






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


lowerCamelCase       :       Any        =				logging.get_logger(__name__)


lowerCamelCase       :       int        =				'''▁'''

lowerCamelCase       :       Tuple        =				{'''vocab_file''': '''spiece.model'''}

lowerCamelCase       :       Optional[int]        =				{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowerCamelCase       :       int        =				{
    '''google/reformer-crime-and-punishment''': 52_42_88,
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Tuple     =					VOCAB_FILES_NAMES
					_A  :	List[str]     =					PRETRAINED_VOCAB_FILES_MAP
					_A  :	Any     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	str     =					['''input_ids''', '''attention_mask''']



					def __init__(     self :		Any						,   __a :		List[Any]						,   __a :		Optional[int]="</s>"						,   __a :		List[Any]="<unk>"						,   __a :		Dict=[]						,   __a :		Optional[Dict[str, Any]] = None						,   **__a :		List[Any]						,   )	->     None:


												"""simple docstring"""

												__lowercase					:							str			=   {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__a						,   unk_token=__a						,   additional_special_tokens=__a						,   sp_model_kwargs=self.sp_model_kwargs						,   **__a						,   )

												__lowercase					:							Optional[int]			=   vocab_file
												__lowercase					:							Any			=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(__a							)



					@property
					def 					lowerCAmelCase (     self :		Dict							)	->     str:


												"""simple docstring"""

												return self.sp_model.get_piece_size()



					def 					lowerCAmelCase (     self :		str							)	->     Dict[str, int]:


												"""simple docstring"""

												__lowercase					:							Dict			=   {self.convert_ids_to_tokens(__a							): i for i in range(self.vocab_size							)}
												vocab.update(self.added_tokens_encoder							)
												return vocab



					def __getstate__(     self :		Dict							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							int			=   self.__dict__.copy()
												__lowercase					:							Optional[int]			=   None
												return state



					def __setstate__(     self :		List[Any]						,   __a :		int							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Dict			=   d

												# for backward compatibility
												if not hasattr(self						,   """sp_model_kwargs"""							):
																			__lowercase					:							List[Any]			=   {}

												__lowercase					:							List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(self.vocab_file							)



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		List[Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												return self.sp_model.piece_to_id(__a							)



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Dict							)	->     List[Any]:


												"""simple docstring"""

												if index < self.sp_model.get_piece_size():
																			__lowercase					:							Optional[Any]			=   self.sp_model.IdToPiece(__a							)
												return token



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Tuple							)	->     int:


												"""simple docstring"""

												__lowercase					:							Tuple			=   []
												__lowercase					:							Optional[int]			=   """"""
												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(__a							) + token
																										__lowercase					:							Any			=   []
																			else:
																										current_sub_tokens.append(__a							)
												out_string += self.sp_model.decode(__a							)
												return out_string.strip()



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		str						,   __a :		Optional[str] = None							)	->     Tuple[str]:


												"""simple docstring"""

												if not os.path.isdir(__a							):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
																			return
												__lowercase					:							Optional[int]			=   os.path.join(
												    __a						,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

												return (out_vocab_file,)







lowerCamelCase       :       Dict        =				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
	from .utils import rich