

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


SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

@dataclass
class 			a					:
						_lowercase        =     field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	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=UpperCAmelCase							,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
						_lowercase        =     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.
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,	)







@dataclass
class 			a					:
						_lowercase        =     field(
						    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}			)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							,	)
						_lowercase        =     field(
						    default=1_2_8							,	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=UpperCAmelCase							,	metadata={"help": "Overwrite the cached training and evaluation sets"}			)







def 							__SCREAMING_SNAKE_CASE							(			)				->    List[Any]:
				# 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.

				_UpperCAmelCase    :						Optional[int]							=      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    :						Tuple							=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
				else:
								_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						List[str]							=      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    :						str							=      import_module("tasks"	)
				try:
								_UpperCAmelCase    :						Optional[int]							=      getattr(lowerCAmelCase					, model_args.task_type	)
								_UpperCAmelCase    :						TokenClassificationTask							=      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"					, lowerCAmelCase	)

				# Set seed
				set_seed(training_args.seed	)

				# Prepare CONLL-2003 task
				_UpperCAmelCase    :						Optional[int]							=      token_classification_task.get_labels(data_args.labels	)
				_UpperCAmelCase    :						Dict[int, str]							=      dict(enumerate(lowerCAmelCase	)	)
				_UpperCAmelCase    :						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.

				_UpperCAmelCase    :						List[str]							=      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					, )
				_UpperCAmelCase    :						Optional[int]							=      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    :						List[str]							=      AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path					, from_tf=bool(".ckpt" in model_args.model_name_or_path	)					, config=lowerCAmelCase					, cache_dir=model_args.cache_dir					, )

				# Get datasets
				_UpperCAmelCase    :						str							=      (
				    TokenClassificationDataset(
				        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
				)
				_UpperCAmelCase    :						Tuple							=      (
				    TokenClassificationDataset(
				        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]]:
								_UpperCAmelCase    :						List[str]							=      np.argmax(lowerCAmelCase					, axis=2	)

								_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      preds.shape

								_UpperCAmelCase    :						Optional[Any]							=      [[] for _ in range(lowerCAmelCase	)]
								_UpperCAmelCase    :						Optional[Any]							=      [[] for _ in range(lowerCAmelCase	)]

								for i in range(lowerCAmelCase	):
												for j in range(lowerCAmelCase	):
																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:					EvalPrediction	) -> Dict:
								_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      align_predictions(p.predictions					, p.label_ids	)
								return {
								    "accuracy_score": accuracy_score(lowerCAmelCase					, lowerCAmelCase	),
								    "precision": precision_score(lowerCAmelCase					, lowerCAmelCase	),
								    "recall": recall_score(lowerCAmelCase					, lowerCAmelCase	),
								    "f1": fa_score(lowerCAmelCase					, lowerCAmelCase	),
								}

				# Data collator
				_UpperCAmelCase    :						int							=      DataCollatorWithPadding(lowerCAmelCase					, pad_to_multiple_of=8	) if training_args.fpaa else None

				# Initialize our Trainer
				_UpperCAmelCase    :						Optional[int]							=      Trainer(
				    model=lowerCAmelCase					, args=lowerCAmelCase					, train_dataset=lowerCAmelCase					, eval_dataset=lowerCAmelCase					, compute_metrics=lowerCAmelCase					, data_collator=lowerCAmelCase					, )

				# 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    :						List[Any]							=      {}
				if training_args.do_eval:
								logger.info("*** Evaluate ***"	)

								_UpperCAmelCase    :						List[Any]							=      trainer.evaluate()

								_UpperCAmelCase    :						str							=      os.path.join(training_args.output_dir					, "eval_results.txt"	)
								if trainer.is_world_process_zero():
												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:
								_UpperCAmelCase    :						Union[str, Any]							=      TokenClassificationDataset(
								    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					, )

								_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      trainer.predict(lowerCAmelCase	)
								_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      align_predictions(lowerCAmelCase					, lowerCAmelCase	)

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

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

				return results

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    List[Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


