
'''simple docstring'''



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class      A							(    unittest.TestCase			):



			@slow
			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    CamembertModel.from_pretrained("""camembert-base"""    )
									model.to(lowerCamelCase__    )

									lowercase__								=    torch.tensor(
									    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]    ,       device=lowerCamelCase__    ,       dtype=torch.long    ,       )  # J'aime le camembert !
									with torch.no_grad():
															lowercase__								=    model(lowerCamelCase__    )["""last_hidden_state"""]
									lowercase__								=    torch.Size((1, 10, 768)    )
									self.assertEqual(output.shape    ,       lowerCamelCase__    )
									# compare the actual values for a slice.
									lowercase__								=    torch.tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]    ,       device=lowerCamelCase__    ,       dtype=torch.float    ,       )
									# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
									# camembert.eval()
									# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

									self.assertTrue(torch.allclose(output[:, :3, :3]    ,       lowerCamelCase__    ,       atol=1e-4    )    )








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





@dataclass
class      A							:
			lowerCamelCase							:							str    						=					field(
			    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
			lowerCamelCase							:							Optional[str]    						=					field(
			    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}			)
			lowerCamelCase							:							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."""
			        )
			    }						,  )
			lowerCamelCase							:							bool    						=					field(
			    default=__UpperCAmelCase						,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)





def 	_A						(							):
						# 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.
						lowercase__								=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
						lowercase__	,  lowercase__	,  lowercase__								=    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."""		)

						lowercase__								=    import_module("""tasks"""		)

						try:
												lowercase__								=    getattr(lowercase__						,						model_args.task_type		)
												lowercase__								=    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"""						,						lowercase__		)

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

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

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

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

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

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

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

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

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

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

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

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

																		results.update(lowercase__		)

    # Predict
						if training_args.do_predict:
												lowercase__								=    TFTokenClassificationDataset(
												    token_classification_task=lowercase__						,						data_dir=data_args.data_dir						,						tokenizer=lowercase__						,						labels=lowercase__						,						model_type=config.model_type						,						max_seq_length=data_args.max_seq_length						,						overwrite_cache=data_args.overwrite_cache						,						mode=Split.test						,						)

												lowercase__	,  lowercase__	,  lowercase__								=    trainer.predict(test_dataset.get_dataset()		)
												lowercase__	,  lowercase__								=    align_predictions(lowercase__						,						lowercase__		)
												lowercase__								=    classification_report(lowercase__						,						lowercase__		)

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

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

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

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

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

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

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

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

						return results


if __name__ == "__main__":
				main()






