




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

snake_case__			:    Tuple			     =      {"""vocab_file""": """spiece.model"""}

snake_case__			:    int			     =      {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

snake_case__			:    Any			     =      {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}





class      snake_case	(						__snake_case				):


   '''simple docstring'''







   UpperCamelCase__						:							Union[str, Any] 	=   VOCAB_FILES_NAMES
   UpperCamelCase__						:							Union[str, Any] 	=   PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase__						:							int 	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase__						:							List[str] 	=   ["input_ids", "attention_mask"]
   UpperCamelCase__						:							Optional[int] 	=   []




   def __init__(       self  :						str    ,     lowerCamelCase_  :						Dict    ,     lowerCamelCase_  :						int="<unk>"    ,     lowerCamelCase_  :						Dict="<s>"    ,     lowerCamelCase_  :						Union[str, Any]="</s>"    ,     lowerCamelCase_  :						Optional[Any]="<pad>"    ,     lowerCamelCase_  :						int="[SEP]"    ,     lowerCamelCase_  :						List[str]="[MASK]"    ,     lowerCamelCase_  :						Optional[int]="[CLS]"    ,     lowerCamelCase_  :						Optional[Dict[str, Any]] = None    ,     **lowerCamelCase_  :						Union[str, Any]    ,     )       ->None:
     '''simple docstring'''







     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else bos_token
     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else eos_token
     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else unk_token
     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else pad_token
     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else cls_token
     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else sep_token

     # Mask token behave like a normal word, i.e. include the space before it
     UpperCAmelCase__         = AddedToken(lowerCamelCase_    ,     lstrip=lowerCamelCase_    ,     rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ) else mask_token

     UpperCAmelCase__         = {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=lowerCamelCase_    ,     eos_token=lowerCamelCase_    ,     unk_token=lowerCamelCase_    ,     pad_token=lowerCamelCase_    ,     sep_token=lowerCamelCase_    ,     mask_token=lowerCamelCase_    ,     cls_token=lowerCamelCase_    ,     sp_model_kwargs=self.sp_model_kwargs    ,     **lowerCamelCase_    ,     )

     UpperCAmelCase__         = vocab_file

     UpperCAmelCase__         = spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(lowerCamelCase_      )




   @property
   def 						UpperCAmelCase							(       self  :						int      )       ->Optional[Any]:
     '''simple docstring'''







     return self.sp_model.get_piece_size()




   def 						UpperCAmelCase							(       self  :						Optional[Any]      )       ->List[str]:
     '''simple docstring'''







     UpperCAmelCase__         = {self.convert_ids_to_tokens(lowerCamelCase_      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab




   def __getstate__(       self  :						List[str]      )       ->Dict:
     '''simple docstring'''







     UpperCAmelCase__         = self.__dict__.copy()
     UpperCAmelCase__         = None
     return state




   def __setstate__(       self  :						Optional[Any]    ,     lowerCamelCase_  :						Union[str, Any]      )       ->List[str]:
     '''simple docstring'''







     UpperCAmelCase__         = d

     # for backward compatibility
     if not hasattr(self    ,     """sp_model_kwargs"""      ):
       UpperCAmelCase__         = {}

     UpperCAmelCase__         = spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(self.vocab_file      )




   def 						UpperCAmelCase							(       self  :						List[str]    ,     lowerCamelCase_  :						str      )       ->List[str]:
     '''simple docstring'''







     return self.sp_model.encode(lowerCamelCase_    ,     out_type=lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						List[Any]    ,     lowerCamelCase_  :						Dict      )       ->Dict:
     '''simple docstring'''







     return self.sp_model.piece_to_id(lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						Any    ,     lowerCamelCase_  :						Optional[Any]      )       ->Any:
     '''simple docstring'''







     UpperCAmelCase__         = self.sp_model.IdToPiece(lowerCamelCase_      )
     return token




   def 						UpperCAmelCase							(       self  :						Optional[Any]    ,     lowerCamelCase_  :						Dict      )       ->int:
     '''simple docstring'''







     UpperCAmelCase__         = []
     UpperCAmelCase__         = ""
     UpperCAmelCase__         = False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(lowerCamelCase_      ) + token
         UpperCAmelCase__         = True
         UpperCAmelCase__         = []
       else:
         current_sub_tokens.append(lowerCamelCase_      )
         UpperCAmelCase__         = False
     out_string += self.sp_model.decode(lowerCamelCase_      )
     return out_string.strip()




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







     UpperCAmelCase__         = kwargs.pop("""use_source_tokenizer"""    ,     lowerCamelCase_      )

     UpperCAmelCase__         = self.convert_ids_to_tokens(lowerCamelCase_    ,     skip_special_tokens=lowerCamelCase_      )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     UpperCAmelCase__         = []
     UpperCAmelCase__         = []
     for token in filtered_tokens:
       if skip_special_tokens and token in self.all_special_ids:
         continue
       if token in self.added_tokens_encoder:
         if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_      )      )
           UpperCAmelCase__         = []
         sub_texts.append(lowerCamelCase_      )
       else:
         current_sub_text.append(lowerCamelCase_      )
     if current_sub_text:
       sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_      )      )

     # Mimic the behavior of the Rust tokenizer:
     # No space before [MASK] and [SEP]
     if spaces_between_special_tokens:
       UpperCAmelCase__         = re.sub(R""" (\[(MASK|SEP)\])"""    ,     R"""\1"""    ,     """ """.join(lowerCamelCase_      )      )
     else:
       UpperCAmelCase__         = "".join(lowerCamelCase_      )

     UpperCAmelCase__         = (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
       UpperCAmelCase__         = self.clean_up_tokenization(lowerCamelCase_      )
       return clean_text
     else:
       return text




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







     if not os.path.isdir(lowerCamelCase_      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
     UpperCAmelCase__         = os.path.join(
         lowerCamelCase_    ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCamelCase_      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file    ,     lowerCamelCase_      )
     elif not os.path.isfile(self.vocab_file      ):
       with open(lowerCamelCase_    ,     """wb"""      ) as fi:
         UpperCAmelCase__         = self.sp_model.serialized_model_proto()
         fi.write(lowerCamelCase_      )

     return (out_vocab_file,)




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







     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     UpperCAmelCase__         = [self.cls_token_id]
     UpperCAmelCase__         = [self.sep_token_id]
     return cls + token_ids_a + sep + token_ids_a + sep




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







     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowerCamelCase_    ,     token_ids_a=lowerCamelCase_    ,     already_has_special_tokens=lowerCamelCase_      )

     if token_ids_a is None:
       return [1] + ([0] * len(lowerCamelCase_      )) + [1]
     return [1] + ([0] * len(lowerCamelCase_      )) + [1] + ([0] * len(lowerCamelCase_      )) + [1]







   def 						UpperCAmelCase							(       self  :						str    ,     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      ) * [0] + len(token_ids_a + sep      ) * [1]






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."}       )




