
import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCamelCase__     :   Any	=							logging.getLogger(__name__)







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







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




def 							__A()		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
		if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
		else:
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	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.""" )

		_UpperCamelCase						    =	import_module("""tasks""" )
		try:
				_UpperCamelCase						    =	getattr(UpperCamelCase__						,  model_args.task_type )
				_UpperCamelCase						    =	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 if training_args.local_rank in [-1, 0] else logging.WARN						,  )
		logger.warning(
		    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""						,  training_args.local_rank						,  training_args.device						,  training_args.n_gpu						,  bool(training_args.local_rank != -1 )						,  training_args.fpaa						,  )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""						,  UpperCamelCase__ )

		# Set seed
		set_seed(training_args.seed )

		# Prepare CONLL-2003 task
		_UpperCamelCase						    =	token_classification_task.get_labels(data_args.labels )
		_UpperCamelCase						    =	dict(enumerate(UpperCamelCase__ ) )
		_UpperCamelCase						    =	len(UpperCamelCase__ )

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

		_UpperCamelCase						    =	AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,  num_labels=UpperCamelCase__						,  idalabel=UpperCamelCase__						,  labelaid={label: i for i, label in enumerate(UpperCamelCase__ )}						,  cache_dir=model_args.cache_dir						,  )
		_UpperCamelCase						    =	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						,  )
		_UpperCamelCase						    =	AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path						,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path )						,  config=UpperCamelCase__						,  cache_dir=model_args.cache_dir						,  )

		# Get datasets
		_UpperCamelCase						    =	(
		    TokenClassificationDataset(
		        token_classification_task=UpperCamelCase__						,  data_dir=data_args.data_dir						,  tokenizer=UpperCamelCase__						,  labels=UpperCamelCase__						,  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
		)
		_UpperCamelCase						    =	(
		    TokenClassificationDataset(
		        token_classification_task=UpperCamelCase__						,  data_dir=data_args.data_dir						,  tokenizer=UpperCamelCase__						,  labels=UpperCamelCase__						,  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						,  lowerCAmelCase ) -> Tuple[List[int], List[int]]:
				_UpperCamelCase						    =	np.argmax(UpperCamelCase__						,  axis=2 )

				_UpperCamelCase    ,	_UpperCamelCase						    =	preds.shape

				_UpperCamelCase						    =	[[] for _ in range(UpperCamelCase__ )]
				_UpperCamelCase						    =	[[] for _ in range(UpperCamelCase__ )]

				for i in range(UpperCamelCase__ ):
						for j in range(UpperCamelCase__ ):
								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
										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 ) -> Dict:
				_UpperCamelCase    ,	_UpperCamelCase						    =	align_predictions(p.predictions						,  p.label_ids )
				return {
				    "accuracy_score": accuracy_score(UpperCamelCase__						,  UpperCamelCase__ ),
				    "precision": precision_score(UpperCamelCase__						,  UpperCamelCase__ ),
				    "recall": recall_score(UpperCamelCase__						,  UpperCamelCase__ ),
				    "f1": fa_score(UpperCamelCase__						,  UpperCamelCase__ ),
				}

		# Data collator
		_UpperCamelCase						    =	DataCollatorWithPadding(UpperCamelCase__						,  pad_to_multiple_of=8 ) if training_args.fpaa else None

		# Initialize our Trainer
		_UpperCamelCase						    =	Trainer(
		    model=UpperCamelCase__						,  args=UpperCamelCase__						,  train_dataset=UpperCamelCase__						,  eval_dataset=UpperCamelCase__						,  compute_metrics=UpperCamelCase__						,  data_collator=UpperCamelCase__						,  )

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_process_zero():
						tokenizer.save_pretrained(training_args.output_dir )

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

				_UpperCamelCase						    =	trainer.evaluate()

				_UpperCamelCase						    =	os.path.join(training_args.output_dir						,  """eval_results.txt""" )
				if trainer.is_world_process_zero():
						with open(UpperCamelCase__						,  """w""" ) as writer:
								logger.info("""***** Eval results *****""" )
								for key, value in result.items():
										logger.info("""  %s = %s"""						,  UpperCamelCase__						,  UpperCamelCase__ )
										writer.write("""%s = %s\n""" % (key, value) )

						results.update(UpperCamelCase__ )

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

				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	trainer.predict(UpperCamelCase__ )
				_UpperCamelCase    ,	_UpperCamelCase						    =	align_predictions(UpperCamelCase__						,  UpperCamelCase__ )

				_UpperCamelCase						    =	os.path.join(training_args.output_dir						,  """test_results.txt""" )
				if trainer.is_world_process_zero():
						with open(UpperCamelCase__						,  """w""" ) as writer:
								for key, value in metrics.items():
										logger.info("""  %s = %s"""						,  UpperCamelCase__						,  UpperCamelCase__ )
										writer.write("""%s = %s\n""" % (key, value) )

        # Save predictions
				_UpperCamelCase						    =	os.path.join(training_args.output_dir						,  """test_predictions.txt""" )
				if trainer.is_world_process_zero():
						with open(UpperCamelCase__						,  """w""" ) as writer:
								with open(os.path.join(data_args.data_dir						,  """test.txt""" )						,  """r""" ) as f:
										token_classification_task.write_predictions_to_file(UpperCamelCase__						,  UpperCamelCase__						,  UpperCamelCase__ )

		return results


def 							__A(lowerCAmelCase )		->				str:

		"""simple docstring"""






		main()


if __name__ == "__main__":
		main()





from math import isclose, sqrt


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float, float]:

		"""simple docstring"""






		_UpperCamelCase						    =	point_y / 4 / point_x
		_UpperCamelCase						    =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
		_UpperCamelCase						    =	(1 - normal_gradient * normal_gradient) / (
		    1 + normal_gradient * normal_gradient
		)
		_UpperCamelCase						    =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

		# to find the next point, solve the simultaeneous equations:
		# y^2 + 4x^2 = 100
		# y - b = m * (x - a)
		# ==> A x^2 + B x + C = 0
		_UpperCamelCase						    =	outgoing_gradient**2 + 4
		_UpperCamelCase						    =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
		_UpperCamelCase						    =	(point_y - outgoing_gradient * point_x) ** 2 - 1_0_0

		_UpperCamelCase						    =	(
		    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)
		_UpperCamelCase						    =	(
		    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)

		# two solutions, one of which is our input point
		_UpperCamelCase						    =	x_minus if isclose(lowerCAmelCase						,  lowerCAmelCase ) else x_plus
		_UpperCamelCase						    =	point_y + outgoing_gradient * (next_x - point_x)

		return next_x, next_y, outgoing_gradient


def 							__A(lowerCAmelCase = 1.4						,  lowerCAmelCase = -9.6 )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	first_x_coord
		_UpperCamelCase						    =	first_y_coord
		_UpperCamelCase						    =	(10.1 - point_y) / (0.0 - point_x)

		while not (-0.01 <= point_x <= 0.01 and point_y > 0):
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	next_point(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
				num_reflections += 1

		return num_reflections


if __name__ == "__main__":
		print(F"""{solution() = }""")



