



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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str	,   _snake_case	:str	,   _snake_case	:PreTrainedTokenizer	,   _snake_case	:int	,   _snake_case	:Optional[int] = None	,   )  ->     Union[str, Any]:
   _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=_snake_case				)
   _A      =       list(ds[list(files.keys()				)[0]].features.keys()				)
   _A      =       features_name.pop(_snake_case				)
   _A      =       list(set(ds[list(files.keys()				)[0]][label_name]				)				)
   _A      =       {label: i for i, label in enumerate(_snake_case				)}
   _A      =       tokenizer.model_input_names
   _A      =       {}

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

   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(
           _snake_case	,   ({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(
           _snake_case	,   ({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(
           _snake_case	,   ({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


UpperCAmelCase_		 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase__	:



 """simple docstring"""

 a__					: int =  field(metadata={"help": "Which column contains the label"})
 a__					: str =  field(default=_A							,  metadata={"help": "The path of the training file"})
 a__					: Optional[str] =  field(default=_A							,  metadata={"help": "The path of the development file"})
 a__					: Optional[str] =  field(default=_A							,  metadata={"help": "The path of the test file"})
 a__					: 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."
         )
     }							,  )
 a__					: bool =  field(
     default=_A							,  metadata={"help": "Overwrite the cached training and evaluation sets"})


@dataclass
class 	lowerCamelCase__	:



 """simple docstring"""

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

def 	SCREAMING_SNAKE_CASE_							(	)  ->     List[str]:
   # 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.
   _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=_snake_case	,   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(_snake_case				)	,   labelaid=_snake_case	,   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=_snake_case	,   cache_dir=model_args.cache_dir	,   )

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

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

   # Initialize our Trainer
   _A      =       TFTrainer(
       model=_snake_case	,   args=_snake_case	,   train_dataset=_snake_case	,   eval_dataset=_snake_case	,   compute_metrics=_snake_case	,   )

   # 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(_snake_case	,   '''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(_snake_case				)

   return results


if __name__ == "__main__":
   main()








UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   if p_len > t_len:
      return False

   _A      =       0
   _A      =       0
   _A      =       1

   # Calculating the hash of pattern and substring of text
   for i in range(_snake_case				):
      _A      =       (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      _A      =       (ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _A      =       (modulus_power * alphabet_size) % modulus

   for i in range(0	,   t_len - p_len + 1				):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      _A      =       (
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False

def 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



