





"""simple docstring"""




import os
import unicodedata
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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_						       =	{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_						       =	{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

SCREAMING_SNAKE_CASE_						       =	{
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

SCREAMING_SNAKE_CASE_						       =	"""▁"""

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      VOCAB_FILES_NAMES
     __snake_case   :     Dict		     =      PRETRAINED_VOCAB_FILES_MAP
     __snake_case   :     Dict		     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Optional[Any]=True						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Dict=False						,lowerCamelCase__						:   List[Any]="[CLS]"						,lowerCamelCase__						:   Dict="[SEP]"						,lowerCamelCase__						:   Optional[int]="<unk>"						,lowerCamelCase__						:   Optional[Any]="[SEP]"						,lowerCamelCase__						:   List[str]="<pad>"						,lowerCamelCase__						:   Tuple="[CLS]"						,lowerCamelCase__						:   int="[MASK]"						,lowerCamelCase__						:   Optional[Dict[str, Any]] = None						,**lowerCamelCase__						:   Optional[Any]						,)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						(
               AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__						,normalized=lowerCamelCase__	)
               if isinstance(lowerCamelCase__						,lowerCamelCase__	)
               else mask_token
           )

           SCREAMING_SNAKE_CASE		=						{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=lowerCamelCase__						,remove_space=lowerCamelCase__						,keep_accents=lowerCamelCase__						,bos_token=lowerCamelCase__						,eos_token=lowerCamelCase__						,unk_token=lowerCamelCase__						,sep_token=lowerCamelCase__						,pad_token=lowerCamelCase__						,cls_token=lowerCamelCase__						,mask_token=lowerCamelCase__						,sp_model_kwargs=self.sp_model_kwargs						,**lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						do_lower_case
           SCREAMING_SNAKE_CASE		=						remove_space
           SCREAMING_SNAKE_CASE		=						keep_accents
           SCREAMING_SNAKE_CASE		=						vocab_file

           SCREAMING_SNAKE_CASE		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(lowerCamelCase__	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Union[str, Any]:




           '''simple docstring'''


           return len(self.sp_model	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{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						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


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






     def __setstate__(							self						:   Tuple						,lowerCamelCase__						:   List[str]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						d

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

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           if self.remove_space:
                 SCREAMING_SNAKE_CASE		=						""" """.join(inputs.strip().split()	)
           else:
                 SCREAMING_SNAKE_CASE		=						inputs
           SCREAMING_SNAKE_CASE		=						outputs.replace("""``"""						,"""\""""	).replace("""''"""						,"""\""""	)

           if not self.keep_accents:
                 SCREAMING_SNAKE_CASE		=						unicodedata.normalize("""NFKD"""						,lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						"""""".join([c for c in outputs if not unicodedata.combining(lowerCamelCase__	)]	)
           if self.do_lower_case:
                 SCREAMING_SNAKE_CASE		=						outputs.lower()

           return outputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.preprocess_text(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.sp_model.encode(lowerCamelCase__						,out_type=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						[]
           for piece in pieces:
                 if len(lowerCamelCase__	) > 1 and piece[-1] == str(""","""	) and piece[-2].isdigit():
                       SCREAMING_SNAKE_CASE		=						self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase__						,""""""	)	)
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]	) == 1:
                                   SCREAMING_SNAKE_CASE		=						cur_pieces[1:]
                             else:
                                   SCREAMING_SNAKE_CASE		=						cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]	)
                       new_pieces.extend(lowerCamelCase__	)
                 else:
                       new_pieces.append(lowerCamelCase__	)

           return new_pieces






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           return self.sp_model.PieceToId(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		str:




           '''simple docstring'''


           return self.sp_model.IdToPiece(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   List[Any]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						""""""
           SCREAMING_SNAKE_CASE		=						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
                       SCREAMING_SNAKE_CASE		=						True
                       SCREAMING_SNAKE_CASE		=						[]
                 else:
                       current_sub_tokens.append(lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						False
           out_string += self.sp_model.decode(lowerCamelCase__	)
           return out_string.strip()






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,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 not None:
                 return [1] + ([0] * len(lowerCamelCase__	)) + [1] + ([0] * len(lowerCamelCase__	)) + [1]
           return [1] + ([0] * len(lowerCamelCase__	)) + [1]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[self.sep_token_id]
           SCREAMING_SNAKE_CASE		=						[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]






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,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
           SCREAMING_SNAKE_CASE		=						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:
                       SCREAMING_SNAKE_CASE		=						self.sp_model.serialized_model_proto()
                       fi.write(lowerCamelCase__	)

           return (out_vocab_file,)









import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      ComputeEnvironment.AMAZON_SAGEMAKER
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[int]		     =      "ml.p3.2xlarge"
     __snake_case   :     List[str]		     =      "accelerate_sagemaker_execution_role"
     __snake_case   :     Tuple		     =      "hf-sm"
     __snake_case   :     Any		     =      "us-east-1"
     __snake_case   :     Union[str, Any]		     =      1
     __snake_case   :     Dict		     =      "accelerate-sagemaker-1"
     __snake_case   :     Tuple		     =      "1.6"
     __snake_case   :     List[str]		     =      "4.4"
     __snake_case   :     str		     =      "train.py"
     __snake_case   :     List[str]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __snake_case   :     Optional[int]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
           assert isinstance(converted_args["""model_name_or_path"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""do_train"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""epochs"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""learning_rate"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""max_steps"""]						,lowerCamelCase__	)

           with pytest.raises(lowerCamelCase__	):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)


