

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							:  Union[str, Any]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Optional[Any]            =						'▁'

lowerCAmelCase							:  Optional[Any]            =						{'vocab_file': 'spiece.model'}

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

lowerCAmelCase							:  Dict            =						{
    'google/reformer-crime-and-punishment': 524288,
}



class   __lowercase    (			_lowerCAmelCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							int      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							Dict      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							int      							=					['''input_ids''', '''attention_mask''']



			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Union[str, Any]="</s>"       ,						lowerCAmelCase__  :					Union[str, Any]="<unk>"       ,						lowerCAmelCase__  :					Dict=[]       ,						lowerCAmelCase__  :					List[str] = None       ,						**lowerCAmelCase__  :					Any       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	{} if sp_model_kwargs is None else sp_model_kwargs

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

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	vocab_file
									SCREAMING_SNAKE_CASE_:			List[str]  		=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
									self.sp_model.Load(SCREAMING_SNAKE_CASE_)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									return self.sp_model.get_piece_size()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_): i for i in range(self.vocab_size)}
									vocab.update(self.added_tokens_encoder)
									return vocab



			def __getstate__(			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Any  		=	self.__dict__.copy()
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	None
									return state



			def __setstate__(			self  :					str       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	d

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

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					int):
									return self.sp_model.encode(SCREAMING_SNAKE_CASE_       ,						out_type=SCREAMING_SNAKE_CASE_)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					int):
									return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					List[str]):
									if index < self.sp_model.get_piece_size():
															SCREAMING_SNAKE_CASE_:			int  		=	self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_)
									return token



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[]
									SCREAMING_SNAKE_CASE_:			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(SCREAMING_SNAKE_CASE_) + token
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
															else:
																					current_sub_tokens.append(SCREAMING_SNAKE_CASE_)
									out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_)
									return out_string.strip()




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					Optional[int] = None):
									if not os.path.isdir(SCREAMING_SNAKE_CASE_):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_:			Dict  		=	os.path.join(
									    SCREAMING_SNAKE_CASE_       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

									return (out_vocab_file,)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase							:  Optional[int]            =						{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						["""YolosFeatureExtractor"""]
						lowerCAmelCase							:  Tuple            =						["""YolosImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						[
						    """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """YolosForObjectDetection""",
						    """YolosModel""",
						    """YolosPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_yolos import YolosFeatureExtractor
												from .image_processing_yolos import YolosImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_yolos import (
												    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    YolosForObjectDetection,
												    YolosModel,
												    YolosPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase							:  int            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






