


'''simple docstring'''

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     str | Literal[False]:







     '''simple docstring'''






     _a						=		list(__lowerCamelCase				)
     _a						=		list(__lowerCamelCase				)
     _a						=		0
     for i in range(len(__lowerCamelCase				)				):
          if lista[i] != lista[i]:
               count += 1
               _a						=		"_"
     if count > 1:
          return False
     else:
          return "".join(__lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: list[str]				)      ->     list[str]:







     '''simple docstring'''






     _a						=		[]
     while True:
          _a						=		["$"] * len(__lowerCamelCase				)
          _a						=		[]
          for i in range(len(__lowerCamelCase				)				):
               for j in range(i + 1      ,  len(__lowerCamelCase				)				):
                    _a						=		compare_string(binary[i]      ,  binary[j]				)
                    if k is False:
                         _a						=		"*"
                         _a						=		"*"
                         temp.append("X"				)
          for i in range(len(__lowerCamelCase				)				):
               if checka[i] == "$":
                    pi.append(binary[i]				)
          if len(__lowerCamelCase				) == 0:
               return pi
          _a						=		list(set(__lowerCamelCase				)				)






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Sequence[float]				)      ->     list[str]:







     '''simple docstring'''






     _a						=		[]
     for minterm in minterms:
          _a						=		""
          for _ in range(__lowerCamelCase				):
               _a						=		str(minterm % 2				) + string
               minterm //= 2
          temp.append(__lowerCamelCase				)
     return temp






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: int				)      ->     bool:







     '''simple docstring'''






     _a						=		list(__lowerCamelCase				)
     _a						=		list(__lowerCamelCase				)
     _a						=		0
     for i in range(len(__lowerCamelCase				)				):
          if lista[i] != lista[i]:
               count_n += 1
     return count_n == count






def __UpperCamelCase   (			__lowerCamelCase		: list[list[int]]      ,  __lowerCamelCase		: list[str]				)      ->     list[str]:







     '''simple docstring'''






     _a						=		[]
     _a						=		[0] * len(__lowerCamelCase				)
     for i in range(len(chart[0]				)				):
          _a						=		0
          _a						=		-1
          for j in range(len(__lowerCamelCase				)				):
               if chart[j][i] == 1:
                    count += 1
                    _a						=		j
          if count == 1:
               _a						=		1
     for i in range(len(__lowerCamelCase				)				):
          if select[i] == 1:
               for j in range(len(chart[0]				)				):
                    if chart[i][j] == 1:
                         for k in range(len(__lowerCamelCase				)				):
                              _a						=		0
               temp.append(prime_implicants[i]				)
     while True:
          _a						=		0
          _a						=		-1
          _a						=		0
          for i in range(len(__lowerCamelCase				)				):
               _a						=		chart[i].count(1				)
               if count_n > max_n:
                    _a						=		count_n
                    _a						=		i

          if max_n == 0:
               return temp

          temp.append(prime_implicants[rem]				)

          for i in range(len(chart[0]				)				):
               if chart[rem][i] == 1:
                    for j in range(len(__lowerCamelCase				)				):
                         _a						=		0






def __UpperCamelCase   (			__lowerCamelCase		: list[str]      ,  __lowerCamelCase		: list[str]				)      ->     list[list[int]]:







     '''simple docstring'''






     _a						=		[[0 for x in range(len(__lowerCamelCase				)				)] for x in range(len(__lowerCamelCase				)				)]
     for i in range(len(__lowerCamelCase				)				):
          _a						=		prime_implicants[i].count("_"				)
          for j in range(len(__lowerCamelCase				)				):
               if is_for_table(prime_implicants[i]      ,  binary[j]      ,  __lowerCamelCase				):
                    _a						=		1

     return chart






def __UpperCamelCase   (			)      ->     None:







     '''simple docstring'''






     _a						=		int(input("Enter the no. of variables\n"				)				)
     _a						=		[
         float(__lowerCamelCase				)
         for x in input(
             "Enter the decimal representation of Minterms \'Spaces Separated\'\n"				).split()
     ]
     _a						=		decimal_to_binary(__lowerCamelCase      ,  __lowerCamelCase				)

     _a						=		check(__lowerCamelCase				)
     print("Prime Implicants are:"				)
     print(__lowerCamelCase				)
     _a						=		prime_implicant_chart(__lowerCamelCase      ,  __lowerCamelCase				)

     _a						=		selection(__lowerCamelCase      ,  __lowerCamelCase				)
     print("Essential Prime Implicants are:"				)
     print(__lowerCamelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()










'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: PreTrainedTokenizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Optional[int] = None      ,  )      ->     Tuple:







     '''simple docstring'''






     _a						=		{}

     if train_file is not None:
          _a						=		[train_file]
     if eval_file is not None:
          _a						=		[eval_file]
     if test_file is not None:
          _a						=		[test_file]

