



"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot'	,      metadata={'help': 'Model name or path of model to be trained.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='./'	,      metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot-clean-train'	,      metadata={'help': 'Name or path of training dataset.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot-clean-valid'	,      metadata={'help': 'Name or path of validation dataset.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=2	,      metadata={'help': 'Batch size for training.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=2	,      metadata={'help': 'Batch size for evaluation.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(default=0.1	,      metadata={'help': 'Value of weight decay.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=1_0_0_0_0	,      metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(default=2E-4	,      metadata={'help': 'Learning rate fo training.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default='cosine'	,      metadata={'help': 'Learning rate.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=7_5_0	,      metadata={'help': 'Number of warmup steps in the learning rate schedule.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=1_6	,      metadata={'help': 'Number of gradient accumulation steps.'}       )
  UpperCamelCase	:				Optional[bool]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=5_0_0_0_0	,      metadata={'help': 'Maximum number of training steps.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=-1	,      metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=1_0_2_4	,      metadata={'help': 'Sequence lengths used for training.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=1	,      metadata={'help': 'Training seed.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=1_0_2_4	,      metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'States path if the training should continue from a checkpoint folder.'}       )
  UpperCamelCase	:				Optional[bool]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'If True the data is pretokenized.'}       )







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot'	,      metadata={'help': 'Model name or path of model to be evaluated.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot-clean-valid'	,      metadata={'help': 'Name or path of validation dataset.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=2	,      metadata={'help': 'Batch size used for evaluation.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=-1	,      metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=1_0_2_4	,      metadata={'help': 'Length of sequences to be evaluated.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=1	,      metadata={'help': 'Random seed used for evaluation.'}       )







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot'	,      metadata={'help': 'Model name or path of model to be evaluated.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'Number of workers used for code evaluation.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}	,      )
  UpperCamelCase	:				Optional[bool]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Sample from the language model\'s output distribution.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(default=0.2	,      metadata={'help': 'Sampling temperature used for generation.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=2_5_6	,      metadata={'help': 'Maximum number of newly generated tokens.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=0	,      metadata={'help': 'Top-k parameter used for generation.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(default=0.95	,      metadata={'help': 'Top-p parameter used for nucleus sampling.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=1_0	,      metadata={'help': 'Number of generations to run in parallel.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=2_0_0	,      metadata={'help': 'Number of completions to generate for each sample.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=1	,      metadata={'help': 'Random seed used for evaluation.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='eval_results.json'	,      metadata={'help': 'Random seed used for evaluation.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='0'	,      metadata={'help': 'Allow `code_eval` to execute Python code on machine'}       )
  UpperCamelCase	:				Optional[int]     						=					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	:
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
      }	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='transformersbook/codeparrot'	,      metadata={'help': 'Folder or name of dataset to process.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot-clean'	,      metadata={'help': 'Folder to save processed processed dataset.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=1_0_0_0_0_0	,      metadata={'help': 'Number of files to save per JSON output file.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default='content'	,      metadata={'help': 'Column containing text data to process.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(
      default=1_0_0_0	,      metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(
      default=1_0_0	,      metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(
      default=0.25	,      metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(
      default=1.5	,      metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(
      default=0.7	,      metadata={'help': 'Probability for filtering config, test and uncommon files.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot'	,      metadata={'help': 'Name or path to the tokenizer.'}	,      )
  UpperCamelCase	:				Optional[bool]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'If True, near-duplicate samples are removed.'}       )
  UpperCamelCase	:				Optional[float]     						=					field(
      default=0.85	,      metadata={'help': 'Jaccard threshold for near-duplicate samples.'}       )







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default='gpt2'	,      metadata={'help': 'Base tokenizer to build new tokenizer from.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='transformersbook/codeparrot-train'	,      metadata={'help': 'Dataset to train tokenizer on.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default='content'	,      metadata={'help': 'Column containing text data to process.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=2_0_0_0_0_0	,      metadata={'help': 'Number of examples to train tokenizer on.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=3_2_7_6_8	,      metadata={'help': 'Number of examples to train the tokenizer on.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default='codeparrot'	,      metadata={'help': 'Name of new tokenizer.'}       )
  UpperCamelCase	:				Optional[bool]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'Push saved tokenizer to the hub.'}       )







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot'	,      metadata={'help': 'Name or path to the tokenizer.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot-clean-train'	,      metadata={'help': 'Name or path to the dataset to pretokenize.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='tokenized-codeparrot-train'	,      metadata={'help': 'Repo name of the pretokenized data.'}       )
  UpperCamelCase	:				Optional[int]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'Number of workers used for code evaluation.'}       )







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default='gpt2-large'	,      metadata={'help': 'Configuration to use for model initialization.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default='codeparrot/codeparrot'	,      metadata={'help': 'Tokenizer attached to model.'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default='codeparrot'	,      metadata={'help': 'Name of the created model.'}       )
  UpperCamelCase	:				Optional[bool]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'Push saved tokenizer to the hub.'}       )








"""simple docstring"""
import string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



