'''simple docstring'''




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()


_UpperCamelCase				:		List[Any]       			=		logging.getLogger(__name__)

@dataclass
class   _lowercase:



			"""simple docstring"""





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




@dataclass
class   _lowercase:



			"""simple docstring"""





			__lowerCamelCase    =  field(
			    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}				)
			__lowerCamelCase    =  field(
			    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}				)
			__lowerCamelCase    =  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    =  field(
			    default=_lowerCamelCase   ,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.
	__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       :     np.ndarray      ,  lowerCAmelCase       :     np.ndarray			) -> 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       :     EvalPrediction			) -> 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()




'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Tuple       			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase				:		int       			=		{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase				:		List[str]       			=		{
    'allenai/longformer-base-4096': 40_96,
    'allenai/longformer-large-4096': 40_96,
    'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
    'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
    'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
   __lowerCamelCase    =  LongformerTokenizer





   def __init__(							self:    Any			,a:    Optional[int]=None			,a:    Optional[int]=None			,a:    int=None			,a:    Any="replace"			,a:    Optional[int]="<s>"			,a:    Any="</s>"			,a:    Optional[int]="</s>"			,a:    Dict="<s>"			,a:    Dict="<unk>"			,a:    int="<pad>"			,a:    List[str]="<mask>"			,a:    Union[str, Any]=False			,a:    Any=True			,**a:    str			,):
    super().__init__(
        a			,a			,tokenizer_file=a			,errors=a			,bos_token=a			,eos_token=a			,sep_token=a			,cls_token=a			,unk_token=a			,pad_token=a			,mask_token=a			,add_prefix_space=a			,trim_offsets=a			,**a			,)

    __UpperCAmelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
    if pre_tok_state.get('add_prefix_space'			,a	) != add_prefix_space:
     __UpperCAmelCase         =					getattr(a			,pre_tok_state.pop('type'	)	)
     __UpperCAmelCase         =					add_prefix_space
     __UpperCAmelCase         =					pre_tok_class(**a	)

    __UpperCAmelCase         =					add_prefix_space

    __UpperCAmelCase         =					'post_processor'
    __UpperCAmelCase         =					getattr(self.backend_tokenizer			,a			,a	)
    if tokenizer_component_instance:
     __UpperCAmelCase         =					json.loads(tokenizer_component_instance.__getstate__()	)

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
      __UpperCAmelCase         =					tuple(state['sep']	)
     if "cls" in state:
      __UpperCAmelCase         =					tuple(state['cls']	)

     __UpperCAmelCase         =					False

     if state.get('add_prefix_space'			,a	) != add_prefix_space:
      __UpperCAmelCase         =					add_prefix_space
      __UpperCAmelCase         =					True

     if state.get('trim_offsets'			,a	) != trim_offsets:
      __UpperCAmelCase         =					trim_offsets
      __UpperCAmelCase         =					True

     if changes_to_apply:
      __UpperCAmelCase         =					getattr(a			,state.pop('type'	)	)
      __UpperCAmelCase         =					component_class(**a	)
      setattr(self.backend_tokenizer			,a			,a	)





   @property
   def 			snake_case	(							self:    List[Any]	):
    if self._mask_token is None:
     if self.verbose:
      logger.error('Using mask_token, but it is not set yet.'	)
     return None
    return str(self._mask_token	)





   @mask_token.setter
   def 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					AddedToken(a			,lstrip=a			,rstrip=a	) if isinstance(a			,a	) else value
    __UpperCAmelCase         =					value





   def 			snake_case	(							self:    Optional[int]			,*a:    Dict			,**a:    Dict	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)
    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._batch_encode_plus(*a			,**a	)





   def 			snake_case	(							self:    str			,*a:    Dict			,**a:    str	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)

    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._encode_plus(*a			,**a	)





   def 			snake_case	(							self:    List[str]			,a:    str			,a:    Optional[str] = None	):
    __UpperCAmelCase         =					self._tokenizer.model.save(a			,name=a	)
    return tuple(a	)





   def 			snake_case	(							self:    Tuple			,a:    Dict			,a:    Tuple=None	):
    __UpperCAmelCase         =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
     return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 			snake_case	(							self:    int			,a:    List[int]			,a:    Optional[List[int]] = None	):
    __UpperCAmelCase         =					[self.sep_token_id]
    __UpperCAmelCase         =					[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]



