






from dataclasses import dataclass, field
from typing import Optional



@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



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





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



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





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



		_snake_case    :    Optional[str]       						=  field(
		    default='''codeparrot/codeparrot''' ,  metadata={'''help''': '''Model name or path of model to be evaluated.'''}			)
		_snake_case    :    Optional[int]       						=  field(default=__lowercase ,  metadata={'''help''': '''Number of workers used for code evaluation.'''}			)
		_snake_case    :    Optional[int]       						=  field(
		    default=__lowercase ,  metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''} ,  )
		_snake_case    :    Optional[bool]       						=  field(
		    default=__lowercase ,  metadata={'''help''': '''Sample from the language model\'s output distribution.'''}			)
		_snake_case    :    Optional[float]       						=  field(default=0.2 ,  metadata={'''help''': '''Sampling temperature used for generation.'''}			)
		_snake_case    :    Optional[int]       						=  field(default=2_5_6 ,  metadata={'''help''': '''Maximum number of newly generated tokens.'''}			)
		_snake_case    :    Optional[int]       						=  field(default=0 ,  metadata={'''help''': '''Top-k parameter used for generation.'''}			)
		_snake_case    :    Optional[float]       						=  field(default=0.95 ,  metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}			)
		_snake_case    :    Optional[int]       						=  field(default=1_0 ,  metadata={'''help''': '''Number of generations to run in parallel.'''}			)
		_snake_case    :    Optional[int]       						=  field(
		    default=2_0_0 ,  metadata={'''help''': '''Number of completions to generate for each sample.'''}			)
		_snake_case    :    Optional[int]       						=  field(default=1 ,  metadata={'''help''': '''Random seed used for evaluation.'''}			)
		_snake_case    :    Optional[str]       						=  field(
		    default='''eval_results.json''' ,  metadata={'''help''': '''Random seed used for evaluation.'''}			)
		_snake_case    :    Optional[str]       						=  field(
		    default='''0''' ,  metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}			)
		_snake_case    :    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   lowerCamelCase      :







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  field(
		    default=__lowercase ,  metadata={
		        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
		    } ,  )
		_snake_case    :    Optional[str]       						=  field(
		    default='''transformersbook/codeparrot''' ,  metadata={'''help''': '''Folder or name of dataset to process.'''}			)
		_snake_case    :    Optional[str]       						=  field(
		    default='''codeparrot-clean''' ,  metadata={'''help''': '''Folder to save processed processed dataset.'''}			)
		_snake_case    :    Optional[int]       						=  field(
		    default=1_0_0_0_0_0 ,  metadata={'''help''': '''Number of files to save per JSON output file.'''}			)
		_snake_case    :    Optional[str]       						=  field(default='''content''' ,  metadata={'''help''': '''Column containing text data to process.'''}			)
		_snake_case    :    Optional[float]       						=  field(
		    default=1_0_0_0 ,  metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}			)
		_snake_case    :    Optional[float]       						=  field(
		    default=1_0_0 ,  metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}			)
		_snake_case    :    Optional[float]       						=  field(
		    default=0.25 ,  metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}			)
		_snake_case    :    Optional[float]       						=  field(
		    default=1.5 ,  metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}			)
		_snake_case    :    Optional[float]       						=  field(
		    default=0.7 ,  metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}			)
		_snake_case    :    Optional[str]       						=  field(
		    default='''codeparrot/codeparrot''' ,  metadata={'''help''': '''Name or path to the tokenizer.'''} ,  )
		_snake_case    :    Optional[bool]       						=  field(
		    default=__lowercase ,  metadata={'''help''': '''If True, near-duplicate samples are removed.'''}			)
		_snake_case    :    Optional[float]       						=  field(
		    default=0.85 ,  metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}			)





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



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





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



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





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



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









import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



