





'''simple docstring'''







import heapq





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					List[Any]:
			__lowerCAmelCase											=				[(0, start)]  # cost from start node,end node
			__lowerCAmelCase											=				set()
			while heap:
						((__lowerCAmelCase)						,       (__lowerCAmelCase))											=				heapq.heappop(lowercase	)
						if u in visited:
									continue
						visited.add(lowercase	)
						if u == end:
									return cost
						for v, c in graph[u]:
									if v in visited:
												continue
									__lowerCAmelCase											=				cost + c
									heapq.heappush(lowercase					,	(next_item, v)	)
			return -1


_a					: List[str]      =    {
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
_a					: Any      =    {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
_a					: Dict      =    {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

_a					: str      =    dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_a					: Tuple      =    dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

_a					: Optional[Any]      =    dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
	import doctest

	doctest.testmod()








'''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					: str      =    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						(						)					->					List[str]:
			# 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()

