



import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowercase     							=						{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_lowercase     							=						{
    "ctrl": 2_56,
}

_lowercase     							=						{
    "Pregnancy": 16_86_29,
    "Christianity": 76_75,
    "Explain": 10_64_23,
    "Fitness": 6_34_40,
    "Saving": 6_31_63,
    "Ask": 2_71_71,
    "Ass": 9_59_85,
    "Joke": 16_35_09,
    "Questions": 4_56_22,
    "Thoughts": 4_96_05,
    "Retail": 5_23_42,
    "Feminism": 16_43_38,
    "Writing": 1_19_92,
    "Atheism": 19_22_63,
    "Netflix": 4_86_16,
    "Computing": 3_96_39,
    "Opinion": 4_32_13,
    "Alone": 4_49_67,
    "Funny": 5_89_17,
    "Gaming": 4_03_58,
    "Human": 40_88,
    "India": 13_31,
    "Joker": 7_71_38,
    "Diet": 3_62_06,
    "Legal": 1_18_59,
    "Norman": 49_39,
    "Tip": 7_26_89,
    "Weight": 5_23_43,
    "Movies": 4_62_73,
    "Running": 2_34_25,
    "Science": 20_90,
    "Horror": 3_77_93,
    "Confession": 6_05_72,
    "Finance": 1_22_50,
    "Politics": 1_63_60,
    "Scary": 19_19_85,
    "Support": 1_26_54,
    "Technologies": 3_25_16,
    "Teenage": 6_61_60,
    "Event": 3_27_69,
    "Learned": 6_74_60,
    "Notion": 18_27_70,
    "Wikipedia": 3_75_83,
    "Books": 66_65,
    "Extract": 7_60_50,
    "Confessions": 10_27_01,
    "Conspiracy": 7_59_32,
    "Links": 6_36_74,
    "Narcissus": 15_04_25,
    "Relationship": 5_47_66,
    "Relationships": 13_47_96,
    "Reviews": 4_16_71,
    "News": 42_56,
    "Translation": 2_68_20,
    "multilingual": 12_84_06,
}






def      _A				(UpperCamelCase		:      Union[str, Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		set()
			lowerCamelCase__ :      int								=		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char) )
						lowerCamelCase__ :      Optional[int]								=		char

			lowerCamelCase__ :      List[Any]								=		set(lowerCamelCase__ )
			return pairs
class 				__A					( A_      ):
			UpperCamelCase					:List[Any]     =			VOCAB_FILES_NAMES
			UpperCamelCase					:Optional[int]     =			PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase					:Optional[Any]     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase					:Tuple     =			CONTROL_CODES





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__="<unk>"							,    **__magic_name__				):
						super().__init__(unk_token=UpperCamelCase_							,    **UpperCamelCase_				)

						with open(UpperCamelCase_							,    encoding="""utf-8"""				) as vocab_handle:
									lowerCamelCase__ :      Union[str, Any]								=		json.load(UpperCamelCase_				)
						lowerCamelCase__ :      Optional[int]								=		{v: k for k, v in self.encoder.items()}
						with open(UpperCamelCase_							,    encoding="""utf-8"""				) as merges_handle:
									lowerCamelCase__ :      List[Any]								=		merges_handle.read().split("""\n"""				)[1:-1]
						lowerCamelCase__ :      Optional[int]								=		[tuple(merge.split()				) for merge in merges]
						lowerCamelCase__ :      str								=		dict(zip(UpperCamelCase_							,    range(len(UpperCamelCase_				)				)				)				)
						lowerCamelCase__ :      List[Any]								=		{}





