





'''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


snake_case_     :       Dict						=  logging.get_logger(__name__)

snake_case_     :       Optional[int]						=  {"vocab_file": "spiece.model"}

snake_case_     :       List[str]						=  {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

snake_case_     :       Union[str, Any]						=  {"bert_for_seq_generation": 5_12}


class 					__a      (lowerCamelCase     ):
							__a					:   Any									=       VOCAB_FILES_NAMES
							__a					:   Dict									=       PRETRAINED_VOCAB_FILES_MAP
							__a					:   List[Any]									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a					:   List[int]									=       []
							__a					:   Optional[Any]									=       ["input_ids", "attention_mask"]

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







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  {} if sp_model_kwargs is None else sp_model_kwargs

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

													UpperCAmelCase_			:      int     						=  vocab_file

													UpperCAmelCase_			:      Optional[int]     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
													self.sp_model.Load(__magic_name__   )

							@property
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													return self.sp_model.get_piece_size()

							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  {self.convert_ids_to_tokens(__magic_name__   ): i for i in range(self.vocab_size   )}
													vocab.update(self.added_tokens_encoder   )
													return vocab

							def __getstate__(							self							:     Optional[int]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.__dict__.copy()
													UpperCAmelCase_			:      List[str]     						=  None
													return state

							def __setstate__(							self							:     Any       ,     __magic_name__							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  d

													# for backward compatibility
													if not hasattr(self       ,     '''sp_model_kwargs'''   ):
																			UpperCAmelCase_			:      List[str]     						=  {}

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

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     str   )					->       List[str]:







													"""simple docstring"""
													return self.sp_model.encode(__magic_name__       ,     out_type=__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     int   )					->       List[str]:







													"""simple docstring"""
													return self.sp_model.piece_to_id(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     Union[str, Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  self.sp_model.IdToPiece(__magic_name__   )
													return token

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     Optional[int]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  []
													UpperCAmelCase_			:      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(__magic_name__   ) + token
																									UpperCAmelCase_			:      Optional[int]     						=  []
																			else:
																									current_sub_tokens.append(__magic_name__   )
													out_string += self.sp_model.decode(__magic_name__   )
													return out_string.strip()



							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[str] = None   )					->       Tuple[str]:







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

													if os.path.abspath(self.vocab_file   ) != os.path.abspath(__magic_name__   ) and os.path.isfile(self.vocab_file   ):
																			copyfile(self.vocab_file       ,     __magic_name__   )
													elif not os.path.isfile(self.vocab_file   ):
																			with open(__magic_name__       ,     '''wb'''   ) as fi:
																									UpperCAmelCase_			:      Optional[int]     						=  self.sp_model.serialized_model_proto()
																									fi.write(__magic_name__   )

													return (out_vocab_file,)












'''simple docstring'''







import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  get_activation('''swish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  get_activation('''silu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  get_activation('''mish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.Mish   )

													self.assertEqual(act(torch.tensor(-2_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  get_activation('''gelu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.GELU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )





