"""simple docstring"""


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()


a							=logging.getLogger(__name__)






@dataclass
class    __UpperCAmelCase						:
				A__      :   str  							=       field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
				A__      :   Optional[str]  							=       field(
				    default=__lowerCAmelCase      ,      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=__lowerCAmelCase      ,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
				A__      :   bool  							=       field(default=__lowerCAmelCase      ,      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=__lowerCAmelCase      ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      ,      )






@dataclass
class    __UpperCAmelCase						:
				A__      :   str  							=       field(
				    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
				A__      :   Optional[str]  							=       field(
				    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=__lowerCAmelCase      ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)





def 			lowerCamelCase_ (					)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=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__					=import_module("tasks"				)

						try:
												lowerCamelCase__					=getattr(__lowerCAmelCase   ,    model_args.task_type				)
												lowerCamelCase__					=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"   ,    __lowerCAmelCase				)

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

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

						lowerCamelCase__					=AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,    num_labels=__lowerCAmelCase   ,    idalabel=__lowerCAmelCase   ,    labelaid={label: i for i, label in enumerate(__lowerCAmelCase				)}   ,    cache_dir=model_args.cache_dir   ,    )
						lowerCamelCase__					=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():
												lowerCamelCase__					=TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path   ,    from_pt=bool(".bin" in model_args.model_name_or_path				)   ,    config=__lowerCAmelCase   ,    cache_dir=model_args.cache_dir   ,    )

						# Get datasets
						lowerCamelCase__					=(
						    TFTokenClassificationDataset(
						        token_classification_task=__lowerCAmelCase   ,    data_dir=data_args.data_dir   ,    tokenizer=__lowerCAmelCase   ,    labels=__lowerCAmelCase   ,    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__					=(
						    TFTokenClassificationDataset(
						        token_classification_task=__lowerCAmelCase   ,    data_dir=data_args.data_dir   ,    tokenizer=__lowerCAmelCase   ,    labels=__lowerCAmelCase   ,    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(__lowerCAmelCase   ,    __lowerCAmelCase				) -> Tuple[List[int], List[int]]:
												lowerCamelCase__					=np.argmax(__lowerCAmelCase   ,    axis=2				)
												lowerCamelCase__					,      lowerCamelCase__					=preds.shape
												lowerCamelCase__					=[[] for _ in range(__lowerCAmelCase				)]
												lowerCamelCase__					=[[] for _ in range(__lowerCAmelCase				)]

												for i in range(__lowerCAmelCase				):
																		for j in range(__lowerCAmelCase				):
																								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(__lowerCAmelCase				) -> Dict:
												lowerCamelCase__					,      lowerCamelCase__					=align_predictions(p.predictions   ,    p.label_ids				)

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

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

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir				)

						# Evaluation
						lowerCamelCase__					={}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"				)

												lowerCamelCase__					=trainer.evaluate()
												lowerCamelCase__					=os.path.join(training_args.output_dir   ,    "eval_results.txt"				)

												with open(__lowerCAmelCase   ,    "w"				) as writer:
																		logger.info("***** Eval results *****"				)

																		for key, value in result.items():
																								logger.info("  %s = %s"   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)
																								writer.write("%s = %s\n" % (key, value)				)

																		results.update(__lowerCAmelCase				)

    # Predict
						if training_args.do_predict:
												lowerCamelCase__					=TFTokenClassificationDataset(
												    token_classification_task=__lowerCAmelCase   ,    data_dir=data_args.data_dir   ,    tokenizer=__lowerCAmelCase   ,    labels=__lowerCAmelCase   ,    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__					=trainer.predict(test_dataset.get_dataset()				)
												lowerCamelCase__					,      lowerCamelCase__					=align_predictions(__lowerCAmelCase   ,    __lowerCAmelCase				)
												lowerCamelCase__					=classification_report(__lowerCAmelCase   ,    __lowerCAmelCase				)

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

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

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

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

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

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

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

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

						return results


if __name__ == "__main__":
	main()



"""simple docstring"""


def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


