




"""simple docstring"""


from dataclasses import dataclass, field
from typing import Optional






@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Model name or path of model to be trained."}				)
 lowercase__				      =      field(
     default="./" ,    metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-train" ,    metadata={"help": "Name or path of training dataset."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-valid" ,    metadata={"help": "Name or path of validation dataset."}				)
 lowercase__				      =      field(default=2 ,    metadata={"help": "Batch size for training."}				)
 lowercase__				      =      field(default=2 ,    metadata={"help": "Batch size for evaluation."}				)
 lowercase__				      =      field(default=0.1 ,    metadata={"help": "Value of weight decay."}				)
 lowercase__				      =      field(
     default=1_00_00 ,    metadata={"help": "Size of buffer used to shuffle streaming dataset."}				)
 lowercase__				      =      field(default=2e-4 ,    metadata={"help": "Learning rate fo training."}				)
 lowercase__				      =      field(default="cosine" ,    metadata={"help": "Learning rate."}				)
 lowercase__				      =      field(
     default=7_50 ,    metadata={"help": "Number of warmup steps in the learning rate schedule."}				)
 lowercase__				      =      field(
     default=16 ,    metadata={"help": "Number of gradient accumulation steps."}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Use gradient checkpointing to reduce memory footprint."}				)
 lowercase__				      =      field(default=5_00_00 ,    metadata={"help": "Maximum number of training steps."}				)
 lowercase__				      =      field(
     default=-1 ,    metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}				)
 lowercase__				      =      field(default=10_24 ,    metadata={"help": "Sequence lengths used for training."}				)
 lowercase__				      =      field(default=1 ,    metadata={"help": "Training seed."}				)
 lowercase__				      =      field(
     default=10_24 ,    metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."} ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "States path if the training should continue from a checkpoint folder."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "If True the data is pretokenized."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Model name or path of model to be evaluated."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-valid" ,    metadata={"help": "Name or path of validation dataset."}				)
 lowercase__				      =      field(default=2 ,    metadata={"help": "Batch size used for evaluation."}				)
 lowercase__				      =      field(
     default=-1 ,    metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}				)
 lowercase__				      =      field(default=10_24 ,    metadata={"help": "Length of sequences to be evaluated."}				)
 lowercase__				      =      field(default=1 ,    metadata={"help": "Random seed used for evaluation."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Model name or path of model to be evaluated."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Number of workers used for code evaluation."}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."} ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Sample from the language model's output distribution."}				)
 lowercase__				      =      field(default=0.2 ,    metadata={"help": "Sampling temperature used for generation."}				)
 lowercase__				      =      field(default=2_56 ,    metadata={"help": "Maximum number of newly generated tokens."}				)
 lowercase__				      =      field(default=0 ,    metadata={"help": "Top-k parameter used for generation."}				)
 lowercase__				      =      field(default=0.95 ,    metadata={"help": "Top-p parameter used for nucleus sampling."}				)
 lowercase__				      =      field(default=10 ,    metadata={"help": "Number of generations to run in parallel."}				)
 lowercase__				      =      field(
     default=2_00 ,    metadata={"help": "Number of completions to generate for each sample."}				)
 lowercase__				      =      field(default=1 ,    metadata={"help": "Random seed used for evaluation."}				)
 lowercase__				      =      field(
     default="eval_results.json" ,    metadata={"help": "Random seed used for evaluation."}				)
 lowercase__				      =      field(
     default="0" ,    metadata={"help": "Allow `code_eval` to execute Python code on machine"}				)
 lowercase__				      =      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       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default=__a ,    metadata={
         "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
     } ,    )
 lowercase__				      =      field(
     default="transformersbook/codeparrot" ,    metadata={"help": "Folder or name of dataset to process."}				)
 lowercase__				      =      field(
     default="codeparrot-clean" ,    metadata={"help": "Folder to save processed processed dataset."}				)
 lowercase__				      =      field(
     default=10_00_00 ,    metadata={"help": "Number of files to save per JSON output file."}				)
 lowercase__				      =      field(default="content" ,    metadata={"help": "Column containing text data to process."}				)
 lowercase__				      =      field(
     default=10_00 ,    metadata={"help": "Maximum line length in file, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=1_00 ,    metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=0.25 ,    metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=1.5 ,    metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=0.7 ,    metadata={"help": "Probability for filtering config, test and uncommon files."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Name or path to the tokenizer."} ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "If True, near-duplicate samples are removed."}				)
 lowercase__				      =      field(
     default=0.85 ,    metadata={"help": "Jaccard threshold for near-duplicate samples."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="gpt2" ,    metadata={"help": "Base tokenizer to build new tokenizer from."}				)
 lowercase__				      =      field(
     default="transformersbook/codeparrot-train" ,    metadata={"help": "Dataset to train tokenizer on."}				)
 lowercase__				      =      field(default="content" ,    metadata={"help": "Column containing text data to process."}				)
 lowercase__				      =      field(default=20_00_00 ,    metadata={"help": "Number of examples to train tokenizer on."}				)
 lowercase__				      =      field(
     default=3_27_68 ,    metadata={"help": "Number of examples to train the tokenizer on."}				)
 lowercase__				      =      field(default="codeparrot" ,    metadata={"help": "Name of new tokenizer."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Push saved tokenizer to the hub."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Name or path to the tokenizer."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-train" ,    metadata={"help": "Name or path to the dataset to pretokenize."}				)
 lowercase__				      =      field(
     default="tokenized-codeparrot-train" ,    metadata={"help": "Repo name of the pretokenized data."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Number of workers used for code evaluation."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="gpt2-large" ,    metadata={"help": "Configuration to use for model initialization."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Tokenizer attached to model."}				)
 lowercase__				      =      field(default="codeparrot" ,    metadata={"help": "Name of the created model."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Push saved tokenizer to the hub."}				)








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


A_      			=       logging.get_logger(__name__)

A_      			=       {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "roformer"


 def __init__(       self:	List[str],   a_:	Tuple=50_000,   a_:	Optional[Any]=None,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	Optional[int]=12,   a_:	Optional[Any]=3_072,   a_:	List[str]="gelu",   a_:	List[str]=0.1,   a_:	Tuple=0.1,   a_:	Optional[int]=1_536,   a_:	Any=2,   a_:	Optional[int]=0.02,   a_:	Tuple=1E-12,   a_:	Dict=0,   a_:	str=False,   a_:	Dict=True,   **a_:	Dict,   ):




   '''simple docstring'''



   super().__init__(pad_token_id=a_,   **a_	)

   _snake_case							:	int			=						vocab_size
   _snake_case							:	int			=						hidden_size if embedding_size is None else embedding_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	Any			=						num_attention_heads
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[Any]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	List[Any]			=						initializer_range
   _snake_case							:	List[Any]			=						layer_norm_eps
   _snake_case							:	Optional[Any]			=						rotary_value
   _snake_case							:	List[str]			=						use_cache







class       lowercase(  __a				):







 '''simple docstring'''





 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   if self.task == "multiple-choice":
     _snake_case							:	str			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     _snake_case							:	List[str]			=						{0: """batch""", 1: """sequence"""}
   _snake_case							:	List[Any]			=						{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ]	)


