


'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional







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







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







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







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







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







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






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__:    Dict					    =       logging.get_logger(__name__)

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







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  Tuple	       =							"roc_bert"






      def __init__( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:List[str]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Dict=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[Any]="gelu"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.1     ,						SCREAMING_SNAKE_CASE			:List[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=1e-12     ,						SCREAMING_SNAKE_CASE			:Any=True     ,						SCREAMING_SNAKE_CASE			:List[Any]=0     ,						SCREAMING_SNAKE_CASE			:Optional[int]="absolute"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						SCREAMING_SNAKE_CASE			:int=True     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=9_1_0     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=5_1_2     ,						SCREAMING_SNAKE_CASE			:str=2_4_8_5_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					Optional[int]:




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


