




"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional



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






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






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






@dataclass
class      a__				:
   lowercase_    =	field(
       default="gpt2"							,    metadata={"help": "Base tokenizer to build new tokenizer from."}   )
   lowercase_    =	field(
       default="transformersbook/codeparrot-train"							,    metadata={"help": "Dataset to train tokenizer on."}   )
   lowercase_    =	field(default="content"							,    metadata={"help": "Column containing text data to process."}   )
   lowercase_    =	field(default=2_0_0_0_0_0							,    metadata={"help": "Number of examples to train tokenizer on."}   )
   lowercase_    =	field(
       default=3_2_7_6_8							,    metadata={"help": "Number of examples to train the tokenizer on."}   )
   lowercase_    =	field(default="codeparrot"							,    metadata={"help": "Name of new tokenizer."}   )
   lowercase_    =	field(default=__magic_name__							,    metadata={"help": "Push saved tokenizer to the hub."}   )






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






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








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


