






"""simple docstring"""







import os
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


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

a			:    Optional[int]				 =		"""▁"""

a			:    int				 =		{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

a			:    int				 =		{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

a			:    Tuple				 =		{"""vinai/bartpho-syllable""": 1024}


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= VOCAB_FILES_NAMES
						__lowerCamelCase    	= PRETRAINED_VOCAB_FILES_MAP
						__lowerCamelCase    	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCamelCase    	= ["input_ids", "attention_mask"]




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__="<s>"	,  snake_case__="</s>"	,  snake_case__="</s>"	,  snake_case__="<s>"	,  snake_case__="<unk>"	,  snake_case__="<pad>"	,  snake_case__="<mask>"	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												# Mask token behave like a normal word, i.e. include the space before it
												lowercase__    :				Optional[int]=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else mask_token

												lowercase__    :				Union[str, Any]=	{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=snake_case__	,  eos_token=snake_case__	,  unk_token=snake_case__	,  sep_token=snake_case__	,  cls_token=snake_case__	,  pad_token=snake_case__	,  mask_token=snake_case__	,  sp_model_kwargs=self.sp_model_kwargs	,  **snake_case__	,  )

												lowercase__    :				List[str]=	vocab_file
												lowercase__    :				Tuple=	monolingual_vocab_file
												lowercase__    :				List[Any]=	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(str(snake_case__      )      )

												# Load the reduced vocab

												# Keep order of special tokens for backward compatibility
												lowercase__    :				Optional[int]=	{}
												lowercase__    :				Optional[int]=	0
												for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
																		if str(snake_case__      ) not in self.fairseq_tokens_to_ids:
																								lowercase__    :				Optional[Any]=	cnt
																								cnt += 1
												with open(snake_case__	,  "r"	,  encoding="utf-8"      ) as f:
																		for line in f.readlines():
																								lowercase__    :				str=	line.strip().split()[0]
																								lowercase__    :				Any=	len(self.fairseq_tokens_to_ids      )
												if str(snake_case__      ) not in self.fairseq_tokens_to_ids:
																		lowercase__    :				Optional[int]=	len(self.fairseq_tokens_to_ids      )

												lowercase__    :				Tuple=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




						def __getstate__(       self      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	self.__dict__.copy()
												lowercase__    :				Union[str, Any]=	None
												lowercase__    :				int=	self.sp_model.serialized_model_proto()
												return state




						def __setstate__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	d

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

												lowercase__    :				Optional[Any]=	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''

												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase__    :				List[str]=	[self.cls_token_id]
												lowercase__    :				int=	[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = False      ):
												'''simple docstring'''

												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case__	,  token_ids_a=snake_case__	,  already_has_special_tokens=snake_case__      )

												if token_ids_a is None:
																		return [1] + ([0] * len(snake_case__      )) + [1]
												return [1] + ([0] * len(snake_case__      )) + [1, 1] + ([0] * len(snake_case__      )) + [1]




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	[self.sep_token_id]
												lowercase__    :				Any=	[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 + sep + token_ids_a + sep      ) * [0]




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return len(self.fairseq_ids_to_tokens      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	{self.convert_ids_to_tokens(snake_case__      ): i for i in range(self.vocab_size      )}
												vocab.update(self.added_tokens_encoder      )
												return vocab




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												return self.sp_model.encode(snake_case__	,  out_type=snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
												else:
																		return self.unk_token_id




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												return self.fairseq_ids_to_tokens[index]




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	"".join(snake_case__      ).replace(snake_case__	,  " "      ).strip()
												return out_string




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												if not os.path.isdir(snake_case__      ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																		return
												lowercase__    :				Tuple=	os.path.join(
												    snake_case__	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
												lowercase__    :				str=	os.path.join(
												    snake_case__	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]	,  )

												if os.path.abspath(self.vocab_file      ) != os.path.abspath(snake_case__      ) and os.path.isfile(self.vocab_file      ):
																		copyfile(self.vocab_file	,  snake_case__      )
												elif not os.path.isfile(self.vocab_file      ):
																		with open(snake_case__	,  "wb"      ) as fi:
																								lowercase__    :				Optional[Any]=	self.sp_model.serialized_model_proto()
																								fi.write(snake_case__      )

												if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
												    snake_case__      ) and os.path.isfile(self.monolingual_vocab_file      ):
																		copyfile(self.monolingual_vocab_file	,  snake_case__      )
												elif not os.path.isfile(self.monolingual_vocab_file      ):
																		with open(snake_case__	,  "w"	,  encoding="utf-8"      ) as fp:
																								for token in self.fairseq_tokens_to_ids:
																														if token not in self.all_special_tokens:
																																				fp.write(F'''{str(snake_case__      )} \n'''      )

												return out_vocab_file, out_monolingual_vocab_file














"""simple docstring"""







import os
from pathlib import Path





def 						lowercase__()    ->List[Any]:




						"""simple docstring"""



						from torch.utils.cpp_extension import load

						lowercase__    :				Any=	Path(A						).resolve().parent.parent.parent / "kernels" / "deformable_detr"
						lowercase__    :				Any=	[
						    root / filename
						    for filename in [
						        "vision.cpp",
						        os.path.join("cpu" ,		"ms_deform_attn_cpu.cpp"						),
						        os.path.join("cuda" ,		"ms_deform_attn_cuda.cu"						),
						    ]
						]

						load(
						    "MultiScaleDeformableAttention" ,		A ,		with_cuda=A ,		extra_include_paths=[str(A						)] ,		extra_cflags=["-DWITH_CUDA=1"] ,		extra_cuda_cflags=[
						        "-DCUDA_HAS_FP16=1",
						        "-D__CUDA_NO_HALF_OPERATORS__",
						        "-D__CUDA_NO_HALF_CONVERSIONS__",
						        "-D__CUDA_NO_HALF2_OPERATORS__",
						    ] ,		)

						import MultiScaleDeformableAttention as MSDA

						return MSDA






