


"""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


a__				:							Optional[Any]						=     logging.get_logger(__name__)


a__				:							Optional[Any]						=     '''▁'''

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

a__				:							Tuple						=     {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

a__				:							Union[str, Any]						=     {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Tuple    					=			VOCAB_FILES_NAMES
	snake_case__				:  Tuple    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Any    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  Optional[Any]    					=			["input_ids", "attention_mask"]




	def __init__( self			:				Optional[Any]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Tuple="</s>"			,			UpperCAmelCase__			:				Any="<unk>"			,			UpperCAmelCase__			:				List[Any]=[]			,			UpperCAmelCase__			:				Optional[Dict[str, Any]] = None			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	None:
						__SCREAMING_SNAKE_CASE						   =				{} if sp_model_kwargs is None else sp_model_kwargs

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

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




	@property
	def   UpperCAmelCase_      ( self			:				Dict   )       ->	str:
						return self.sp_model.get_piece_size()




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Dict[str, int]:
						__SCREAMING_SNAKE_CASE						   =				{self.convert_ids_to_tokens(UpperCAmelCase__   ): i for i in range(self.vocab_size   )}
						vocab.update(self.added_tokens_encoder   )
						return vocab




	def __getstate__( self			:				Optional[int]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.__dict__.copy()
						__SCREAMING_SNAKE_CASE						   =				None
						return state




	def __setstate__( self			:				Optional[int]			,			UpperCAmelCase__			:				List[str]   )       ->	Union[str, Any]:
						__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   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				str   )       ->	List[str]:
						return self.sp_model.encode(UpperCAmelCase__			,			out_type=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				int			,			UpperCAmelCase__			:				Union[str, Any]   )       ->	Optional[int]:
						return self.sp_model.piece_to_id(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Any   )       ->	List[str]:
						if index < self.sp_model.get_piece_size():
											__SCREAMING_SNAKE_CASE						   =				self.sp_model.IdToPiece(UpperCAmelCase__   )
						return token




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				List[Any]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				[]
						__SCREAMING_SNAKE_CASE						   =				""
						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(UpperCAmelCase__   ) + token
																__SCREAMING_SNAKE_CASE						   =				[]
											else:
																current_sub_tokens.append(UpperCAmelCase__   )
						out_string += self.sp_model.decode(UpperCAmelCase__   )
						return out_string.strip()




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						if not os.path.isdir(UpperCAmelCase__   ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
											return
						__SCREAMING_SNAKE_CASE						   =				os.path.join(
						    UpperCAmelCase__			,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

						return (out_vocab_file,)




"""simple docstring"""







import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs




@pytest.mark.parametrize(
    "kwargs, expected"  ,						[
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10   )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(lowerCAmelCase_  ,						i + 1   ) for i in range(10   )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1   )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0  ,						4   ), range(4  ,						7   ), range(7  ,						10   )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0  ,						1   ), range(1  ,						2   ), range(2  ,						3   )]),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				_distribute_shards(**lowerCAmelCase_   )
					assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"  ,						[
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				_split_gen_kwargs(lowerCAmelCase_  ,						lowerCAmelCase_   )
					assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, expected"  ,						[
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					if expected is RuntimeError:
										with pytest.raises(lowerCAmelCase_   ):
															_number_of_shards_in_gen_kwargs(lowerCAmelCase_   )
					else:
										__SCREAMING_SNAKE_CASE						   =				_number_of_shards_in_gen_kwargs(lowerCAmelCase_   )
										assert out == expected
