
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

lowercase_       :						Optional[int]     					=	{'''vocab_file''': '''spiece.model'''}

lowercase_       :						Optional[Any]     					=	{
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

lowercase_       :						List[str]     					=	{'''bert_for_seq_generation''': 5_12}
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			VOCAB_FILES_NAMES
     A__      =			PRETRAINED_VOCAB_FILES_MAP
     A__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A__      =			[]
     A__      =			["""input_ids""", """attention_mask"""]
     def __init__(     self  ,     snake_case__  ,     snake_case__="<s>"  ,     snake_case__="</s>"  ,     snake_case__="<unk>"  ,     snake_case__="<pad>"  ,     snake_case__="<::::>"  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = {} if sp_model_kwargs is None else sp_model_kwargs

       # Add extra_ids to the special token list
       super().__init__(
           bos_token=snake_case__  ,     eos_token=snake_case__  ,     unk_token=snake_case__  ,     pad_token=snake_case__  ,     sep_token=snake_case__  ,     sp_model_kwargs=self.sp_model_kwargs  ,     **snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				str							    = vocab_file

       _SCREAMING_SNAKE_CASE   :				Any							    = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(snake_case__				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.sp_model.get_piece_size()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = {self.convert_ids_to_tokens(snake_case__				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab
     def __getstate__(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.__dict__.copy()
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = None
       return state
     def __setstate__(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = d

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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return self.sp_model.encode(snake_case__  ,     out_type=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return self.sp_model.piece_to_id(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.sp_model.IdToPiece(snake_case__				)
       return token
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = []
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = ""
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           out_string += self.sp_model.decode(snake_case__				) + token
           _SCREAMING_SNAKE_CASE   :				Any							    = []
         else:
           current_sub_tokens.append(snake_case__				)
       out_string += self.sp_model.decode(snake_case__				)
       return out_string.strip()




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       if not os.path.isdir(snake_case__				):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
         return
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = os.path.join(
           snake_case__  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case__				) and os.path.isfile(self.vocab_file				):
         copyfile(self.vocab_file  ,     snake_case__				)
       elif not os.path.isfile(self.vocab_file				):
         with open(snake_case__  ,     "wb"				) as fi:
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.sp_model.serialized_model_proto()
           fi.write(snake_case__				)

       return (out_vocab_file,)





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     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=__SCREAMING_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=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     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 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_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 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_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=__SCREAMING_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 			UpperCamelCase      :
     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=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     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=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     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=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



