



from dataclasses import dataclass, field
from typing import Optional



@dataclass
class 				lowerCAmelCase_		:
       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=a__       ,    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=a__       ,    metadata={"help": "States path if the training should continue from a checkpoint folder."}							)
       UpperCAmelCase__				:					Optional[bool]        =  field(default=a__       ,    metadata={"help": "If True the data is pretokenized."}							)






@dataclass
class 				lowerCAmelCase_		:
       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 				lowerCAmelCase_		:
       UpperCAmelCase__				:					Optional[str]        =  field(
           default="codeparrot/codeparrot"       ,    metadata={"help": "Model name or path of model to be evaluated."}							)
       UpperCAmelCase__				:					Optional[int]        =  field(default=a__       ,    metadata={"help": "Number of workers used for code evaluation."}							)
       UpperCAmelCase__				:					Optional[int]        =  field(
           default=a__       ,    metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}       ,    )
       UpperCAmelCase__				:					Optional[bool]        =  field(
           default=a__       ,    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 				lowerCAmelCase_		:
       UpperCAmelCase__				:					Optional[int]        =  field(
           default=a__       ,    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=a__       ,    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 				lowerCAmelCase_		:
       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=a__       ,    metadata={"help": "Push saved tokenizer to the hub."}							)






@dataclass
class 				lowerCAmelCase_		:
       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=a__       ,    metadata={"help": "Number of workers used for code evaluation."}							)






@dataclass
class 				lowerCAmelCase_		:
       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=a__       ,    metadata={"help": "Push saved tokenizer to the hub."}							)








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


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



