelCase_ ,     cache_dir=model_args.cache_dir ,     )

    # Get datasets
    a__  			=							(
        TFTokenClassificationDataset(
            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
    )
    a__  			=							(
        TFTokenClassificationDataset(
            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_ ,     UpperCAmelCase_		) -> Tuple[List[int], List[int]]:
        a__  			=							np.argmax(UpperCAmelCase_ ,     axis=2		)
        a__			,		a__  			=							preds.shape
        a__  			=							[[] for _ in range(UpperCAmelCase_		)]
        a__  			=							[[] for _ in range(UpperCAmelCase_		)]

        for i in range(UpperCAmelCase_		):
            for j in range(UpperCAmelCase_		):
                if label_ids[i, j] != -100:
                    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__			,		a__  			=							align_predictions(p.predictions ,     p.label_ids		)

        return {
            "precision": precision_score(UpperCAmelCase_ ,     UpperCAmelCase_		),
            "recall": recall_score(UpperCAmelCase_ ,     UpperCAmelCase_		),
            "f1": fa_score(UpperCAmelCase_ ,     UpperCAmelCase_		),
        }

    # Initialize our Trainer
    a__  			=							TFTrainer(
        model=UpperCAmelCase_ ,     args=UpperCAmelCase_ ,     train_dataset=train_dataset.get_dataset() if train_dataset else None ,     eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,     compute_metrics=UpperCAmelCase_ ,     )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        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"""		)

        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:
        a__  			=							TFTokenClassificationDataset(
            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 ,     )

        a__			,		a__			,		a__  			=							trainer.predict(test_dataset.get_dataset()		)
        a__			,		a__  			=							align_predictions(UpperCAmelCase_ ,     UpperCAmelCase_		)
        a__  			=							classification_report(UpperCAmelCase_ ,     UpperCAmelCase_		)

        logger.info("""\n%s""" ,     UpperCAmelCase_		)

        a__  			=							os.path.join(training_args.output_dir ,     """test_results.txt"""		)

        with open(UpperCAmelCase_ ,     """w"""		) as writer:
            writer.write("""%s\n""" % report		)

        # Save predictions
        a__  			=							os.path.join(training_args.output_dir ,     """test_predictions.txt"""		)

        with open(UpperCAmelCase_ ,     """w"""		) as writer:
            with open(os.path.join(data_args.data_dir ,     """test.txt"""		) ,     """r"""		) as f:
                a__  			=							0

                for line in f:
                    if line.startswith("""-DOCSTART-"""		) or line == "" or line == "\n":
                        writer.write(UpperCAmelCase_		)

                        if not preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        a__  			=							line.split()[0] + """ """ + preds_list[example_id].pop(0		) + """\n"""

                        writer.write(UpperCAmelCase_		)
                    else:
                        logger.warning("""Maximum sequence length exceeded: No prediction for \'%s\'.""" ,     line.split()[0]		)

    return results


if __name__ == "__main__":
 main()










'''simple docstring'''

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class    a__					(			__A				,    unittest.TestCase   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						SpeechaTextaTokenizer
				__UpperCamelCase     :		str						=						False




				def        _snake_case	(self		):
										super().setUp()

										__lowerCAmelCase											=					'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''		)
										__lowerCAmelCase											=					['''he re</w> 123''', '''here a 1456''']
										__lowerCAmelCase											=					dict(zip(__lowercase							,      range(len(__lowercase		)		)		)		)

										__lowerCAmelCase											=					{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

										__lowerCAmelCase											=					tempfile.mkdtemp()
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file''']		)
										__lowerCAmelCase											=					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(__lowercase		) + '''\n'''		)

										with open(self.merges_file							,      '''w'''		) as fp:
																fp.write('''\n'''.join(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]							,      '''<s>'''		)
										self.assertEqual(vocab_keys[1]							,      '''<pad>'''		)
										self.assertEqual(vocab_keys[-1]							,      '''vocab'''		)
										self.assertEqual(len(__lowercase		)							,      14		)




				def        _snake_case	(self		):
										self.assertEqual(self.get_tokenizer().vocab_size							,      14		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										# make sure @@ is correctly concatenated
										__lowerCAmelCase											=					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
										__lowerCAmelCase											=					tokenizer.decode(__lowercase		)

										self.assertTrue(output_string == '''herecouple words ofthe'''		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																tokenizer.save_pretrained(__lowercase		)
																os.remove(os.path.join(__lowercase							,      '''merges.txt'''		)		)

																# load tokenizer without merges file should not throw an error
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																# save tokenizer and load again
																tokenizer.save_pretrained(__lowercase		)
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										self.assertIsNotNone(__lowercase		)




				def        _snake_case	(self		):
										if not self.test_slow_tokenizer:
																return

										__lowerCAmelCase											=					inspect.signature(self.tokenizer_class.__init__		)
										__lowerCAmelCase											=					self.get_tokenizer()

										for parameter_name, parameter in signature.parameters.items():
																if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(__lowercase							,      tokenizer.init_kwargs		)

