


'''simple docstring'''







from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       for param in module.parameters():
              _A					:     Any     = False






def 				lowerCAmelCase__	(    ):
       _A					:     int     = 'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              _A					:     Any     = 'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'       )
       return device






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       _A					:     int     = plt.imshow(lowerCamelCase       )
       fig.axes.get_xaxis().set_visible(lowerCamelCase       )
       fig.axes.get_yaxis().set_visible(lowerCamelCase       )
       plt.show()






def 				lowerCAmelCase__	(    ):
       _A					:     Tuple     = datetime.now()
       _A					:     Optional[int]     = current_time.strftime('%H:%M:%S'       )
       return timestamp










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=1_0000    , 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=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , 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=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , 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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default=a_    , 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=10_0000    , 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=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=a_    , metadata={"help": "Number of workers used for code evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






