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


__lowercase							 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






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


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						str						=      field(
        metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""} )
    a__	:						Optional[str]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						,	)
    a__	:						int						=      field(
        default=1_2_8						,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }						,	)
    a__	:						bool						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :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.
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
    else:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		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     :List[str]			  =		import_module('''tasks'''	)
    try:
        __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			model_args.task_type	)
        __UpperCamelCase     :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'''  ,			SCREAMING_SNAKE_CASE	)

    # Set seed
    set_seed(training_args.seed	)

    # Prepare CONLL-2003 task
    __UpperCamelCase     :Optional[Any]			  =		token_classification_task.get_labels(data_args.labels	)
    __UpperCamelCase     :Dict[int, str]			  =		dict(enumerate(SCREAMING_SNAKE_CASE	)	)
    __UpperCamelCase     :List[str]			  =		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     :int			  =		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     :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  ,			)
    __UpperCamelCase     :int			  =		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     :List[str]			  =		(
        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     :Optional[Any]			  =		(
        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  ,			SCREAMING_SNAKE_CASE	) -> Tuple[List[int], List[int]]:
        __UpperCamelCase     :Dict			  =		np.argmax(SCREAMING_SNAKE_CASE  ,			axis=2	)

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		preds.shape

        __UpperCamelCase     :List[str]			  =		[[] for _ in range(SCREAMING_SNAKE_CASE	)]
        __UpperCamelCase     :Optional[Any]			  =		[[] 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	) -> Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		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     :str			  =		DataCollatorWithPadding(SCREAMING_SNAKE_CASE  ,			pad_to_multiple_of=8	) if training_args.fpaa else None

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

        __UpperCamelCase     :Dict			  =		trainer.evaluate()

        __UpperCamelCase     :str			  =		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     :str			  =		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     :Optional[Any]			  =		trainer.predict(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		align_predictions(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :Tuple			  =		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     :Any			  =		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	):
    '''simple docstring'''







    main()


if __name__ == "__main__":
    main()









'''simple docstring'''



import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__lowercase	:						Tuple       =	pytest.mark.integration


__lowercase	:						Optional[int]       =	{'comet'}
__lowercase	:						List[str]       =	importlib.util.find_spec('fairseq') is not None

__lowercase	:						str       =	{'code_eval'}
__lowercase	:						List[Any]       =	os.name == 'nt'

__lowercase	:						Optional[Any]       =	{'bertscore', 'frugalscore', 'perplexity'}
__lowercase	:						Optional[Any]       =	importlib.util.find_spec('transformers') is not None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   int						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
										if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
															self.skipTest('"test requires Fairseq"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
										if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
															self.skipTest('"test requires transformers"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
										if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
															self.skipTest('"test not supported on Windows"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							():
					__a			:       List[Any]					    =  [metric_dir.split(os.sep   )[-2] for metric_dir in glob.glob('./metrics/*/'   )]
					return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()					)
@for_all_test_methods(
    lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_					)
@local
class 							__UpperCamelCase				(		parameterized.TestCase					):
	A_				=    {}
	A_				=    None




	@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'							)
	@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'							)
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       int					    =  '[...]'
						__a			:       Tuple					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						__a			:       Optional[Any]					    =  datasets.load.import_main_class(metric_module.__name__						,				dataset=__a							)
						# check parameters
						__a			:       Dict					    =  inspect.signature(metric._compute							).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()							)							)  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__a						,				metric_module.__name__							):
											with self.use_local_metrics():
																try:
																					__a			:       str					    =  doctest.testmod(__a						,				verbose=__a						,				raise_on_error=__a							)
																except doctest.UnexpectedException as e:
																					raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed						,				0							)
						self.assertGreater(results.attempted						,				1							)




	@slow
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  '[...]'
						__a			:       Optional[Any]					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						# run doctest
						with self.use_local_metrics():
											__a			:       List[Any]					    =  doctest.testmod(__a						,				verbose=__a						,				raise_on_error=__a							)
						self.assertEqual(results.failed						,				0							)
						self.assertGreater(results.attempted						,				1							)




	@contextmanager
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](__a							):
																yield
						else:
											yield




	@contextmanager
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						def load_local_metric(__a						,				*__a						,				**__a							):
											return load_metric(os.path.join('metrics'						,				__a							)						,				*__a						,				**__a							)

						with patch('datasets.load_metric'							) as mock_load_metric:
											__a			:       Dict					    =  load_local_metric
											yield





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						def wrapper(__a							):
											__a			:       Optional[Any]					    =  contextmanager(__a							)
											__a			:       str					    =  patcher
											return patcher

						return wrapper






@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					import tensorflow.compat.va as tf
					from bleurt.score import Predictor

					tf.flags.DEFINE_string('sv'						,      ''						,      ''   )  # handle pytest cli flags





					class 							__UpperCamelCase				(		lowerCAmelCase_					):




						def       __UpperCAmelCase							(   self						,				__a							):

											'''simple docstring'''





											assert len(input_dict['input_ids']							) == 2
											return np.array([1.03, 1.04]							)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
					with patch('bleurt.score._create_predictor'   ) as mock_create_predictor:
										__a			:       Dict					    =  MockedPredictor()
										yield






@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					import torch

					def bert_cos_score_idf(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   str						,      *_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
										return torch.tensor([[1.0, 1.0, 1.0]] * len(_SCREAMING_SNAKE_CASE   )   )

					# mock get_model which is supposed to do download a bert model
					# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
					with patch('bert_score.scorer.get_model'   ), patch(
					    'bert_score.scorer.bert_cos_score_idf'   ) as mock_bert_cos_score_idf:
										__a			:       str					    =  bert_cos_score_idf
										yield






@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					def load_from_checkpoint(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):





										class 							__UpperCamelCase				:




											def       __UpperCAmelCase							(   self						,				__a						,				*__a						,				**__a							):

																'''simple docstring'''





																assert len(__a							) == 2
																__a			:       Dict					    =  [0.19, 0.92]
																return scores, sum(__a							) / len(__a							)



										return Model()

					# mock load_from_checkpoint which is supposed to do download a bert model
					# mock load_from_checkpoint which is supposed to do download a bert model
					with patch('comet.download_model'   ) as mock_download_model:
										__a			:       str					    =  None
										with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
															__a			:       int					    =  load_from_checkpoint
															yield






def   lowerCamelCase							():
					__a			:       Optional[Any]					    =  load_metric(os.path.join('metrics'						,      'seqeval'   )   )
					__a			:       List[str]					    =  'ERROR'
					__a			:       List[str]					    =  F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
					with pytest.raises(_SCREAMING_SNAKE_CASE						,      match=re.escape(_SCREAMING_SNAKE_CASE   )   ):
										metric.compute(predictions=[]						,      references=[]						,      scheme=_SCREAMING_SNAKE_CASE   )



