
"""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				 =					logging.getLogger(__name__)







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     str   =     field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
						_lowerCamelCase:     Optional[str]   =     field(
						    default=_lowercase		,       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=_lowercase		,       metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       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=_lowercase		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_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=_lowercase		,       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		,       )
						_lowerCamelCase:     int   =     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.'''
						        )
						    }		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )






def    _snake_case					(		):
	# 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.

	A     = 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.
		A ,   A ,   A     = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
	else:
		A ,   A ,   A     = 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.'  )

	A     = import_module('tasks'  )
	try:
		A     = getattr(snake_case__						,   model_args.task_type  )
		A     = 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'						,   snake_case__  )

	# Set seed
	set_seed(training_args.seed  )

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

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

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

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

		A ,   A     = preds.shape

		A     = [[] for _ in range(snake_case__  )]
		A     = [[] for _ in range(snake_case__  )]

		for i in range(snake_case__  ):
			for j in range(snake_case__  ):
				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(snake_case__ :  EvalPrediction  ) -> Dict:
		A ,   A     = align_predictions(p.predictions						,   p.label_ids  )
		return {
		    "accuracy_score": accuracy_score(snake_case__						,   snake_case__  ),
		    "precision": precision_score(snake_case__						,   snake_case__  ),
		    "recall": recall_score(snake_case__						,   snake_case__  ),
		    "f1": fa_score(snake_case__						,   snake_case__  ),
		}

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

	# Initialize our Trainer
	A     = Trainer(
	    model=snake_case__						,   args=snake_case__						,   train_dataset=snake_case__						,   eval_dataset=snake_case__						,   compute_metrics=snake_case__						,   data_collator=snake_case__						,   )

	# 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
	A     = {}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'  )

		A     = trainer.evaluate()

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

			results.update(snake_case__  )

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

		A ,   A ,   A     = trainer.predict(snake_case__  )
		A ,   A     = align_predictions(snake_case__						,   snake_case__  )

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

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

	return results






def    _snake_case					(		snake_case__ :  Any  ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()

"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowercase				 =					{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     List[str]   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Union[str, Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Any   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   Union[str, Any]	,A_  :   str	,A_  :   str="<s>"	,A_  :   Any="</s>"	,A_  :   Tuple="</s>"	,A_  :   Any="<s>"	,A_  :   Optional[Any]="<unk>"	,A_  :   int="<pad>"	,A_  :   str="<mask>"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   Optional[int]	,)  -> None:
							# Mask token behave like a normal word, i.e. include the space before it
							A     = AddedToken(A_	,lstrip=A_	,rstrip=A_   ) if isinstance(A_	,A_   ) else mask_token

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_	,eos_token=A_	,unk_token=A_	,sep_token=A_	,cls_token=A_	,pad_token=A_	,mask_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							A     = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							A     = 1

							A     = len(self.sp_model   ) + self.fairseq_offset
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(			self  :   Union[str, Any]   )  -> Any:
							A     = self.__dict__.copy()
							A     = None
							A     = self.sp_model.serialized_model_proto()
							return state


						def __setstate__(			self  :   str	,A_  :   str   )  -> Optional[Any]:
							A     = d

							# for backward compatibility
							if not hasattr(self	,'sp_model_kwargs'   ):
								A     = {}

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A     = [self.cls_token_id]
							A     = [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

							if token_ids_a is None:
								return [1] + ([0] * len(A_   )) + [1]
							return [1] + ([0] * len(A_   )) + [1, 1] + ([0] * len(A_   )) + [1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]
							A     = [self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> List[str]:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> int:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Optional[Any]   )  -> List[Any]:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = os.path.join(
							    A_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)