			@property
			def        _snake_case    (self				):
						return len(self.encoder				)





			def        _snake_case    (self				):
						return dict(self.encoder							,    **self.added_tokens_encoder				)





			def        _snake_case    (self							,    __magic_name__				):
						if token in self.cache:
									return self.cache[token]
						lowerCamelCase__ :      List[Any]								=		tuple(UpperCamelCase_				)
						lowerCamelCase__ :      Union[str, Any]								=		tuple(list(word[:-1]				) + [word[-1] + """</w>"""]				)
						lowerCamelCase__ :      Tuple								=		get_pairs(UpperCamelCase_				)

						if not pairs:
									return token

						while True:
									lowerCamelCase__ :      int								=		min(UpperCamelCase_							,    key=lambda __magic_name__				: self.bpe_ranks.get(UpperCamelCase_							,    float("""inf"""				)				)				)
									if bigram not in self.bpe_ranks:
												break
									lowerCamelCase__ :      Optional[int]								=		bigram
									lowerCamelCase__ :      int								=		[]
									lowerCamelCase__ :      Optional[Any]								=		0
									while i < len(UpperCamelCase_				):
												try:
															lowerCamelCase__ :      Any								=		word.index(UpperCamelCase_							,    UpperCamelCase_				)
												except ValueError:
															new_word.extend(word[i:]				)
															break
												else:
															new_word.extend(word[i:j]				)
															lowerCamelCase__ :      Optional[Any]								=		j

												if word[i] == first and i < len(UpperCamelCase_				) - 1 and word[i + 1] == second:
															new_word.append(first + second				)
															i += 2
												else:
															new_word.append(word[i]				)
															i += 1
									lowerCamelCase__ :      Dict								=		tuple(UpperCamelCase_				)
									lowerCamelCase__ :      int								=		new_word
									if len(UpperCamelCase_				) == 1:
												break
									else:
												lowerCamelCase__ :      str								=		get_pairs(UpperCamelCase_				)
						lowerCamelCase__ :      Any								=		"@@ ".join(UpperCamelCase_				)
						lowerCamelCase__ :      Union[str, Any]								=		word[:-4]
						lowerCamelCase__ :      Optional[Any]								=		word
						return word





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      int								=		[]

						lowerCamelCase__ :      str								=		re.findall(R"""\S+\n?"""							,    UpperCamelCase_				)

						for token in words:
									split_tokens.extend(list(self.bpe(UpperCamelCase_				).split(""" """				)				)				)
						return split_tokens





			def        _snake_case    (self							,    __magic_name__				):
						return self.encoder.get(UpperCamelCase_							,    self.encoder.get(self.unk_token				)				)





			def        _snake_case    (self							,    __magic_name__				):
						return self.decoder.get(UpperCamelCase_							,    self.unk_token				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		" ".join(UpperCamelCase_				).replace("""@@ """							,    """"""				).strip()
						return out_string





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None				):
						if not os.path.isdir(UpperCamelCase_				):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
									return
						lowerCamelCase__ :      int								=		os.path.join(
						    UpperCamelCase_							,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
						lowerCamelCase__ :      List[Any]								=		os.path.join(
						    UpperCamelCase_							,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

						with open(UpperCamelCase_							,    """w"""							,    encoding="""utf-8"""				) as f:
									f.write(json.dumps(self.encoder							,    indent=2							,    sort_keys=UpperCamelCase_							,    ensure_ascii=UpperCamelCase_				) + """\n"""				)

						lowerCamelCase__ :      List[str]								=		0
						with open(UpperCamelCase_							,    """w"""							,    encoding="""utf-8"""				) as writer:
									writer.write("""#version: 0.2\n"""				)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,    key=lambda __magic_name__				: kv[1]				):
												if index != token_index:
															logger.warning(
															    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
															    """ Please check that the tokenizer is not corrupted!"""				)
															lowerCamelCase__ :      List[str]								=		token_index
												writer.write(""" """.join(UpperCamelCase_				) + """\n"""				)
												index += 1

						return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)








import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


_lowercase     							=						logging.getLogger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Optional[int]     =			'''token-classification'''





