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


_lowerCAmelCase       =		logging.getLogger(__name__)
@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  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=__lowercase,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
							UpperCAmelCase				   =     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.
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},  )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  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=__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.

					_UpperCamelCase			     =     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     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
					else:
										_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(__snake_case  ,     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 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
					_UpperCamelCase			     =     token_classification_task.get_labels(data_args.labels						)
					_UpperCamelCase			     =     dict(enumerate(__snake_case						)						)
					_UpperCamelCase			     =     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.

					_UpperCamelCase			     =     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  ,     )
					_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  ,     )
					_UpperCamelCase			     =     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
					_UpperCamelCase			     =     (
					    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
					)
					_UpperCamelCase			     =     (
					    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  ,     __snake_case						) -> Tuple[List[int], List[int]]:
										_UpperCamelCase			     =     np.argmax(__snake_case  ,     axis=2						)

										_UpperCamelCase     ,    _UpperCamelCase			     =     preds.shape

										_UpperCamelCase			     =     [[] for _ in range(__snake_case						)]
										_UpperCamelCase			     =     [[] 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						) -> Dict:
										_UpperCamelCase     ,    _UpperCamelCase			     =     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
					_UpperCamelCase			     =     DataCollatorWithPadding(__snake_case  ,     pad_to_multiple_of=8						) if training_args.fpaa else None

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

										_UpperCamelCase			     =     trainer.evaluate()

										_UpperCamelCase			     =     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:
										_UpperCamelCase			     =     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  ,     )

										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     trainer.predict(__snake_case						)
										_UpperCamelCase     ,    _UpperCamelCase			     =     align_predictions(__snake_case  ,     __snake_case						)

										_UpperCamelCase			     =     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
										_UpperCamelCase			     =     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						):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()

from typing import List

from .keymap import KEYMAP, get_character
def 					_snake_case	(  __snake_case						):

					def decorator(__snake_case						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     '''handle_key'''  ,     []						)
										handle += [key]
										setattr(__snake_case  ,     '''handle_key'''  ,     __snake_case						)
										return func

					return decorator
def 					_snake_case	(  *__snake_case						):

					def decorator(__snake_case						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     '''handle_key'''  ,     []						)
										handle += keys
										setattr(__snake_case  ,     '''handle_key'''  ,     __snake_case						)
										return func

					return decorator
class     lowerCAmelCase_     (       __lowercase ):







							def __new__(   cls      :     Optional[Any] ,			_A      :     Optional[Any] ,			_A      :     Optional[int] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     super().__new__(cls ,			_A ,			_A ,			_A		)
												if not hasattr(_A ,			'''key_handler'''		):
																	setattr(_A ,			'''key_handler''' ,			{}		)
												setattr(_A ,			'''handle_input''' ,			KeyHandler.handle_input		)

												for value in attrs.values():
																	_UpperCamelCase			     =     getattr(_A ,			'''handle_key''' ,			[]		)
																	for key in handled_keys:
																						_UpperCamelCase			     =     value
												return new_cls







							@staticmethod
							def  UpperCamelCase_						(   cls      :     str		):
												_UpperCamelCase			     =     get_character()
												if char != KEYMAP["undefined"]:
																	_UpperCamelCase			     =     ord(_A		)
												_UpperCamelCase			     =     cls.key_handler.get(_A		)
												if handler:
																	_UpperCamelCase			     =     char
																	return handler(cls		)
												else:
																	return None




def 					_snake_case	(  cls						):
					return KeyHandler(cls.__name__  ,     cls.__bases__  ,     cls.__dict__.copy()						)
