




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							int	      =					"upernet"


				def __init__(							self:   Optional[Any]		,					_lowerCamelCase:   Optional[Any]=None		,					_lowerCamelCase:   List[str]=5_12		,					_lowerCamelCase:   List[Any]=0.02		,					_lowerCamelCase:   Any=[1, 2, 3, 6]		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Optional[Any]=0.4		,					_lowerCamelCase:   List[Any]=3_84		,					_lowerCamelCase:   Optional[Any]=2_56		,					_lowerCamelCase:   Dict=1		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   List[str]=2_55		,					**_lowerCamelCase:   Optional[Any]		,					):
											super().__init__(**UpperCAmelCase__		)

											if backbone_config is None:
																		logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''		)
																		SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		)
											elif isinstance(UpperCAmelCase__		,					UpperCAmelCase__		):
																		SCREAMING_SNAKE_CASE_   	=    backbone_config.get('''model_type'''		)
																		SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING[backbone_model_type]
																		SCREAMING_SNAKE_CASE_   	=    config_class.from_dict(UpperCAmelCase__		)

											SCREAMING_SNAKE_CASE_   	=    backbone_config
											SCREAMING_SNAKE_CASE_   	=    hidden_size
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    pool_scales
											SCREAMING_SNAKE_CASE_   	=    use_auxiliary_head
											SCREAMING_SNAKE_CASE_   	=    auxiliary_loss_weight
											SCREAMING_SNAKE_CASE_   	=    auxiliary_in_channels
											SCREAMING_SNAKE_CASE_   	=    auxiliary_channels
											SCREAMING_SNAKE_CASE_   	=    auxiliary_num_convs
											SCREAMING_SNAKE_CASE_   	=    auxiliary_concat_input
											SCREAMING_SNAKE_CASE_   	=    loss_ignore_index





				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    copy.deepcopy(self.__dict__		)
											SCREAMING_SNAKE_CASE_   	=    self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE_   	=    self.__class__.model_type
											return output











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""": 524_288,
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["input_ids", "attention_mask"]


				def __init__(							self:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[Any]="</s>"		,					_lowerCamelCase:   str="<unk>"		,					_lowerCamelCase:   Union[str, Any]=[]		,					_lowerCamelCase:   Optional[Dict[str, Any]] = None		,					**_lowerCamelCase:   List[str]		,					):
											SCREAMING_SNAKE_CASE_   	=    {} 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		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(_lowerCamelCase		)


				@property
				def     _A							(							self:   Optional[int]		):
											return self.sp_model.get_piece_size()


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    {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:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.__dict__.copy()
											SCREAMING_SNAKE_CASE_   	=    None
											return state


				def __setstate__(							self:   Dict		,					_lowerCamelCase:   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     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   str		):
											return self.sp_model.encode(_lowerCamelCase		,					out_type=_lowerCamelCase		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[int]		):
											return self.sp_model.piece_to_id(_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Tuple		):
											if index < self.sp_model.get_piece_size():
																		SCREAMING_SNAKE_CASE_   	=    self.sp_model.IdToPiece(_lowerCamelCase		)
											return token


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   int		):
											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(_lowerCamelCase		) + token
																									SCREAMING_SNAKE_CASE_   	=    []
																		else:
																									current_sub_tokens.append(_lowerCamelCase		)
											out_string += self.sp_model.decode(_lowerCamelCase		)
											return out_string.strip()





				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    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:
																									SCREAMING_SNAKE_CASE_   	=    self.sp_model.serialized_model_proto()
																									fi.write(_lowerCamelCase		)

											return (out_vocab_file,)


