"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional





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






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






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






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






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






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


__lowerCAmelCase    :       List[str]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       str					     ={
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Tuple							  =	'dpr'

      def __init__(						self		,       __lowerCAmelCase=3_0522		,       __lowerCAmelCase=768		,       __lowerCAmelCase=12		,       __lowerCAmelCase=12		,       __lowerCAmelCase=3072		,       __lowerCAmelCase="gelu"		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=512		,       __lowerCAmelCase=2		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-12		,       __lowerCAmelCase=0		,       __lowerCAmelCase="absolute"		,       __lowerCAmelCase = 0		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



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

            lowercase														=	vocab_size
            lowercase														=	hidden_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	intermediate_size
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_probs_dropout_prob
            lowercase														=	max_position_embeddings
            lowercase														=	type_vocab_size
            lowercase														=	initializer_range
            lowercase														=	layer_norm_eps
            lowercase														=	projection_dim
            lowercase														=	position_embedding_type



