



"""simple docstring"""

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


lowercase__					:						List[str]												=			logging.getLogger(__name__)




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





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





def __lowercase    (							):
				# 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.

				snake_case_				:  List[Any]       =			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.
								snake_case_				:  Optional[Any]       =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								snake_case_				:  List[Any]       =			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.'''					)

				snake_case_				:  Dict       =			import_module('''tasks'''					)
				try:
								snake_case_				:  str       =			getattr(_a	,    model_args.task_type					)
								snake_case_				:  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'''	,    _a					)

				# Set seed
				set_seed(training_args.seed					)

				# Prepare CONLL-2003 task
				snake_case_				:  Tuple       =			token_classification_task.get_labels(data_args.labels					)
				snake_case_				:  Dict[int, str]       =			dict(enumerate(_a					)					)
				snake_case_				:  List[Any]       =			len(_a					)

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

				snake_case_				:  int       =			AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    num_labels=_a	,    idalabel=_a	,    labelaid={label: i for i, label in enumerate(_a					)}	,    cache_dir=model_args.cache_dir	,    )
				snake_case_				:  Union[str, Any]       =			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	,    )
				snake_case_				:  Optional[Any]       =			AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path	,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path					)	,    config=_a	,    cache_dir=model_args.cache_dir	,    )

				# Get datasets
				snake_case_				:  Union[str, Any]       =			(
				    TokenClassificationDataset(
				        token_classification_task=_a	,    data_dir=data_args.data_dir	,    tokenizer=_a	,    labels=_a	,    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
				)
				snake_case_				:  Any       =			(
				    TokenClassificationDataset(
				        token_classification_task=_a	,    data_dir=data_args.data_dir	,    tokenizer=_a	,    labels=_a	,    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(_a	,    _a					) -> Tuple[List[int], List[int]]:
								snake_case_				:  List[str]       =			np.argmax(_a	,    axis=2					)

								snake_case_				:  Any       =			preds.shape

								snake_case_				:  List[Any]       =			[[] for _ in range(_a					)]
								snake_case_				:  Union[str, Any]       =			[[] for _ in range(_a					)]

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

				# Data collator
				snake_case_				:  Dict       =			DataCollatorWithPadding(_a	,    pad_to_multiple_of=8					) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case_				:  str       =			Trainer(
				    model=_a	,    args=_a	,    train_dataset=_a	,    eval_dataset=_a	,    compute_metrics=_a	,    data_collator=_a	,    )

				# 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
				snake_case_				:  Optional[Any]       =			{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''					)

								snake_case_				:  Tuple       =			trainer.evaluate()

								snake_case_				:  List[Any]       =			os.path.join(training_args.output_dir	,    '''eval_results.txt'''					)
								if trainer.is_world_process_zero():
												with open(_a	,    '''w'''					) as writer:
																logger.info('''***** Eval results *****'''					)
																for key, value in result.items():
																				logger.info('''  %s = %s'''	,    _a	,    _a					)
																				writer.write('''%s = %s\n''' % (key, value)					)

												results.update(_a					)

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

								snake_case_				:  str       =			trainer.predict(_a					)
								snake_case_				:  Optional[int]       =			align_predictions(_a	,    _a					)

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

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

				return results




def __lowercase    (							_a					):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





