




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				     =  logging.getLogger(__name__)





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






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





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






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



def __A   (				)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase     =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
						_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(lowercase__			,		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			,		)
						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
						_UpperCAmelCase     =      token_classification_task.get_labels(data_args.labels     )
						_UpperCAmelCase     =      dict(enumerate(lowercase__     )     )
						_UpperCAmelCase     =      len(lowercase__     )

						# 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=lowercase__			,		idalabel=lowercase__			,		labelaid={label: i for i, label in enumerate(lowercase__     )}			,		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			,		)

						with training_args.strategy.scope():
												_UpperCAmelCase     =      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
						_UpperCAmelCase     =      (
						    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
						)
						_UpperCAmelCase     =      (
						    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(__lowerCAmelCase			,		__lowerCAmelCase     ) -> Tuple[List[int], List[int]]:
												_UpperCAmelCase     =      np.argmax(lowercase__			,		axis=2     )
												_UpperCAmelCase ,			_UpperCAmelCase     =      preds.shape
												_UpperCAmelCase     =      [[] for _ in range(lowercase__     )]
												_UpperCAmelCase     =      [[] 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(__lowerCAmelCase     ) -> Dict:
												_UpperCAmelCase ,			_UpperCAmelCase     =      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
						_UpperCAmelCase     =      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
						_UpperCAmelCase     =      {}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'     )

												_UpperCAmelCase     =      trainer.evaluate()
												_UpperCAmelCase     =      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:
												_UpperCAmelCase     =      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			,		)

												_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      trainer.predict(test_dataset.get_dataset()     )
												_UpperCAmelCase ,			_UpperCAmelCase     =      align_predictions(lowercase__			,		lowercase__     )
												_UpperCAmelCase     =      classification_report(lowercase__			,		lowercase__     )

												logger.info('\n%s'			,		lowercase__     )

												_UpperCAmelCase     =      os.path.join(training_args.output_dir			,		'test_results.txt'     )

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

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








from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						if not inplace:
												_UpperCAmelCase     =      ma + ma
						else:
												ma += ma

						return ma


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      -1 * len(__lowerCAmelCase     )
						_UpperCAmelCase     =      list(range(len(tensor.shape[:zero_index]     )     )     )
						return tensor.permute(first_inds + [zero_index + i for i in inds]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						return t.reshape(t.shape[:-no_dims] + (-1,)     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 1E-4     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      mask.expand(*value.shape     )
						return torch.sum(mask * value			,		dim=__lowerCAmelCase     ) / (eps + torch.sum(__lowerCAmelCase			,		dim=__lowerCAmelCase     ))


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase = 2.31_25			,		__lowerCAmelCase = 21.68_75			,		__lowerCAmelCase = 64     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.linspace(__lowerCAmelCase			,		__lowerCAmelCase			,		no_bins - 1			,		device=pts.device     )
						_UpperCAmelCase     =      torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2			,		dim=-1     )     )
						return torch.bucketize(__lowerCAmelCase			,		__lowerCAmelCase     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						_UpperCAmelCase     =      dicts[0]
						_UpperCAmelCase     =      {}
						for k, v in first.items():
												_UpperCAmelCase     =      [d[k] for d in dicts]
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_multimap(__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      fn(__lowerCAmelCase     )

						return new_dict


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      v_bins.view(((1,) * len(x.shape     )) + (len(__lowerCAmelCase     ),)     )
						_UpperCAmelCase     =      x[..., None] - reshaped_bins
						_UpperCAmelCase     =      torch.argmin(torch.abs(__lowerCAmelCase     )			,		dim=-1     )
						return nn.functional.one_hot(__lowerCAmelCase			,		num_classes=len(__lowerCAmelCase     )     ).float()


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 0			,		__lowerCAmelCase = 0     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      []
						for i, s in enumerate(data.shape[:no_batch_dims]     ):
												_UpperCAmelCase     =      torch.arange(__lowerCAmelCase     )
												_UpperCAmelCase     =      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
												ranges.append(__lowerCAmelCase     )

						_UpperCAmelCase     =      [slice(__lowerCAmelCase     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
						_UpperCAmelCase     =      inds
						ranges.extend(__lowerCAmelCase     )
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(__lowerCAmelCase     )]


_a				     =  TypeVar('''T''')


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Dict[Any, Union[dict, list, tuple, Any]]:





						"""simple docstring"""



						_UpperCAmelCase     =      {}
						for k, v in dic.items():
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						return new_dict


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  list:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  tuple:





						"""simple docstring"""



						...


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return [tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree]
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return tuple(tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return fn(__lowerCAmelCase     )
						else:
												print(type(__lowerCAmelCase     )     )
												raise ValueError('Not supported'     )

_a				     =  partial(tree_map, leaf_type=torch.Tensor)
