


'''simple docstring'''






from dataclasses import dataclass, field
from typing import Optional




@dataclass
class 		a__    :





							'''simple docstring'''






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




@dataclass
class 		a__    :





							'''simple docstring'''






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




@dataclass
class 		a__    :





							'''simple docstring'''






							lowercase__      :       Optional[str]     						=				field(
							    default="codeparrot/codeparrot"    ,    metadata={"help": "Model name or path of model to be evaluated."}  )
							lowercase__      :       Optional[int]     						=				field(default=a__    ,    metadata={"help": "Number of workers used for code evaluation."}  )
							lowercase__      :       Optional[int]     						=				field(
							    default=a__    ,    metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    ,    )
							lowercase__      :       Optional[bool]     						=				field(
							    default=a__    ,    metadata={"help": "Sample from the language model's output distribution."}  )
							lowercase__      :       Optional[float]     						=				field(default=0.2    ,    metadata={"help": "Sampling temperature used for generation."}  )
							lowercase__      :       Optional[int]     						=				field(default=2_5_6    ,    metadata={"help": "Maximum number of newly generated tokens."}  )
							lowercase__      :       Optional[int]     						=				field(default=0    ,    metadata={"help": "Top-k parameter used for generation."}  )
							lowercase__      :       Optional[float]     						=				field(default=0.9_5    ,    metadata={"help": "Top-p parameter used for nucleus sampling."}  )
							lowercase__      :       Optional[int]     						=				field(default=1_0    ,    metadata={"help": "Number of generations to run in parallel."}  )
							lowercase__      :       Optional[int]     						=				field(
							    default=2_0_0    ,    metadata={"help": "Number of completions to generate for each sample."}  )
							lowercase__      :       Optional[int]     						=				field(default=1    ,    metadata={"help": "Random seed used for evaluation."}  )
							lowercase__      :       Optional[str]     						=				field(
							    default="eval_results.json"    ,    metadata={"help": "Random seed used for evaluation."}  )
							lowercase__      :       Optional[str]     						=				field(
							    default="0"    ,    metadata={"help": "Allow `code_eval` to execute Python code on machine"}  )
							lowercase__      :       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'''






							lowercase__      :       Optional[int]     						=				field(
							    default=a__    ,    metadata={
							        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
							    }    ,    )
							lowercase__      :       Optional[str]     						=				field(
							    default="transformersbook/codeparrot"    ,    metadata={"help": "Folder or name of dataset to process."}  )
							lowercase__      :       Optional[str]     						=				field(
							    default="codeparrot-clean"    ,    metadata={"help": "Folder to save processed processed dataset."}  )
							lowercase__      :       Optional[int]     						=				field(
							    default=1_0_0_0_0_0    ,    metadata={"help": "Number of files to save per JSON output file."}  )
							lowercase__      :       Optional[str]     						=				field(default="content"    ,    metadata={"help": "Column containing text data to process."}  )
							lowercase__      :       Optional[float]     						=				field(
							    default=1_0_0_0    ,    metadata={"help": "Maximum line length in file, otherwise file is filtered."}  )
							lowercase__      :       Optional[float]     						=				field(
							    default=1_0_0    ,    metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}  )
							lowercase__      :       Optional[float]     						=				field(
							    default=0.2_5    ,    metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}  )
							lowercase__      :       Optional[float]     						=				field(
							    default=1.5    ,    metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}  )
							lowercase__      :       Optional[float]     						=				field(
							    default=0.7    ,    metadata={"help": "Probability for filtering config, test and uncommon files."}  )
							lowercase__      :       Optional[str]     						=				field(
							    default="codeparrot/codeparrot"    ,    metadata={"help": "Name or path to the tokenizer."}    ,    )
							lowercase__      :       Optional[bool]     						=				field(
							    default=a__    ,    metadata={"help": "If True, near-duplicate samples are removed."}  )
							lowercase__      :       Optional[float]     						=				field(
							    default=0.8_5    ,    metadata={"help": "Jaccard threshold for near-duplicate samples."}  )




@dataclass
class 		a__    :





							'''simple docstring'''






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




@dataclass
class 		a__    :





							'''simple docstring'''






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




@dataclass
class 		a__    :





							'''simple docstring'''






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



'''simple docstring'''






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

							for _ in range(A	):
														# Approximates curve as a sequence of linear lines and sums their length
														lowerCAmelCase__	       =     (x_end - x_start) / steps + xa
														lowerCAmelCase__	       =     fnc(A	)
														length += math.hypot(xa - xa			,   fxa - fxa	)

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10