





'''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      :				Union[str, Any]											=					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_28			,      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		(		)      ->      int:
			# 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.
			__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(lowercase      ,			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"""      ,			lowercase			)

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

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

			# Get datasets
			__lowerCAmelCase      		=       (
			    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
			)
			__lowerCAmelCase      		=       (
			    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]]:
						__lowerCAmelCase      		=       np.argmax(lowercase      ,			axis=2			)
						__lowerCAmelCase		, __lowerCAmelCase      		=       preds.shape
						__lowerCAmelCase      		=       [[] for _ in range(lowercase			)]
						__lowerCAmelCase      		=       [[] 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:
						__lowerCAmelCase		, __lowerCAmelCase      		=       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
			__lowerCAmelCase      		=       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
			__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(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:
						__lowerCAmelCase      		=       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      ,			)

						__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       trainer.predict(test_dataset.get_dataset()			)
						__lowerCAmelCase		, __lowerCAmelCase      		=       align_predictions(lowercase      ,			lowercase			)
						__lowerCAmelCase      		=       classification_report(lowercase      ,			lowercase			)

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

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

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

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








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_a      :				Dict											=					TypeVar("""T""", bound=bool)





class 					_UpperCAmelCase							(		Generic[T]     ):
		def __init__(						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       value
					__lowerCAmelCase      		=       None
					__lowerCAmelCase      		=       None
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return self._value







		@staticmethod
		def 						lowerCamelCase__    (						__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					if not roota:
								return roota

					if not roota:
								return roota

					if roota.value > roota.value:
								__lowerCAmelCase		, __lowerCAmelCase      		=       roota, roota

					__lowerCAmelCase      		=       roota
					__lowerCAmelCase      		=       roota.right
					__lowerCAmelCase      		=       roota.left
					__lowerCAmelCase      		=       SkewNode.merge(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )

					return result





class 					_UpperCAmelCase							(		Generic[T]     ):
		def __init__(						self,__SCREAMING_SNAKE_CASE = ()    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       None
					if data:
								for item in data:
											self.insert(__SCREAMING_SNAKE_CASE    )
		def __bool__(						self    ):



					'''simple docstring'''

					return self._root is not None
		def __iter__(						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       []
					while self:
								result.append(self.pop()    )

					# Pushing items back to the heap not to clear it.
					for item in result:
								self.insert(__SCREAMING_SNAKE_CASE    )

					return iter(__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       SkewNode.merge(self._root,SkewNode(__SCREAMING_SNAKE_CASE    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       self.top()
					__lowerCAmelCase      		=       (
					    SkewNode.merge(self._root.left,self._root.right    ) if self._root else None
					)

					return result
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""    )
					return self._root.value







		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       None


if __name__ == "__main__":
	import doctest

	doctest.testmod()

