



'''simple docstring'''





from dataclasses import dataclass, field
from typing import Optional







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    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_00_00		,		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_50		,		metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    lowercase					=		field(
        default=16		,		metadata={"help": "Number of gradient accumulation steps."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    lowercase					=		field(default=5_00_00		,		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=10_24		,		metadata={"help": "Sequence lengths used for training."}	)
    lowercase					=		field(default=1		,		metadata={"help": "Training seed."}	)
    lowercase					=		field(
        default=10_24		,		metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,		)
    lowercase					=		field(
        default=_a		,		metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    lowercase					=		field(default=_a		,		metadata={"help": "If True the data is pretokenized."}	)







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    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=10_24		,		metadata={"help": "Length of sequences to be evaluated."}	)
    lowercase					=		field(default=1		,		metadata={"help": "Random seed used for evaluation."}	)







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    lowercase					=		field(
        default="codeparrot/codeparrot"		,		metadata={"help": "Model name or path of model to be evaluated."}	)
    lowercase					=		field(default=_a		,		metadata={"help": "Number of workers used for code evaluation."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,		)
    lowercase					=		field(
        default=_a		,		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_56		,		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=10		,		metadata={"help": "Number of generations to run in parallel."}	)
    lowercase					=		field(
        default=2_00		,		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     UpperCAmelCase_       :
    """simple docstring"""







    lowercase					=		field(
        default=_a		,		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=10_00_00		,		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=10_00		,		metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    lowercase					=		field(
        default=1_00		,		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=_a		,		metadata={"help": "If True, near-duplicate samples are removed."}	)
    lowercase					=		field(
        default=0.85		,		metadata={"help": "Jaccard threshold for near-duplicate samples."}	)







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    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=20_00_00		,		metadata={"help": "Number of examples to train tokenizer on."}	)
    lowercase					=		field(
        default=3_27_68		,		metadata={"help": "Number of examples to train the tokenizer on."}	)
    lowercase					=		field(default="codeparrot"		,		metadata={"help": "Name of new tokenizer."}	)
    lowercase					=		field(default=_a		,		metadata={"help": "Push saved tokenizer to the hub."}	)







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    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=_a		,		metadata={"help": "Number of workers used for code evaluation."}	)







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    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=_a		,		metadata={"help": "Push saved tokenizer to the hub."}	)






import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """title""": (
          """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
          """capacitance, volumetric capacitance, and energy density"""
      ),
      """journal""": """Chem. Mater.""",
      """volume""": 30,
      """pages""": """3979-3990""",
      """year""": 2018,
      """hl""": """en""",
  }
  print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


