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


UpperCAmelCase_			    =							logging.getLogger(__name__)




@dataclass
class  __magic_name__  :





   """simple docstring"""




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




@dataclass
class  __magic_name__  :





   """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=__a      ,	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=__a      ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )




def       lowerCAmelCase_     (		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= 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.
  _UpperCamelCase:     str							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  _UpperCamelCase:     Union[str, 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.'''		)

 _UpperCamelCase:     str							= import_module('''tasks'''		)
 try:
  _UpperCamelCase:     Optional[Any]							= getattr(lowercase  ,					model_args.task_type		)
  _UpperCamelCase:     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'''  ,					lowercase		)

 # Set seed
 set_seed(training_args.seed		)

 # Prepare CONLL-2003 task
 _UpperCamelCase:     Dict							= token_classification_task.get_labels(data_args.labels		)
 _UpperCamelCase:     Dict[int, str]							= dict(enumerate(lowercase		)		)
 _UpperCamelCase:     Tuple							= 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:     List[Any]							= 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:     Optional[int]							= 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  ,					)
 _UpperCamelCase:     str							= AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path  ,					from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)  ,					config=lowercase  ,					cache_dir=model_args.cache_dir  ,					)

 # Get datasets
 _UpperCamelCase:     str							= (
     TokenClassificationDataset(
         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:     Any							= (
     TokenClassificationDataset(
         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(lowercase:							np.ndarray  ,					lowercase:							np.ndarray		) -> Tuple[List[int], List[int]]:
  _UpperCamelCase:     Union[str, Any]							= np.argmax(lowercase  ,					axis=2		)

  _UpperCamelCase:     int							= preds.shape

  _UpperCamelCase:     Union[str, Any]							= [[] for _ in range(lowercase		)]
  _UpperCamelCase:     Dict							= [[] for _ in range(lowercase		)]

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

 # Data collator
 _UpperCamelCase:     Optional[Any]							= DataCollatorWithPadding(lowercase  ,					pad_to_multiple_of=8		) if training_args.fpaa else None

 # Initialize our Trainer
 _UpperCamelCase:     List[Any]							= Trainer(
     model=lowercase  ,					args=lowercase  ,					train_dataset=lowercase  ,					eval_dataset=lowercase  ,					compute_metrics=lowercase  ,					data_collator=lowercase  ,					)

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

  _UpperCamelCase:     Optional[Any]							= trainer.evaluate()

  _UpperCamelCase:     Union[str, Any]							= os.path.join(training_args.output_dir  ,					'''eval_results.txt'''		)
  if trainer.is_world_process_zero():
   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:     List[str]							= TokenClassificationDataset(
      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:     Union[str, Any]							= trainer.predict(lowercase		)
  _UpperCamelCase:     Optional[int]							= align_predictions(lowercase  ,					lowercase		)

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

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

 return results




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				Tuple:







 '''simple docstring'''
 main()


if __name__ == "__main__":
       main()
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCAmelCase_			    =							[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							Optional[Any]=None  ,					lowercase:							List[str]=None  ,					lowercase:							List[str]=None		)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     int							= True
 while ask_again:
  _UpperCamelCase:     Any							= input(lowercase		)
  try:
   if default is not None and len(lowercase		) == 0:
    return default
   return convert_value(lowercase		) if convert_value is not None else result
  except Exception:
   if error_message is not None:
    print(lowercase		)




def       lowerCAmelCase_     (		lowercase:							List[Any]  ,					lowercase:							Union[str, Any]=[]  ,					lowercase:							Union[str, Any]=None  ,					lowercase:							Union[str, Any]=0		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= BulletMenu(lowercase  ,					lowercase		)
 _UpperCamelCase:     List[Any]							= menu.run(default_choice=lowercase		)
 return convert_value(lowercase		) if convert_value is not None else result




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     int							= int(lowercase		)
 return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)




def       lowerCAmelCase_     (		lowercase:							Tuple		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     str							= int(lowercase		)
 return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)




def       lowerCAmelCase_     (		lowercase:							Union[str, Any]		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= int(lowercase		)
 return DynamoBackend(DYNAMO_BACKENDS[value]		).value




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     int							= int(lowercase		)
 return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)




def       lowerCAmelCase_     (		lowercase:							List[str]		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= int(lowercase		)
 return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				List[str]:







 '''simple docstring'''
 return {"yes": True, "no": False}[value.lower()]





class  __magic_name__  (		argparse.RawDescriptionHelpFormatter    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Optional[int]		,  _lowercase					:  List[Any]		,  _lowercase					:  Dict		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= super()._format_usage(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase							)
    _UpperCamelCase:     int							= usage.replace('''<command> [<args>] '''		,  ''''''							)
    return usage