			def __init__(self							,    __magic_name__				):
						if type(__magic_name__				) == dict:
									lowerCamelCase__ :      Any								=		Namespace(**__magic_name__				)
						lowerCamelCase__ :      str								=		import_module("""tasks"""				)
						try:
									lowerCamelCase__ :      Optional[Any]								=		getattr(__magic_name__							,    hparams.task_type				)
									lowerCamelCase__ :      TokenClassificationTask								=		token_classification_task_clazz()
						except AttributeError:
									raise ValueError(
									    f"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
									    f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"				)
						lowerCamelCase__ :      Any								=		self.token_classification_task.get_labels(hparams.labels				)
						lowerCamelCase__ :      Tuple								=		CrossEntropyLoss().ignore_index
						super().__init__(__magic_name__							,    len(self.labels				)							,    self.mode				)





			def        _snake_case    (self							,    **__magic_name__				):
						return self.model(**__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Tuple								=		{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
						if self.config.model_type != "distilbert":
									lowerCamelCase__ :      Union[str, Any]								=		(
									    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
									)  # XLM and RoBERTa don"t use token_type_ids

						lowerCamelCase__ :      List[str]								=		self(**__magic_name__				)
						lowerCamelCase__ :      Dict								=		outputs[0]
						# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss}





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.hparams
						for mode in ["train", "dev", "test"]:
									lowerCamelCase__ :      List[str]								=		self._feature_file(__magic_name__				)
									if os.path.exists(__magic_name__				) and not args.overwrite_cache:
												logger.info("""Loading features from cached file %s"""							,    __magic_name__				)
												lowerCamelCase__ :      Union[str, Any]								=		torch.load(__magic_name__				)
									else:
												logger.info("""Creating features from dataset file at %s"""							,    args.data_dir				)
												lowerCamelCase__ :      int								=		self.token_classification_task.read_examples_from_file(args.data_dir							,    __magic_name__				)
												lowerCamelCase__ :      Tuple								=		self.token_classification_task.convert_examples_to_features(
												    __magic_name__							,    self.labels							,    args.max_seq_length							,    self.tokenizer							,    cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]				)							,    cls_token=self.tokenizer.cls_token							,    cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0							,    sep_token=self.tokenizer.sep_token							,    sep_token_extra=__magic_name__							,    pad_on_left=bool(self.config.model_type in ["""xlnet"""]				)							,    pad_token=self.tokenizer.pad_token_id							,    pad_token_segment_id=self.tokenizer.pad_token_type_id							,    pad_token_label_id=self.pad_token_label_id							,    )
												logger.info("""Saving features into cached file %s"""							,    __magic_name__				)
												torch.save(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = False				):
						lowerCamelCase__ :      Any								=		self._feature_file(__magic_name__				)
						logger.info("""Loading features from cached file %s"""							,    __magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		torch.load(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		torch.tensor([f.input_ids for f in features]							,    dtype=torch.long				)
						lowerCamelCase__ :      Optional[Any]								=		torch.tensor([f.attention_mask for f in features]							,    dtype=torch.long				)
						if features[0].token_type_ids is not None:
									lowerCamelCase__ :      str								=		torch.tensor([f.token_type_ids for f in features]							,    dtype=torch.long				)
						else:
									lowerCamelCase__ :      int								=		torch.tensor([0 for f in features]							,    dtype=torch.long				)
									# HACK(we will not use this anymore soon)
						lowerCamelCase__ :      Any								=		torch.tensor([f.label_ids for f in features]							,    dtype=torch.long				)
						return DataLoader(
						    TensorDataset(__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)							,    batch_size=__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						"""Compute validation""" ""
						lowerCamelCase__ :      Optional[int]								=		{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
						if self.config.model_type != "distilbert":
									lowerCamelCase__ :      Tuple								=		(
									    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
									)  # XLM and RoBERTa don"t use token_type_ids
						lowerCamelCase__ :      str								=		self(**__magic_name__				)
						lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		outputs[:2]
						lowerCamelCase__ :      List[Any]								=		logits.detach().cpu().numpy()
						lowerCamelCase__ :      Dict								=		inputs["""labels"""].detach().cpu().numpy()
						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		torch.stack([x["""val_loss"""] for x in outputs]				).mean()
						lowerCamelCase__ :      Optional[int]								=		np.concatenate([x["""pred"""] for x in outputs]							,    axis=0				)
						lowerCamelCase__ :      List[str]								=		np.argmax(__magic_name__							,    axis=2				)
						lowerCamelCase__ :      Optional[Any]								=		np.concatenate([x["""target"""] for x in outputs]							,    axis=0				)

						lowerCamelCase__ :      Optional[int]								=		dict(enumerate(self.labels				)				)
						lowerCamelCase__ :      List[str]								=		[[] for _ in range(out_label_ids.shape[0]				)]
						lowerCamelCase__ :      Any								=		[[] for _ in range(out_label_ids.shape[0]				)]

						for i in range(out_label_ids.shape[0]				):
									for j in range(out_label_ids.shape[1]				):
												if out_label_ids[i, j] != self.pad_token_label_id:
															out_label_list[i].append(label_map[out_label_ids[i][j]]				)
															preds_list[i].append(label_map[preds[i][j]]				)

						lowerCamelCase__ :      Tuple								=		{
						    """val_loss""": val_loss_mean,
						    """accuracy_score""": accuracy_score(__magic_name__							,    __magic_name__				),
						    """precision""": precision_score(__magic_name__							,    __magic_name__				),
						    """recall""": recall_score(__magic_name__							,    __magic_name__				),
						    """f1""": fa_score(__magic_name__							,    __magic_name__				),
						}

						lowerCamelCase__ :      Dict								=		dict(results.items()				)
						lowerCamelCase__ :      str								=		results
						return ret, preds_list, out_label_list





			def        _snake_case    (self							,    __magic_name__				):
						# when stable
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      str								=		self._eval_end(__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		ret["""log"""]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





			def        _snake_case    (self							,    __magic_name__				):
						# updating to test_epoch_end instead of deprecated test_end
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		self._eval_end(__magic_name__				)

						# Converting to the dict required by pl
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
						# pytorch_lightning/trainer/logging.py#L139
						lowerCamelCase__ :      List[Any]								=		ret["""log"""]
						# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
						return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





			@staticmethod
			def        _snake_case    (__magic_name__							,    __magic_name__				):
						# Add NER specific options
						BaseTransformer.add_model_specific_args(__magic_name__							,    __magic_name__				)
						parser.add_argument(
						    """--task_type"""							,    default="""NER"""							,    type=__magic_name__							,    help="""Task type to fine tune in training (e.g. NER, POS, etc)"""				)
						parser.add_argument(
						    """--max_seq_length"""							,    default=128							,    type=__magic_name__							,    help=(
						        """The maximum total input sequence length after tokenization. Sequences longer """
						        """than this will be truncated, sequences shorter will be padded."""
						    )							,    )

						parser.add_argument(
						    """--labels"""							,    default=""""""							,    type=__magic_name__							,    help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""							,    )
						parser.add_argument(
						    """--gpus"""							,    default=0							,    type=__magic_name__							,    help="""The number of GPUs allocated for this, it is by default 0 meaning none"""							,    )

						parser.add_argument(
						    """--overwrite_cache"""							,    action="""store_true"""							,    help="""Overwrite the cached training and evaluation sets"""				)

						return parser


if __name__ == "__main__":
				_lowercase     							=						argparse.ArgumentParser()
				add_generic_args(parser, os.getcwd())
				_lowercase     							=						NERTransformer.add_model_specific_args(parser, os.getcwd())
				_lowercase     							=						parser.parse_args()
				_lowercase     							=						NERTransformer(args)
				_lowercase     							=						generic_train(model, args)

				if args.do_predict:
								# See https://github.com/huggingface/transformers/issues/3159
								# pl use this default format to create a checkpoint:
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
								# /pytorch_lightning/callbacks/model_checkpoint.py#L322
								_lowercase     							=						sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
								_lowercase     							=						model.load_from_checkpoint(checkpoints[-1])
								trainer.test(model)



