

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()


__A   :     Optional[Any]      =			logging.getLogger(__name__)






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






@dataclass
class    __A				:
    lowerCAmelCase_							:		str						=							field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
    lowerCAmelCase_							:		Optional[str]						=							field(
        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=lowerCAmelCase						,						metadata={"help": "Overwrite the cached training and evaluation sets"} )



def 		SCREAMING_SNAKE_CASE__				(							)					-> Optional[int]:



    '''simple docstring'''






    lowerCAmelCase				:			Tuple    			=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			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.'				)

    lowerCAmelCase				:			str    			=      import_module('tasks'				)

    try:
        lowerCAmelCase				:			Dict    			=      getattr(_UpperCAmelCase,      model_args.task_type				)
        lowerCAmelCase				:			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,      )
    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',      _UpperCAmelCase				)

    # Prepare Token Classification task
    lowerCAmelCase				:			Tuple    			=      token_classification_task.get_labels(data_args.labels				)
    lowerCAmelCase				:			Dict[int, str]    			=      dict(enumerate(_UpperCAmelCase				)				)
    lowerCAmelCase				:			Dict    			=      len(_UpperCAmelCase				)

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

    lowerCAmelCase				:			Optional[Any]    			=      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,      num_labels=_UpperCAmelCase,      idalabel=_UpperCAmelCase,      labelaid={label: i for i, label in enumerate(_UpperCAmelCase				)},      cache_dir=model_args.cache_dir,      )
    lowerCAmelCase				:			Any    			=      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():
        lowerCAmelCase				:			Optional[int]    			=      TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path,      from_pt=bool('.bin' in model_args.model_name_or_path				),      config=_UpperCAmelCase,      cache_dir=model_args.cache_dir,      )

    # Get datasets
    lowerCAmelCase				:			str    			=      (
        TFTokenClassificationDataset(
            token_classification_task=_UpperCAmelCase,      data_dir=data_args.data_dir,      tokenizer=_UpperCAmelCase,      labels=_UpperCAmelCase,      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
    )
    lowerCAmelCase				:			List[str]    			=      (
        TFTokenClassificationDataset(
            token_classification_task=_UpperCAmelCase,      data_dir=data_args.data_dir,      tokenizer=_UpperCAmelCase,      labels=_UpperCAmelCase,      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(_UpperCAmelCase,      _UpperCAmelCase				) -> Tuple[List[int], List[int]]:
        lowerCAmelCase				:			Optional[Any]    			=      np.argmax(_UpperCAmelCase,      axis=2				)
        lowerCAmelCase			,  lowerCAmelCase				:			Tuple    			=      preds.shape
        lowerCAmelCase				:			Tuple    			=      [[] for _ in range(_UpperCAmelCase				)]
        lowerCAmelCase				:			Optional[int]    			=      [[] for _ in range(_UpperCAmelCase				)]

        for i in range(_UpperCAmelCase				):
            for j in range(_UpperCAmelCase				):
                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(_UpperCAmelCase				) -> Dict:
        lowerCAmelCase			,  lowerCAmelCase				:			str    			=      align_predictions(p.predictions,      p.label_ids				)

        return {
            "precision": precision_score(_UpperCAmelCase,      _UpperCAmelCase				),
            "recall": recall_score(_UpperCAmelCase,      _UpperCAmelCase				),
            "f1": fa_score(_UpperCAmelCase,      _UpperCAmelCase				),
        }

    # Initialize our Trainer
    lowerCAmelCase				:			Tuple    			=      TFTrainer(
        model=_UpperCAmelCase,      args=_UpperCAmelCase,      train_dataset=train_dataset.get_dataset() if train_dataset else None,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,      compute_metrics=_UpperCAmelCase,      )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
    lowerCAmelCase				:			List[str]    			=      {}
    if training_args.do_eval:
        logger.info('*** Evaluate ***'				)

        lowerCAmelCase				:			Tuple    			=      trainer.evaluate()
        lowerCAmelCase				:			Optional[int]    			=      os.path.join(training_args.output_dir,      'eval_results.txt'				)

        with open(_UpperCAmelCase,      'w'				) as writer:
            logger.info('***** Eval results *****'				)

            for key, value in result.items():
                logger.info('  %s = %s',      _UpperCAmelCase,      _UpperCAmelCase				)
                writer.write('%s = %s\n' % (key, value)				)

            results.update(_UpperCAmelCase				)

    # Predict
    if training_args.do_predict:
        lowerCAmelCase				:			List[Any]    			=      TFTokenClassificationDataset(
            token_classification_task=_UpperCAmelCase,      data_dir=data_args.data_dir,      tokenizer=_UpperCAmelCase,      labels=_UpperCAmelCase,      model_type=config.model_type,      max_seq_length=data_args.max_seq_length,      overwrite_cache=data_args.overwrite_cache,      mode=Split.test,      )

        lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			Optional[int]    			=      trainer.predict(test_dataset.get_dataset()				)
        lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      align_predictions(_UpperCAmelCase,      _UpperCAmelCase				)
        lowerCAmelCase				:			int    			=      classification_report(_UpperCAmelCase,      _UpperCAmelCase				)

        logger.info('\n%s',      _UpperCAmelCase				)

        lowerCAmelCase				:			Optional[int]    			=      os.path.join(training_args.output_dir,      'test_results.txt'				)

        with open(_UpperCAmelCase,      'w'				) as writer:
            writer.write('%s\n' % report				)

        # Save predictions
        lowerCAmelCase				:			int    			=      os.path.join(training_args.output_dir,      'test_predictions.txt'				)

        with open(_UpperCAmelCase,      'w'				) as writer:
            with open(os.path.join(data_args.data_dir,      'test.txt'				),      'r'				) as f:
                lowerCAmelCase				:			int    			=      0

                for line in f:
                    if line.startswith('-DOCSTART-'				) or line == "" or line == "\n":
                        writer.write(_UpperCAmelCase				)

                        if not preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        lowerCAmelCase				:			Optional[int]    			=      line.split()[0] + ' ' + preds_list[example_id].pop(0				) + '\n'

                        writer.write(_UpperCAmelCase				)
                    else:
                        logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.',      line.split()[0]				)

    return results


if __name__ == "__main__":
 main()








def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:



    '''simple docstring'''






    lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
    lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)

    lowerCAmelCase				:			str    			=      [
        [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]
    lowerCAmelCase				:			str    			=      [
        ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]

    for i in range(1,      len_source_seq + 1				):
        lowerCAmelCase				:			int    			=      i * delete_cost
        lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"

    for i in range(1,      len_destination_seq + 1				):
        lowerCAmelCase				:			Tuple    			=      i * insert_cost
        lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"

    for i in range(1,      len_source_seq + 1				):
        for j in range(1,      len_destination_seq + 1				):
            if source_seq[i - 1] == destination_seq[j - 1]:
                lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
            else:
                lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"

    return costs, ops
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:



    '''simple docstring'''






    if i == 0 and j == 0:
        return []
    else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
            lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
            seq.append(ops[i][j]				)
            return seq
        elif ops[i][j][0] == "D":
            lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
            seq.append(ops[i][j]				)
            return seq
        else:
            lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
            seq.append(ops[i][j]				)
            return seq


if __name__ == "__main__":
 __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)

 __A   :     Optional[int]      =			len(operations)
 __A   :     Optional[Any]      =			len(operations[0])
 __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)

 __A   :     int      =			list('''Python''')
 __A   :     Tuple      =			0
 __A   :     Tuple      =			0

 with open('''min_cost.txt''', '''w''') as file:
  for op in sequence:
   print(''''''.join(string))

   if op[0] == "C":
    file.write('''%-16s''' % '''Copy %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost -= 1
   elif op[0] == "R":
    __A   :     Any      =			op[2]

    file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
    file.write('''\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('''%-16s''' % '''Delete %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('''%-16s''' % '''Insert %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2

   i += 1

  print(''''''.join(string))
  print('''Cost: ''', cost)

  file.write('''\r\nMinimum cost: ''' + str(cost))





