


'''simple docstring'''







import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

A				:					int    						=			logging.get_logger(__name__)


A				:					List[Any]    						=			{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      str       ):
       if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
              raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()       )}.'       )

       if tokenizer_name is None:
              _A					:     Optional[Any]     = TOKENIZER_CLASSES
       else:
              _A					:     int     = {tokenizer_name: getattr(lowerCamelCase  ,tokenizer_name + 'Fast'       )}

       logger.info(F'Loading tokenizer classes: {tokenizer_names}'       )

       for tokenizer_name in tokenizer_names:
              _A					:     str     = TOKENIZER_CLASSES[tokenizer_name]

              _A					:     Optional[int]     = True
              if checkpoint_name is None:
                     _A					:     Any     = list(tokenizer_class.max_model_input_sizes.keys()       )
              else:
                     _A					:     Any     = [checkpoint_name]

              logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'       )

              for checkpoint in checkpoint_names:
                     logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}'       )

                     # Load tokenizer
                     _A					:     Dict     = tokenizer_class.from_pretrained(lowerCamelCase  ,force_download=lowerCamelCase       )

                     # Save fast tokenizer
                     logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'       )

                     # For organization names we create sub-directories
                     if "/" in checkpoint:
                            _A  ,			_A					:     Optional[Any]     = checkpoint.split('/'       )
                            _A					:     List[Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     elif add_prefix:
                            _A					:     Union[str, Any]     = checkpoint
                            _A					:     int     = dump_path
                     else:
                            _A					:     int     = None
                            _A					:     Tuple     = dump_path

                     logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'       )

                     if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()       )[0]:
                            _A					:     Dict     = list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
                            _A					:     Dict     = file_path.split(lowerCamelCase       )[-1][0]
                            if next_char == "/":
                                   _A					:     Dict     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                                   _A					:     Tuple     = None

                            logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'       )

                     _A					:     int     = tokenizer.save_pretrained(
                         lowerCamelCase  ,legacy_format=lowerCamelCase  ,filename_prefix=lowerCamelCase       )
                     logger.info(F'=> File names {file_names}'       )

                     for file_name in file_names:
                            if not file_name.endswith('tokenizer.json'       ):
                                   os.remove(lowerCamelCase       )
                                   logger.info(F'=> removing {file_name}'       )


if __name__ == "__main__":
   A				:					str    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           f"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   A				:					Union[str, Any]    						=			parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=1_0000    , 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=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , 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=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , 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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default=a_    , 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=10_0000    , 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=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=a_    , metadata={"help": "Number of workers used for code evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    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=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






