





'''simple docstring'''


from dataclasses import dataclass, field
from typing import Optional
@dataclass
class       a_   :
     __A      =			field(
         default="codeparrot/codeparrot"	,						metadata={"help": "Model name or path of model to be trained."}							)
     __A      =			field(
         default="./"	,						metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}							)
     __A      =			field(
         default="codeparrot/codeparrot-clean-train"	,						metadata={"help": "Name or path of training dataset."}							)
     __A      =			field(
         default="codeparrot/codeparrot-clean-valid"	,						metadata={"help": "Name or path of validation dataset."}							)
     __A      =			field(default=2	,						metadata={"help": "Batch size for training."}							)
     __A      =			field(default=2	,						metadata={"help": "Batch size for evaluation."}							)
     __A      =			field(default=0.1	,						metadata={"help": "Value of weight decay."}							)
     __A      =			field(
         default=10_000	,						metadata={"help": "Size of buffer used to shuffle streaming dataset."}							)
     __A      =			field(default=2e-4	,						metadata={"help": "Learning rate fo training."}							)
     __A      =			field(default="cosine"	,						metadata={"help": "Learning rate."}							)
     __A      =			field(
         default=750	,						metadata={"help": "Number of warmup steps in the learning rate schedule."}							)
     __A      =			field(
         default=16	,						metadata={"help": "Number of gradient accumulation steps."}							)
     __A      =			field(
         default=_lowerCAmelCase	,						metadata={"help": "Use gradient checkpointing to reduce memory footprint."}							)
     __A      =			field(default=50_000	,						metadata={"help": "Maximum number of training steps."}							)
     __A      =			field(
         default=-1	,						metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}							)
     __A      =			field(default=1_024	,						metadata={"help": "Sequence lengths used for training."}							)
     __A      =			field(default=1	,						metadata={"help": "Training seed."}							)
     __A      =			field(
         default=1_024	,						metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}	,						)
     __A      =			field(
         default=_lowerCAmelCase	,						metadata={"help": "States path if the training should continue from a checkpoint folder."}							)
     __A      =			field(default=_lowerCAmelCase	,						metadata={"help": "If True the data is pretokenized."}							)







@dataclass
class       a_   :
     __A      =			field(
         default="codeparrot/codeparrot"	,						metadata={"help": "Model name or path of model to be evaluated."}							)
     __A      =			field(
         default="codeparrot/codeparrot-clean-valid"	,						metadata={"help": "Name or path of validation dataset."}							)
     __A      =			field(default=2	,						metadata={"help": "Batch size used for evaluation."}							)
     __A      =			field(
         default=-1	,						metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}							)
     __A      =			field(default=1_024	,						metadata={"help": "Length of sequences to be evaluated."}							)
     __A      =			field(default=1	,						metadata={"help": "Random seed used for evaluation."}							)







@dataclass
class       a_   :
     __A      =			field(
         default="codeparrot/codeparrot"	,						metadata={"help": "Model name or path of model to be evaluated."}							)
     __A      =			field(default=_lowerCAmelCase	,						metadata={"help": "Number of workers used for code evaluation."}							)
     __A      =			field(
         default=_lowerCAmelCase	,						metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}	,						)
     __A      =			field(
         default=_lowerCAmelCase	,						metadata={"help": "Sample from the language model's output distribution."}							)
     __A      =			field(default=0.2	,						metadata={"help": "Sampling temperature used for generation."}							)
     __A      =			field(default=256	,						metadata={"help": "Maximum number of newly generated tokens."}							)
     __A      =			field(default=0	,						metadata={"help": "Top-k parameter used for generation."}							)
     __A      =			field(default=0.95	,						metadata={"help": "Top-p parameter used for nucleus sampling."}							)
     __A      =			field(default=10	,						metadata={"help": "Number of generations to run in parallel."}							)
     __A      =			field(
         default=200	,						metadata={"help": "Number of completions to generate for each sample."}							)
     __A      =			field(default=1	,						metadata={"help": "Random seed used for evaluation."}							)
     __A      =			field(
         default="eval_results.json"	,						metadata={"help": "Random seed used for evaluation."}							)
     __A      =			field(
         default="0"	,						metadata={"help": "Allow `code_eval` to execute Python code on machine"}							)
     __A      =			field(
         default=-1	,						metadata={
             "help": (
                 "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
                 " number corresponds to which GPU device id to run on."
             )
         }	,						)







@dataclass
class       a_   :
     __A      =			field(
         default=_lowerCAmelCase	,						metadata={
             "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
         }	,						)
     __A      =			field(
         default="transformersbook/codeparrot"	,						metadata={"help": "Folder or name of dataset to process."}							)
     __A      =			field(
         default="codeparrot-clean"	,						metadata={"help": "Folder to save processed processed dataset."}							)
     __A      =			field(
         default=100_000	,						metadata={"help": "Number of files to save per JSON output file."}							)
     __A      =			field(default="content"	,						metadata={"help": "Column containing text data to process."}							)
     __A      =			field(
         default=1_000	,						metadata={"help": "Maximum line length in file, otherwise file is filtered."}							)
     __A      =			field(
         default=100	,						metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}							)
     __A      =			field(
         default=0.25	,						metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}							)
     __A      =			field(
         default=1.5	,						metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}							)
     __A      =			field(
         default=0.7	,						metadata={"help": "Probability for filtering config, test and uncommon files."}							)
     __A      =			field(
         default="codeparrot/codeparrot"	,						metadata={"help": "Name or path to the tokenizer."}	,						)
     __A      =			field(
         default=_lowerCAmelCase	,						metadata={"help": "If True, near-duplicate samples are removed."}							)
     __A      =			field(
         default=0.85	,						metadata={"help": "Jaccard threshold for near-duplicate samples."}							)







@dataclass
class       a_   :
     __A      =			field(
         default="gpt2"	,						metadata={"help": "Base tokenizer to build new tokenizer from."}							)
     __A      =			field(
         default="transformersbook/codeparrot-train"	,						metadata={"help": "Dataset to train tokenizer on."}							)
     __A      =			field(default="content"	,						metadata={"help": "Column containing text data to process."}							)
     __A      =			field(default=200_000	,						metadata={"help": "Number of examples to train tokenizer on."}							)
     __A      =			field(
         default=32_768	,						metadata={"help": "Number of examples to train the tokenizer on."}							)
     __A      =			field(default="codeparrot"	,						metadata={"help": "Name of new tokenizer."}							)
     __A      =			field(default=_lowerCAmelCase	,						metadata={"help": "Push saved tokenizer to the hub."}							)







@dataclass
class       a_   :
     __A      =			field(
         default="codeparrot/codeparrot"	,						metadata={"help": "Name or path to the tokenizer."}							)
     __A      =			field(
         default="codeparrot/codeparrot-clean-train"	,						metadata={"help": "Name or path to the dataset to pretokenize."}							)
     __A      =			field(
         default="tokenized-codeparrot-train"	,						metadata={"help": "Repo name of the pretokenized data."}							)
     __A      =			field(default=_lowerCAmelCase	,						metadata={"help": "Number of workers used for code evaluation."}							)







@dataclass
class       a_   :
     __A      =			field(
         default="gpt2-large"	,						metadata={"help": "Configuration to use for model initialization."}							)
     __A      =			field(
         default="codeparrot/codeparrot"	,						metadata={"help": "Tokenizer attached to model."}							)
     __A      =			field(default="codeparrot"	,						metadata={"help": "Name of the created model."}							)
     __A      =			field(default=_lowerCAmelCase	,						metadata={"help": "Push saved tokenizer to the hub."}							)









'''simple docstring'''


lowerCAmelCase					:     Optional[int]												=256
# Modulus to hash a string
lowerCAmelCase					:     Tuple												=1_000_003
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					str ):
   lowercase_			:Dict    			= len(__lowerCamelCase )
   lowercase_			:int    			= len(__lowerCamelCase )
   if p_len > t_len:
      return False

   lowercase_			:Any    			= 0
   lowercase_			:Dict    			= 0
   lowercase_			:int    			= 1

   # Calculating the hash of pattern and substring of text
   for i in range(__lowerCamelCase ):
      lowercase_			:Union[str, Any]    			= (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      lowercase_			:Tuple    			= (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      lowercase_			:Tuple    			= (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
      lowercase_			:Tuple    			= (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 							UpperCAmelCase_  (						):
   lowercase_			:List[Any]    			= "abc1abc12"
   lowercase_			:List[str]    			= "alskfjaldsabc1abc1abc12k23adsfabcabc"
   lowercase_			:int    			= "alskfjaldsk23adsfabcabc"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase ) and not rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 2)
   lowercase_			:Union[str, Any]    			= "ABABX"
   lowercase_			:Optional[Any]    			= "ABABZABABYABABX"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 3)
   lowercase_			:Dict    			= "AAAB"
   lowercase_			:int    			= "ABAAAAAB"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 4)
   lowercase_			:Tuple    			= "abcdabcy"
   lowercase_			:Union[str, Any]    			= "abcxabcdabxabcdabcdabcy"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )

   # Test 5)
   lowercase_			:Tuple    			= "Lü"
   lowercase_			:List[Any]    			= "Lüsai"
   assert rabin_karp(__lowerCamelCase	,__lowerCamelCase )
   lowercase_			:str    			= "Lue"
   assert not rabin_karp(__lowerCamelCase	,__lowerCamelCase )
   print("Success." )


if __name__ == "__main__":
 test_rabin_karp()


