

'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class        a__  :
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='codeparrot/codeparrot'  ,				metadata={'help': 'Model name or path of model to be trained.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='./'  ,				metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='codeparrot/codeparrot-clean-train'  ,				metadata={'help': 'Name or path of training dataset.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='codeparrot/codeparrot-clean-valid'  ,				metadata={'help': 'Name or path of validation dataset.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=2  ,				metadata={'help': 'Batch size for training.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=2  ,				metadata={'help': 'Batch size for evaluation.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(default=0.1  ,				metadata={'help': 'Value of weight decay.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=1_0000  ,				metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(default=2E-4  ,				metadata={'help': 'Learning rate fo training.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(default='cosine'  ,				metadata={'help': 'Learning rate.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=750  ,				metadata={'help': 'Number of warmup steps in the learning rate schedule.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=16  ,				metadata={'help': 'Number of gradient accumulation steps.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[bool]     	=							field(
       default=__lowerCAmelCase  ,				metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=5_0000  ,				metadata={'help': 'Maximum number of training steps.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=-1  ,				metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=1024  ,				metadata={'help': 'Sequence lengths used for training.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=1  ,				metadata={'help': 'Training seed.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=1024  ,				metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}  ,				)
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default=__lowerCAmelCase  ,				metadata={'help': 'States path if the training should continue from a checkpoint folder.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[bool]     	=							field(default=__lowerCAmelCase  ,				metadata={'help': 'If True the data is pretokenized.'}  )




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




@dataclass
class        a__  :
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='codeparrot/codeparrot'  ,				metadata={'help': 'Model name or path of model to be evaluated.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=__lowerCAmelCase  ,				metadata={'help': 'Number of workers used for code evaluation.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=__lowerCAmelCase  ,				metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}  ,				)
   _SCREAMING_SNAKE_CASE   :					Optional[bool]     	=							field(
       default=__lowerCAmelCase  ,				metadata={'help': 'Sample from the language model\'s output distribution.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(default=0.2  ,				metadata={'help': 'Sampling temperature used for generation.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=256  ,				metadata={'help': 'Maximum number of newly generated tokens.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=0  ,				metadata={'help': 'Top-k parameter used for generation.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(default=0.95  ,				metadata={'help': 'Top-p parameter used for nucleus sampling.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=10  ,				metadata={'help': 'Number of generations to run in parallel.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=200  ,				metadata={'help': 'Number of completions to generate for each sample.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=1  ,				metadata={'help': 'Random seed used for evaluation.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='eval_results.json'  ,				metadata={'help': 'Random seed used for evaluation.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='0'  ,				metadata={'help': 'Allow `code_eval` to execute Python code on machine'}  )
   _SCREAMING_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        a__  :
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=__lowerCAmelCase  ,				metadata={
           'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
       }  ,				)
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='transformersbook/codeparrot'  ,				metadata={'help': 'Folder or name of dataset to process.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='codeparrot-clean'  ,				metadata={'help': 'Folder to save processed processed dataset.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=10_0000  ,				metadata={'help': 'Number of files to save per JSON output file.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(default='content'  ,				metadata={'help': 'Column containing text data to process.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(
       default=1000  ,				metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(
       default=100  ,				metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(
       default=0.25  ,				metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(
       default=1.5  ,				metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(
       default=0.7  ,				metadata={'help': 'Probability for filtering config, test and uncommon files.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='codeparrot/codeparrot'  ,				metadata={'help': 'Name or path to the tokenizer.'}  ,				)
   _SCREAMING_SNAKE_CASE   :					Optional[bool]     	=							field(
       default=__lowerCAmelCase  ,				metadata={'help': 'If True, near-duplicate samples are removed.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[float]     	=							field(
       default=0.85  ,				metadata={'help': 'Jaccard threshold for near-duplicate samples.'}  )




@dataclass
class        a__  :
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='gpt2'  ,				metadata={'help': 'Base tokenizer to build new tokenizer from.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(
       default='transformersbook/codeparrot-train'  ,				metadata={'help': 'Dataset to train tokenizer on.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(default='content'  ,				metadata={'help': 'Column containing text data to process.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(default=20_0000  ,				metadata={'help': 'Number of examples to train tokenizer on.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							field(
       default=3_2768  ,				metadata={'help': 'Number of examples to train the tokenizer on.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[str]     	=							field(default='codeparrot'  ,				metadata={'help': 'Name of new tokenizer.'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[bool]     	=							field(default=__lowerCAmelCase  ,				metadata={'help': 'Push saved tokenizer to the hub.'}  )




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




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









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






