



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






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






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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.
  __SCREAMING_SNAKE_CASE       :    int       =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
 else:
  __SCREAMING_SNAKE_CASE       :    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."     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	import_module("tasks"     )
 try:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	getattr(__lowerCamelCase		,	model_args.task_type     )
  __SCREAMING_SNAKE_CASE       :    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"		,	__lowerCamelCase     )

 # Set seed
 set_seed(training_args.seed     )

 # Prepare CONLL-2003 task
 __SCREAMING_SNAKE_CASE       :    str       =	token_classification_task.get_labels(data_args.labels     )
 __SCREAMING_SNAKE_CASE       :    Dict[int, str]       =	dict(enumerate(__lowerCamelCase     )     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	len(__lowerCamelCase     )

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

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path		,	num_labels=__lowerCamelCase		,	idalabel=__lowerCamelCase		,	labelaid={label: i for i, label in enumerate(__lowerCamelCase     )}		,	cache_dir=model_args.cache_dir		,	)
 __SCREAMING_SNAKE_CASE       :    str       =	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		,	)
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path		,	from_tf=bool(".ckpt" in model_args.model_name_or_path     )		,	config=__lowerCamelCase		,	cache_dir=model_args.cache_dir		,	)

 # Get datasets
 __SCREAMING_SNAKE_CASE       :    Dict       =	(
     TokenClassificationDataset(
         token_classification_task=__lowerCamelCase		,	data_dir=data_args.data_dir		,	tokenizer=__lowerCamelCase		,	labels=__lowerCamelCase		,	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
 )
 __SCREAMING_SNAKE_CASE       :    str       =	(
     TokenClassificationDataset(
         token_classification_task=__lowerCamelCase		,	data_dir=data_args.data_dir		,	tokenizer=__lowerCamelCase		,	labels=__lowerCamelCase		,	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]]:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	np.argmax(__lowerCamelCase		,	axis=2     )

  __SCREAMING_SNAKE_CASE       :    Dict       =	preds.shape

  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[[] for _ in range(__lowerCamelCase     )]
  __SCREAMING_SNAKE_CASE       :    Dict       =	[[] for _ in range(__lowerCamelCase     )]

  for i in range(__lowerCamelCase     ):
   for j in range(__lowerCamelCase     ):
    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(__lowerCamelCase   :     EvalPrediction     ) -> Dict:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	align_predictions(p.predictions		,	p.label_ids     )
  return {
      "accuracy_score": accuracy_score(__lowerCamelCase		,	__lowerCamelCase     ),
      "precision": precision_score(__lowerCamelCase		,	__lowerCamelCase     ),
      "recall": recall_score(__lowerCamelCase		,	__lowerCamelCase     ),
      "f1": fa_score(__lowerCamelCase		,	__lowerCamelCase     ),
  }

 # Data collator
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	DataCollatorWithPadding(__lowerCamelCase		,	pad_to_multiple_of=8     ) if training_args.fpaa else None

 # Initialize our Trainer
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	Trainer(
     model=__lowerCamelCase		,	args=__lowerCamelCase		,	train_dataset=__lowerCamelCase		,	eval_dataset=__lowerCamelCase		,	compute_metrics=__lowerCamelCase		,	data_collator=__lowerCamelCase		,	)

 # 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
 __SCREAMING_SNAKE_CASE       :    List[str]       =	{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"     )

  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	trainer.evaluate()

  __SCREAMING_SNAKE_CASE       :    List[Any]       =	os.path.join(training_args.output_dir		,	"eval_results.txt"     )
  if trainer.is_world_process_zero():
   with open(__lowerCamelCase		,	"w"     ) as writer:
    logger.info("***** Eval results *****"     )
    for key, value in result.items():
     logger.info("  %s = %s"		,	__lowerCamelCase		,	__lowerCamelCase     )
     writer.write("%s = %s\n" % (key, value)     )

   results.update(__lowerCamelCase     )

    # Predict
 if training_args.do_predict:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	TokenClassificationDataset(
      token_classification_task=__lowerCamelCase		,	data_dir=data_args.data_dir		,	tokenizer=__lowerCamelCase		,	labels=__lowerCamelCase		,	model_type=config.model_type		,	max_seq_length=data_args.max_seq_length		,	overwrite_cache=data_args.overwrite_cache		,	mode=Split.test		,	)

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	trainer.predict(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	align_predictions(__lowerCamelCase		,	__lowerCamelCase     )

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

        # Save predictions
  __SCREAMING_SNAKE_CASE       :    Tuple       =	os.path.join(training_args.output_dir		,	"test_predictions.txt"     )
  if trainer.is_world_process_zero():
   with open(__lowerCamelCase		,	"w"     ) as writer:
    with open(os.path.join(data_args.data_dir		,	"test.txt"     )		,	"r"     ) as f:
     token_classification_task.write_predictions_to_file(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )

 return results






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 main()


if __name__ == "__main__":
 main()











from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_SNAKE_CASE       :    str       =	0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

    # print entropy
 print(F"""{round(-1 * my_fir_sum     ):.1f}"""     )

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # print second entropy
 print(F"""{round(-1 * my_sec_sum     ):.1f}"""     )

 # print the difference between them
 print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0		,	len(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
      main()






