

"""simple docstring"""


from dataclasses import dataclass, field
from typing import Optional

@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Model name or path of model to be trained."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""./"""   ,	metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-train"""   ,	metadata={"""help""": """Name or path of training dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-valid"""   ,	metadata={"""help""": """Name or path of validation dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2   ,	metadata={"""help""": """Batch size for training."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2   ,	metadata={"""help""": """Batch size for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=0.1   ,	metadata={"""help""": """Value of weight decay."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=1_00_00   ,	metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=2E-4   ,	metadata={"""help""": """Learning rate fo training."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""cosine"""   ,	metadata={"""help""": """Learning rate."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=7_50   ,	metadata={"""help""": """Number of warmup steps in the learning rate schedule."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=16   ,	metadata={"""help""": """Number of gradient accumulation steps."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=5_00_00   ,	metadata={"""help""": """Maximum number of training steps."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=-1   ,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=10_24   ,	metadata={"""help""": """Sequence lengths used for training."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=1   ,	metadata={"""help""": """Training seed."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=10_24   ,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """If True the data is pretokenized."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Model name or path of model to be evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-valid"""   ,	metadata={"""help""": """Name or path of validation dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2   ,	metadata={"""help""": """Batch size used for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=-1   ,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=10_24   ,	metadata={"""help""": """Length of sequences to be evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=1   ,	metadata={"""help""": """Random seed used for evaluation."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Model name or path of model to be evaluated."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Number of workers used for code evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Sample from the language model's output distribution."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=0.2   ,	metadata={"""help""": """Sampling temperature used for generation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=2_56   ,	metadata={"""help""": """Maximum number of newly generated tokens."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=0   ,	metadata={"""help""": """Top-k parameter used for generation."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(default=0.95   ,	metadata={"""help""": """Top-p parameter used for nucleus sampling."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=10   ,	metadata={"""help""": """Number of generations to run in parallel."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=2_00   ,	metadata={"""help""": """Number of completions to generate for each sample."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=1   ,	metadata={"""help""": """Random seed used for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""eval_results.json"""   ,	metadata={"""help""": """Random seed used for evaluation."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""0"""   ,	metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""})
     _SCREAMING_SNAKE_CASE						: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							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=UpperCamelCase_   ,	metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""transformersbook/codeparrot"""   ,	metadata={"""help""": """Folder or name of dataset to process."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot-clean"""   ,	metadata={"""help""": """Folder to save processed processed dataset."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=10_00_00   ,	metadata={"""help""": """Number of files to save per JSON output file."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""content"""   ,	metadata={"""help""": """Column containing text data to process."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=10_00   ,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=1_00   ,	metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=0.25   ,	metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=1.5   ,	metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=0.7   ,	metadata={"""help""": """Probability for filtering config, test and uncommon files."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Name or path to the tokenizer."""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """If True, near-duplicate samples are removed."""})
     _SCREAMING_SNAKE_CASE						:Optional[float]       = field(
         default=0.85   ,	metadata={"""help""": """Jaccard threshold for near-duplicate samples."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""gpt2"""   ,	metadata={"""help""": """Base tokenizer to build new tokenizer from."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""transformersbook/codeparrot-train"""   ,	metadata={"""help""": """Dataset to train tokenizer on."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""content"""   ,	metadata={"""help""": """Column containing text data to process."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=20_00_00   ,	metadata={"""help""": """Number of examples to train tokenizer on."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=3_27_68   ,	metadata={"""help""": """Number of examples to train the tokenizer on."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""codeparrot"""   ,	metadata={"""help""": """Name of new tokenizer."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Push saved tokenizer to the hub."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Name or path to the tokenizer."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot-clean-train"""   ,	metadata={"""help""": """Name or path to the dataset to pretokenize."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""tokenized-codeparrot-train"""   ,	metadata={"""help""": """Repo name of the pretokenized data."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Number of workers used for code evaluation."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""gpt2-large"""   ,	metadata={"""help""": """Configuration to use for model initialization."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""codeparrot/codeparrot"""   ,	metadata={"""help""": """Tokenizer attached to model."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default="""codeparrot"""   ,	metadata={"""help""": """Name of the created model."""})
     _SCREAMING_SNAKE_CASE						:Optional[bool]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Push saved tokenizer to the hub."""})







"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bool:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							) + 1
      SCREAMING_SNAKE_CASE__   :      int      		=	len(__lowerCAmelCase							) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[[0 for i in range(__lowerCAmelCase							)] for j in range(__lowerCAmelCase							)]

      # since string of zero length match pattern of zero length
      SCREAMING_SNAKE_CASE__   :      Dict      		=	1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      int      		=	dp[0][j - 2] if pattern[j - 1] == """*""" else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1  ,					__lowerCAmelCase							):
            for j in range(1  ,					__lowerCAmelCase							):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        SCREAMING_SNAKE_CASE__   :      Any      		=	dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              SCREAMING_SNAKE_CASE__   :      List[str]      		=	1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dp[i - 1][j]
                        else:
                              SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
                  else:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	0

      return bool(dp[-1][-1]							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # inputing the strings
  # input_string = input("input a string :")
  # pattern = input("input a pattern :")

  a					:Any    = "aab"
  a					:Optional[Any]    = "c*a*b"

  # using function to check whether given string matches the given pattern
  if match_pattern(input_string, pattern):
    print(f'{input_string} matches the given pattern {pattern}')
  else:
    print(f'{input_string} does not match with the given pattern {pattern}')




