






'''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()


__lowerCAmelCase							=logging.getLogger(__name__)


@dataclass
class    _snake_case   :

					"""simple docstring"""





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



@dataclass
class    _snake_case   :

					"""simple docstring"""





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



def 							a	(   )				->				Tuple:






					"""simple docstring"""







					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] != -1_0_0:
																									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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=7   , UpperCAmelCase__=3   , UpperCAmelCase__=18   , UpperCAmelCase__=30   , UpperCAmelCase__=400   , UpperCAmelCase__=True   , UpperCAmelCase__=None   , UpperCAmelCase__=0.8_7_5   , UpperCAmelCase__=True   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , )      ->	Optional[int]:
										a_									=   size if size is not None else {'shortest_edge': 20}
										a_									=   parent
										a_									=   batch_size
										a_									=   num_channels
										a_									=   image_size
										a_									=   min_resolution
										a_									=   max_resolution
										a_									=   do_resize
										a_									=   size
										a_									=   crop_pct
										a_									=   do_normalize
										a_									=   image_mean
										a_									=   image_std




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}



@require_torch
@require_vision
class    _snake_case   (				snake_case     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      ConvNextImageProcessor if is_vision_available() else None




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   ConvNextImageProcessingTester(self     )




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_resize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'size'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'crop_pct'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_normalize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_mean'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_std'     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 20}     )

										a_									=   self.image_processing_class.from_dict(self.image_processor_dict   , size=42     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 42}     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , Image.Image     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , numpify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , np.ndarray     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , torchify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , torch.Tensor     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

