


from dataclasses import dataclass, field
from typing import Optional





@dataclass
class  _a							:
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot'''					,						metadata={'''help''': '''Model name or path of model to be trained.'''})
	_a  :  Optional[str]				   =							field(
	    default='''./'''					,						metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''})
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot-clean-train'''					,						metadata={'''help''': '''Name or path of training dataset.'''})
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot-clean-valid'''					,						metadata={'''help''': '''Name or path of validation dataset.'''})
	_a  :  Optional[int]				   =							field(default=2					,						metadata={'''help''': '''Batch size for training.'''})
	_a  :  Optional[int]				   =							field(default=2					,						metadata={'''help''': '''Batch size for evaluation.'''})
	_a  :  Optional[float]				   =							field(default=0.1					,						metadata={'''help''': '''Value of weight decay.'''})
	_a  :  Optional[int]				   =							field(
	    default=10000					,						metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''})
	_a  :  Optional[float]				   =							field(default=2E-4					,						metadata={'''help''': '''Learning rate fo training.'''})
	_a  :  Optional[str]				   =							field(default='''cosine'''					,						metadata={'''help''': '''Learning rate.'''})
	_a  :  Optional[int]				   =							field(
	    default=750					,						metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''})
	_a  :  Optional[int]				   =							field(
	    default=16					,						metadata={'''help''': '''Number of gradient accumulation steps.'''})
	_a  :  Optional[bool]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''})
	_a  :  Optional[int]				   =							field(default=50000					,						metadata={'''help''': '''Maximum number of training steps.'''})
	_a  :  Optional[int]				   =							field(
	    default=-1					,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''})
	_a  :  Optional[int]				   =							field(default=1024					,						metadata={'''help''': '''Sequence lengths used for training.'''})
	_a  :  Optional[int]				   =							field(default=1					,						metadata={'''help''': '''Training seed.'''})
	_a  :  Optional[int]				   =							field(
	    default=1024					,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}					,						)
	_a  :  Optional[str]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''})
	_a  :  Optional[bool]				   =							field(default=_lowercase					,						metadata={'''help''': '''If True the data is pretokenized.'''})





@dataclass
class  _a							:
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot'''					,						metadata={'''help''': '''Model name or path of model to be evaluated.'''})
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot-clean-valid'''					,						metadata={'''help''': '''Name or path of validation dataset.'''})
	_a  :  Optional[int]				   =							field(default=2					,						metadata={'''help''': '''Batch size used for evaluation.'''})
	_a  :  Optional[int]				   =							field(
	    default=-1					,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''})
	_a  :  Optional[int]				   =							field(default=1024					,						metadata={'''help''': '''Length of sequences to be evaluated.'''})
	_a  :  Optional[int]				   =							field(default=1					,						metadata={'''help''': '''Random seed used for evaluation.'''})





@dataclass
class  _a							:
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot'''					,						metadata={'''help''': '''Model name or path of model to be evaluated.'''})
	_a  :  Optional[int]				   =							field(default=_lowercase					,						metadata={'''help''': '''Number of workers used for code evaluation.'''})
	_a  :  Optional[int]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}					,						)
	_a  :  Optional[bool]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''Sample from the language model\'s output distribution.'''})
	_a  :  Optional[float]				   =							field(default=0.2					,						metadata={'''help''': '''Sampling temperature used for generation.'''})
	_a  :  Optional[int]				   =							field(default=256					,						metadata={'''help''': '''Maximum number of newly generated tokens.'''})
	_a  :  Optional[int]				   =							field(default=0					,						metadata={'''help''': '''Top-k parameter used for generation.'''})
	_a  :  Optional[float]				   =							field(default=0.95					,						metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''})
	_a  :  Optional[int]				   =							field(default=10					,						metadata={'''help''': '''Number of generations to run in parallel.'''})
	_a  :  Optional[int]				   =							field(
	    default=200					,						metadata={'''help''': '''Number of completions to generate for each sample.'''})
	_a  :  Optional[int]				   =							field(default=1					,						metadata={'''help''': '''Random seed used for evaluation.'''})
	_a  :  Optional[str]				   =							field(
	    default='''eval_results.json'''					,						metadata={'''help''': '''Random seed used for evaluation.'''})
	_a  :  Optional[str]				   =							field(
	    default='''0'''					,						metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''})
	_a  :  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							:
	_a  :  Optional[int]				   =							field(
	    default=_lowercase					,						metadata={
	        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
	    }					,						)
	_a  :  Optional[str]				   =							field(
	    default='''transformersbook/codeparrot'''					,						metadata={'''help''': '''Folder or name of dataset to process.'''})
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot-clean'''					,						metadata={'''help''': '''Folder to save processed processed dataset.'''})
	_a  :  Optional[int]				   =							field(
	    default=100000					,						metadata={'''help''': '''Number of files to save per JSON output file.'''})
	_a  :  Optional[str]				   =							field(default='''content'''					,						metadata={'''help''': '''Column containing text data to process.'''})
	_a  :  Optional[float]				   =							field(
	    default=1000					,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''})
	_a  :  Optional[float]				   =							field(
	    default=100					,						metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''})
	_a  :  Optional[float]				   =							field(
	    default=0.25					,						metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''})
	_a  :  Optional[float]				   =							field(
	    default=1.5					,						metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''})
	_a  :  Optional[float]				   =							field(
	    default=0.7					,						metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''})
	_a  :  Optional[str]				   =							field(
	    default='''codeparrot/codeparrot'''					,						metadata={'''help''': '''Name or path to the tokenizer.'''}					,						)
	_a  :  Optional[bool]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''If True, near-duplicate samples are removed.'''})
	_a  :  Optional[float]				   =							field(
	    default=0.85					,						metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''})





@dataclass
class  _a							:
	_a  :  Optional[str]				   =							field(
	    default='''gpt2'''					,						metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''})
	_a  :  Optional[str]				   =							field(
	    default='''transformersbook/codeparrot-train'''					,						metadata={'''help''': '''Dataset to train tokenizer on.'''})
	_a  :  Optional[str]				   =							field(default='''content'''					,						metadata={'''help''': '''Column containing text data to process.'''})
	_a  :  Optional[int]				   =							field(default=200000					,						metadata={'''help''': '''Number of examples to train tokenizer on.'''})
	_a  :  Optional[int]				   =							field(
	    default=32768					,						metadata={'''help''': '''Number of examples to train the tokenizer on.'''})
	_a  :  Optional[str]				   =							field(default='''codeparrot'''					,						metadata={'''help''': '''Name of new tokenizer.'''})
	_a  :  Optional[bool]				   =							field(default=_lowercase					,						metadata={'''help''': '''Push saved tokenizer to the hub.'''})





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





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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




