




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_ 		=  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=_SCREAMING_SNAKE_CASE				,      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=_SCREAMING_SNAKE_CASE				,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
      lowerCAmelCase__      :			bool     =       field(default=_SCREAMING_SNAKE_CASE				,      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=_SCREAMING_SNAKE_CASE				,      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(
          metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} )
      lowerCAmelCase__      :			int     =       field(
          default=1_28				,      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=_SCREAMING_SNAKE_CASE				,      metadata={"help": "Overwrite the cached training and evaluation sets"} )







def        __SCREAMING_SNAKE_CASE (	)							->						Union[str, Any]:

    """simple docstring"""


    __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(lowercase_       ,  lowercase_   ) -> 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] != -1_00:
                    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_   ) -> 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()










from string import ascii_uppercase



def      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     str    ,   snake_case						:     bool = False    ,   snake_case						:     str = ascii_uppercase			)							->				str:
   __lowerCamelCase:					List[str]	 =						keyword.upper()
   __lowerCamelCase:					Tuple	 =						plaintext.upper()
   __lowerCamelCase:					Optional[Any]	 =						set(snake_case			)

   # create a list of unique characters in the keyword - their order matters
   # it determines how we will map plaintext characters to the ciphertext
   __lowerCamelCase:					Tuple	 =						[]
   for char in keyword:
      if char in alphabet_set and char not in unique_chars:
         unique_chars.append(snake_case			)
    # the number of those unique characters will determine the number of rows
   __lowerCamelCase:					Tuple	 =						len(snake_case			)

   # create a shifted version of the alphabet
   __lowerCamelCase:					List[str]	 =						unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

   # create a modified alphabet by splitting the shifted alphabet into rows
   __lowerCamelCase:					List[str]	 =						[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0    ,   26    ,   snake_case			)
   ]

   # map the alphabet characters to the modified alphabet characters
   # going 'vertically' through the modified alphabet - consider columns first
   __lowerCamelCase:					Union[str, Any]	 =						{}
   __lowerCamelCase:					str	 =						0
   for column in range(snake_case			):
      for row in modified_alphabet:
         # if current row (the last one) is too short, break out of loop
         if len(snake_case			) <= column:
            break

         # map current letter to letter in modified alphabet
         __lowerCamelCase:					Union[str, Any]	 =						row[column]
         letter_index += 1

   if verbose:
      print(snake_case			)
   # create the encrypted text by mapping the plaintext to the modified alphabet
   return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
   # example use
   print(mixed_keyword('''college''', '''UNIVERSITY'''))


