




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


snake_case__			:    Optional[Any]			     =      logging.get_logger(__name__)

snake_case__			:    Dict			     =      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

snake_case__			:    Any			     =      {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

snake_case__			:    Tuple			     =      {
    """facebook/blenderbot_small-90M""": 5_1_2,
}





class      snake_case	(						__snake_case				):


   '''simple docstring'''







   UpperCamelCase__						:							Union[str, Any] 	=   VOCAB_FILES_NAMES
   UpperCamelCase__						:							int 	=   PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase__						:							List[Any] 	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase__						:							str 	=   BlenderbotSmallTokenizer




   def __init__(       self  :						Optional[Any]    ,     lowerCamelCase_  :						Tuple=None    ,     lowerCamelCase_  :						Optional[Any]=None    ,     lowerCamelCase_  :						Union[str, Any]="<|endoftext|>"    ,     lowerCamelCase_  :						Any="<|endoftext|>"    ,     lowerCamelCase_  :						Dict="<|endoftext|>"    ,     lowerCamelCase_  :						Any=False    ,     lowerCamelCase_  :						Optional[int]=True    ,     **lowerCamelCase_  :						int    ,     )       ->Tuple:
     '''simple docstring'''







     super().__init__(
         ByteLevelBPETokenizer(
             vocab=lowerCamelCase_    ,     merges=lowerCamelCase_    ,     add_prefix_space=lowerCamelCase_    ,     trim_offsets=lowerCamelCase_    ,     )    ,     bos_token=lowerCamelCase_    ,     eos_token=lowerCamelCase_    ,     unk_token=lowerCamelCase_    ,     **lowerCamelCase_    ,     )
     UpperCAmelCase__         = add_prefix_space




   def 						UpperCAmelCase							(       self  :						str    ,     lowerCamelCase_  :						Any    ,     lowerCamelCase_  :						str=None      )       ->Dict:
     '''simple docstring'''







     UpperCAmelCase__         = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 						UpperCAmelCase							(       self  :						Union[str, Any]    ,     lowerCamelCase_  :						List[int]    ,     lowerCamelCase_  :						Optional[List[int]] = None      )       ->List[int]:
     '''simple docstring'''







     UpperCAmelCase__         = [self.sep_token_id]
     UpperCAmelCase__         = [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






from dataclasses import dataclass, field
from typing import Optional




@dataclass
class   __lowercase						:
	_A       =   field(
	    default="codeparrot/codeparrot"   , metadata={"help": "Model name or path of model to be trained."}       )
	_A       =   field(
	    default="./"   , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}       )
	_A       =   field(
	    default="codeparrot/codeparrot-clean-train"   , metadata={"help": "Name or path of training dataset."}       )
	_A       =   field(
	    default="codeparrot/codeparrot-clean-valid"   , metadata={"help": "Name or path of validation dataset."}       )
	_A       =   field(default=2   , metadata={"help": "Batch size for training."}       )
	_A       =   field(default=2   , metadata={"help": "Batch size for evaluation."}       )
	_A       =   field(default=0.1   , metadata={"help": "Value of weight decay."}       )
	_A       =   field(
	    default=10000   , metadata={"help": "Size of buffer used to shuffle streaming dataset."}       )
	_A       =   field(default=2e-4   , metadata={"help": "Learning rate fo training."}       )
	_A       =   field(default="cosine"   , metadata={"help": "Learning rate."}       )
	_A       =   field(
	    default=750   , metadata={"help": "Number of warmup steps in the learning rate schedule."}       )
	_A       =   field(
	    default=16   , metadata={"help": "Number of gradient accumulation steps."}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}       )
	_A       =   field(default=50000   , metadata={"help": "Maximum number of training steps."}       )
	_A       =   field(
	    default=-1   , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}       )
	_A       =   field(default=1024   , metadata={"help": "Sequence lengths used for training."}       )
	_A       =   field(default=1   , metadata={"help": "Training seed."}       )
	_A       =   field(
	    default=1024   , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}   , )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "States path if the training should continue from a checkpoint folder."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "If True the data is pretokenized."}       )




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




@dataclass
class   __lowercase						:
	_A       =   field(
	    default="codeparrot/codeparrot"   , metadata={"help": "Model name or path of model to be evaluated."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Number of workers used for code evaluation."}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}   , )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Sample from the language model's output distribution."}       )
	_A       =   field(default=0.2   , metadata={"help": "Sampling temperature used for generation."}       )
	_A       =   field(default=256   , metadata={"help": "Maximum number of newly generated tokens."}       )
	_A       =   field(default=0   , metadata={"help": "Top-k parameter used for generation."}       )
	_A       =   field(default=0.95   , metadata={"help": "Top-p parameter used for nucleus sampling."}       )
	_A       =   field(default=10   , metadata={"help": "Number of generations to run in parallel."}       )
	_A       =   field(
	    default=200   , metadata={"help": "Number of completions to generate for each sample."}       )
	_A       =   field(default=1   , metadata={"help": "Random seed used for evaluation."}       )
	_A       =   field(
	    default="eval_results.json"   , metadata={"help": "Random seed used for evaluation."}       )
	_A       =   field(
	    default="0"   , metadata={"help": "Allow `code_eval` to execute Python code on machine"}       )
	_A       =   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						:
	_A       =   field(
	    default=__snake_case   , metadata={
	        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
	    }   , )
	_A       =   field(
	    default="transformersbook/codeparrot"   , metadata={"help": "Folder or name of dataset to process."}       )
	_A       =   field(
	    default="codeparrot-clean"   , metadata={"help": "Folder to save processed processed dataset."}       )
	_A       =   field(
	    default=100000   , metadata={"help": "Number of files to save per JSON output file."}       )
	_A       =   field(default="content"   , metadata={"help": "Column containing text data to process."}       )
	_A       =   field(
	    default=1000   , metadata={"help": "Maximum line length in file, otherwise file is filtered."}       )
	_A       =   field(
	    default=100   , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}       )
	_A       =   field(
	    default=0.25   , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}       )
	_A       =   field(
	    default=1.5   , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}       )
	_A       =   field(
	    default=0.7   , metadata={"help": "Probability for filtering config, test and uncommon files."}       )
	_A       =   field(
	    default="codeparrot/codeparrot"   , metadata={"help": "Name or path to the tokenizer."}   , )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "If True, near-duplicate samples are removed."}       )
	_A       =   field(
	    default=0.85   , metadata={"help": "Jaccard threshold for near-duplicate samples."}       )




@dataclass
class   __lowercase						:
	_A       =   field(
	    default="gpt2"   , metadata={"help": "Base tokenizer to build new tokenizer from."}       )
	_A       =   field(
	    default="transformersbook/codeparrot-train"   , metadata={"help": "Dataset to train tokenizer on."}       )
	_A       =   field(default="content"   , metadata={"help": "Column containing text data to process."}       )
	_A       =   field(default=200000   , metadata={"help": "Number of examples to train tokenizer on."}       )
	_A       =   field(
	    default=32768   , metadata={"help": "Number of examples to train the tokenizer on."}       )
	_A       =   field(default="codeparrot"   , metadata={"help": "Name of new tokenizer."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Push saved tokenizer to the hub."}       )




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




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




