



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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  """▁"""

__SCREAMING_SNAKE_CASE           =  {"""vocab_file""": """spiece.model"""}

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

__SCREAMING_SNAKE_CASE           =  {
    """google/reformer-crime-and-punishment""": 524288,
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  ["input_ids", "attention_mask"]







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

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

									A							:					Union[str, Any]              =						vocab_file
									A							:					str              =						spm.SentencePieceProcessor(**self.sp_model_kwargs )
									self.sp_model.Load(__lowerCamelCase )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									return self.sp_model.get_piece_size()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict[str, int]:
									A							:					int              =						{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] )      ->				int:
									A							:					Union[str, Any]              =						self.__dict__.copy()
									A							:					int              =						None
									return state







						def __setstate__(	self      :			Dict	,	__lowerCamelCase      :			List[str] )      ->				Optional[Any]:
									A							:					Dict              =						d

									# for backward compatibility
									if not hasattr(self	,	"sp_model_kwargs" ):
												A							:					Optional[int]              =						{}

									A							:					Tuple              =						spm.SentencePieceProcessor(**self.sp_model_kwargs )
									self.sp_model.Load(self.vocab_file )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			str )      ->				List[str]:
									return self.sp_model.encode(__lowerCamelCase	,	out_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			int )      ->				Dict:
									return self.sp_model.piece_to_id(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any] )      ->				Dict:
									if index < self.sp_model.get_piece_size():
												A							:					Optional[Any]              =						self.sp_model.IdToPiece(__lowerCamelCase )
									return token







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[Any] )      ->				str:
									A							:					Dict              =						[]
									A							:					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(__lowerCamelCase ) + token
															A							:					Tuple              =						[]
												else:
															current_sub_tokens.append(__lowerCamelCase )
									out_string += self.sp_model.decode(__lowerCamelCase )
									return out_string.strip()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									if not os.path.isdir(__lowerCamelCase ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
												return
									A							:					Union[str, Any]              =						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:
															A							:					Dict              =						self.sp_model.serialized_model_proto()
															fi.write(__lowerCamelCase )

									return (out_vocab_file,)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A							:					Union[str, Any]              =						XLMRobertaModel.from_pretrained("xlm-roberta-base" )
									A							:					Tuple              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Tuple              =						torch.Size((1, 12, 7_68) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[str]              =						torch.tensor(
									    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Tuple              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					str              =						XLMRobertaModel.from_pretrained("xlm-roberta-large" )
									A							:					List[Any]              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Optional[Any]              =						torch.Size((1, 12, 10_24) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[Any]              =						torch.tensor(
									    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Optional[int]              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )