
'''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()


__SCREAMING_SNAKE_CASE		:  List[str]        =      logging.getLogger(__name__)
@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
	lowercase__	    =							field(
	    default="NER"			,					metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}   )
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
	lowercase__	    =							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.
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,					)







@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							field(
	    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}   )
	lowercase__	    =							field(
	    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}   )
	lowercase__	    =							field(
	    default=1_28			,					metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }			,					)
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={"help": "Overwrite the cached training and evaluation sets"}   )







def 				_snake_case (		)       ->       Optional[Any]:
				__a       :     str								=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
				__a							,	__a							,	__a       :     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."""						)

				__a       :     str								=	import_module("""tasks"""						)

				try:
								__a       :     int								=	getattr(__lowerCamelCase			,    model_args.task_type						)
								__a       :     Tuple								=	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
				__a       :     Union[str, Any]								=	token_classification_task.get_labels(data_args.labels						)
				__a       :     Union[str, Any]								=	dict(enumerate(__lowerCamelCase						)						)
				__a       :     Optional[int]								=	len(__lowerCamelCase						)

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

				__a       :     int								=	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			,    )
				__a       :     List[Any]								=	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       :     List[str]								=	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
				__a       :     Dict								=	(
				    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
				)
				__a       :     List[str]								=	(
				    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(lowercase			,    lowercase						) -> Tuple[List[int], List[int]]:
								__a       :     Optional[int]								=	np.argmax(__lowerCamelCase			,    axis=2						)
								__a							,	__a       :     List[Any]								=	preds.shape
								__a       :     Tuple								=	[[] for _ in range(__lowerCamelCase						)]
								__a       :     Optional[int]								=	[[] for _ in range(__lowerCamelCase						)]

								for i in range(__lowerCamelCase						):
												for j in range(__lowerCamelCase						):
																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(lowercase						) -> Dict:
								__a							,	__a       :     int								=	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
				__a       :     List[str]								=	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
				__a       :     Optional[int]								=	{}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""						)

								__a       :     Tuple								=	trainer.evaluate()
								__a       :     Dict								=	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:
								__a       :     List[str]								=	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			,    )

								__a							,	__a							,	__a       :     Optional[int]								=	trainer.predict(test_dataset.get_dataset()						)
								__a							,	__a       :     str								=	align_predictions(__lowerCamelCase			,    __lowerCamelCase						)
								__a       :     Any								=	classification_report(__lowerCamelCase			,    __lowerCamelCase						)

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

								__a       :     Tuple								=	os.path.join(training_args.output_dir			,    """test_results.txt"""						)

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

								# Save predictions
								__a       :     Union[str, Any]								=	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:
																__a       :     int								=	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]:
																								__a       :     List[Any]								=	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"""

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class     A_				(   unittest.TestCase	):
				UpperCAmelCase__										=					EsmTokenizer







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										super().setUp()
										__magic_name__      =  tempfile.mkdtemp()
										# fmt: off
										__magic_name__      =  ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
										# fmt: on
										__magic_name__      =  os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES["vocab_file"]						)
										with open(self.vocab_file    ,     "w"    ,     encoding="utf-8"						) as vocab_writer:
																vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)







				def      _snake_case       (				self				:    Optional[Any]    ,     **__lowerCamelCase				:    str						)					->       List[PreTrainedTokenizerBase]:
										return [self.get_tokenizer(**__lowerCamelCase						)]







				def      _snake_case       (				self				:    Dict    ,     **__lowerCamelCase				:    int						)					->       PreTrainedTokenizer:
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       Optional[int]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  tokenizer.tokenize("LAGVS"						)
										self.assertListEqual(__lowerCamelCase    ,     ["L", "A", "G", "V", "S"]						)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase						)    ,     [4, 5, 6, 7, 8]						)







				def      _snake_case       (				self				:    int						)					->       Dict:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  "LAGVS"
										self.assertListEqual(tokenizer.encode(__lowerCamelCase						)    ,     [0, 4, 5, 6, 7, 8, 2]						)







				def      _snake_case       (				self				:    List[Any]						)					->       Tuple:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[str]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.get_tokenizers(fast=__lowerCamelCase						)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																						__magic_name__      =  "<unk>"
																						__magic_name__      =  "<mask>"

																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)
																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)

																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)



