




'''simple docstring'''


from dataclasses import dataclass, field
from typing import Optional





@dataclass
class  __lowercase    :
   _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=10000							,				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=__magic_name__							,				metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}			)
   _a    						=   field(default=50000							,				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=1024							,				metadata={"""help""": """Sequence lengths used for training."""}			)
   _a    						=   field(default=1							,				metadata={"""help""": """Training seed."""}			)
   _a    						=   field(
       default=1024							,				metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,				)
   _a    						=   field(
       default=__magic_name__							,				metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}			)
   _a    						=   field(default=__magic_name__							,				metadata={"""help""": """If True the data is pretokenized."""}			)





@dataclass
class  __lowercase    :
   _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=1024							,				metadata={"""help""": """Length of sequences to be evaluated."""}			)
   _a    						=   field(default=1							,				metadata={"""help""": """Random seed used for evaluation."""}			)





@dataclass
class  __lowercase    :
   _a    						=   field(
       default="""codeparrot/codeparrot"""							,				metadata={"""help""": """Model name or path of model to be evaluated."""}			)
   _a    						=   field(default=__magic_name__							,				metadata={"""help""": """Number of workers used for code evaluation."""}			)
   _a    						=   field(
       default=__magic_name__							,				metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,				)
   _a    						=   field(
       default=__magic_name__							,				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  __lowercase    :
   _a    						=   field(
       default=__magic_name__							,				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=100000							,				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=1000							,				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=__magic_name__							,				metadata={"""help""": """If True, near-duplicate samples are removed."""}			)
   _a    						=   field(
       default=0.85							,				metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}			)





@dataclass
class  __lowercase    :
   _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=200000							,				metadata={"""help""": """Number of examples to train tokenizer on."""}			)
   _a    						=   field(
       default=32768							,				metadata={"""help""": """Number of examples to train the tokenizer on."""}			)
   _a    						=   field(default="""codeparrot"""							,				metadata={"""help""": """Name of new tokenizer."""}			)
   _a    						=   field(default=__magic_name__							,				metadata={"""help""": """Push saved tokenizer to the hub."""}			)





@dataclass
class  __lowercase    :
   _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=__magic_name__							,				metadata={"""help""": """Number of workers used for code evaluation."""}			)





@dataclass
class  __lowercase    :
   _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=__magic_name__							,				metadata={"""help""": """Push saved tokenizer to the hub."""}			)









"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):  # This function is recursive
				_lowerCamelCase	:     Optional[Any]       =       len(lowercase__						)
				# If the array contains only one element, we return it (it's the stop condition of
				# recursion)
				if array_length <= 1:
								return array
								# Else
				_lowerCamelCase	:     Optional[Any]       =       array[0]
				_lowerCamelCase	:     Optional[int]       =       False
				_lowerCamelCase	:     List[str]       =       1
				_lowerCamelCase	:     list[int]       =       []
				while not is_found and i < array_length:
								if array[i] < pivot:
												_lowerCamelCase	:     Union[str, Any]       =       True
												_lowerCamelCase	:     str       =       [element for element in array[i:] if element >= array[i]]
												_lowerCamelCase	:     str       =       longest_subsequence(lowercase__						)
												if len(lowercase__						) > len(lowercase__						):
																_lowerCamelCase	:     Any       =       temp_array
								else:
												i += 1

				_lowerCamelCase	:     Optional[Any]       =       [element for element in array[1:] if element >= pivot]
				_lowerCamelCase	:     Dict       =       [pivot, *longest_subsequence(lowercase__						)]
				if len(lowercase__						) > len(lowercase__						):
								return temp_array
				else:
								return longest_subseq


if __name__ == "__main__":
			import doctest

			doctest.testmod()