


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__       			=				logging.getLogger(__name__)







@dataclass
class     __lowerCamelCase     :


							"""simple docstring"""

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







@dataclass
class     __lowerCamelCase     :


							"""simple docstring"""

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







def SCREAMING_SNAKE_CASE_  (				):







	'''simple docstring'''



	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(__lowerCamelCase  ,			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"  ,			__lowerCamelCase  )

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

	# 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=__lowerCamelCase  ,			idalabel=__lowerCamelCase  ,			labelaid={label: i for i, label in enumerate(__lowerCamelCase  )}  ,			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=__lowerCamelCase  ,			cache_dir=model_args.cache_dir  ,			)

	# Get datasets
	lowercase_			 = (
	    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
	)
	lowercase_			 = (
	    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:		np.ndarray  ,			__lowerCamelCase:		np.ndarray  ) -> Tuple[List[int], List[int]]:
		lowercase_			 = np.argmax(__lowerCamelCase  ,			axis=2  )
		lowercase_ ,	lowercase_			 = preds.shape
		lowercase_			 = [[] for _ in range(__lowerCamelCase  )]
		lowercase_			 = [[] 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:		EvalPrediction  ) -> Dict:
		lowercase_ ,	lowercase_			 = 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
	lowercase_			 = 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
	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(__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:
		lowercase_			 = 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  ,			)

		lowercase_ ,	lowercase_ ,	lowercase_			 = trainer.predict(test_dataset.get_dataset()  )
		lowercase_ ,	lowercase_			 = align_predictions(__lowerCamelCase  ,			__lowerCamelCase  )
		lowercase_			 = classification_report(__lowerCamelCase  ,			__lowerCamelCase  )

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

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

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

		# Save predictions
		lowercase_			 = 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:
				lowercase_			 = 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]:
						lowercase_			 = 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()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       			=				{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__       			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)