     _a						=		datasets.load_dataset("csv"      ,  data_files=__lowerCamelCase				)
     _a						=		list(ds[list(files.keys()				)[0]].features.keys()				)
     _a						=		features_name.pop(__lowerCamelCase				)
     _a						=		list(set(ds[list(files.keys()				)[0]][label_name]				)				)
     _a						=		{label: i for i, label in enumerate(__lowerCamelCase				)}
     _a						=		tokenizer.model_input_names
     _a						=		{}

     if len(__lowerCamelCase				) == 1:
          for k in files.keys():
               _a						=		ds[k].map(
                   lambda __lowerCamelCase				: tokenizer.batch_encode_plus(
                       example[features_name[0]]      ,  truncation=__lowerCamelCase      ,  max_length=__lowerCamelCase      ,  padding="max_length"				)      ,  batched=__lowerCamelCase      ,  )
     elif len(__lowerCamelCase				) == 2:
          for k in files.keys():
               _a						=		ds[k].map(
                   lambda __lowerCamelCase				: tokenizer.batch_encode_plus(
                       (example[features_name[0]], example[features_name[1]])      ,  truncation=__lowerCamelCase      ,  max_length=__lowerCamelCase      ,  padding="max_length"      ,  )      ,  batched=__lowerCamelCase      ,  )

     def gen_train():
          for ex in transformed_ds[datasets.Split.TRAIN]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

     def gen_val():
          for ex in transformed_ds[datasets.Split.VALIDATION]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

     def gen_test():
          for ex in transformed_ds[datasets.Split.TEST]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

     _a						=		(
         tf.data.Dataset.from_generator(
             __lowerCamelCase      ,  ({k: tf.intaa for k in input_names}, tf.intaa)      ,  ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))      ,  )
         if datasets.Split.TRAIN in transformed_ds
         else None
     )

     if train_ds is not None:
          _a						=		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

     _a						=		(
         tf.data.Dataset.from_generator(
             __lowerCamelCase      ,  ({k: tf.intaa for k in input_names}, tf.intaa)      ,  ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))      ,  )
         if datasets.Split.VALIDATION in transformed_ds
         else None
     )

     if val_ds is not None:
          _a						=		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

     _a						=		(
         tf.data.Dataset.from_generator(
             __lowerCamelCase      ,  ({k: tf.intaa for k in input_names}, tf.intaa)      ,  ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))      ,  )
         if datasets.Split.TEST in transformed_ds
         else None
     )

     if test_ds is not None:
          _a						=		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

     return train_ds, val_ds, test_ds, labelaid


lowercase__				     =		logging.getLogger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(metadata={'''help''': '''Which column contains the label'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the training file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the development file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the test file'''} )
     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=lowerCamelCase__ ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
     UpperCAmelCase 					=				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.
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,			)







def __UpperCamelCase   (			)      ->     str:







     '''simple docstring'''






     _a						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
     _a			,			_a			,			_a						=		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."				)

     # 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(
         F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, "
         F"16-bits training: {training_args.fpaa}"				)
     logger.info(F"Training/evaluation parameters {training_args}"				)

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

     _a						=		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      ,  )

     _a			,			_a			,			_a			,			_a						=		get_tfds(
         train_file=data_args.train_file      ,  eval_file=data_args.dev_file      ,  test_file=data_args.test_file      ,  tokenizer=__lowerCamelCase      ,  label_column_id=data_args.label_column_id      ,  max_seq_length=data_args.max_seq_length      ,  )

     _a						=		AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,  num_labels=len(__lowerCamelCase				)      ,  labelaid=__lowerCamelCase      ,  idalabel={id: label for label, id in labelaid.items()}      ,  finetuning_task="text-classification"      ,  cache_dir=model_args.cache_dir      ,  )

     with training_args.strategy.scope():
          _a						=		TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path      ,  from_pt=bool(".bin" in model_args.model_name_or_path				)      ,  config=__lowerCamelCase      ,  cache_dir=model_args.cache_dir      ,  )

     def compute_metrics(__lowerCamelCase		: EvalPrediction				) -> Dict:
          _a						=		np.argmax(p.predictions      ,  axis=1				)

          return {"acc": (preds == p.label_ids).mean()}

     # Initialize our Trainer
     _a						=		TFTrainer(
         model=__lowerCamelCase      ,  args=__lowerCamelCase      ,  train_dataset=__lowerCamelCase      ,  eval_dataset=__lowerCamelCase      ,  compute_metrics=__lowerCamelCase      ,  )

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

     # Evaluation
     _a						=		{}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"				)
          _a						=		trainer.evaluate()
          _a						=		os.path.join(training_args.output_dir      ,  "eval_results.txt"				)

          with open(__lowerCamelCase      ,  "w"				) as writer:
               logger.info("***** Eval results *****"				)

               for key, value in result.items():
                    logger.info(F"  {key} = {value}"				)
                    writer.write(F"{key} = {value}\n"				)

               results.update(__lowerCamelCase				)

     return results


if __name__ == "__main__":
   main()

