

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


_UpperCAmelCase		:   List[str]										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Union[str, Any]										=       """▁"""

_UpperCAmelCase		:   Tuple										=       {"""vocab_file""": """spiece.model"""}

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

_UpperCAmelCase		:   Any										=       {
    """google/reformer-crime-and-punishment""": 52_4288,
}







class  lowercase				(       _UpperCAmelCase			):
				__SCREAMING_SNAKE_CASE					: int       			=			VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE					: int       			=			PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE					: Tuple       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE					: Tuple       			=			['''input_ids''', '''attention_mask''']




				def __init__(					self      ,   snake_case      ,   snake_case="</s>"      ,   snake_case="<unk>"      ,   snake_case=[]      ,   snake_case = None      ,   **snake_case      ,   ):
											snake_case_							=    {} if sp_model_kwargs is None else sp_model_kwargs

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

											snake_case_							=    vocab_file
											snake_case_							=    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(lowercase_       )




				@property
				def     a							(					self       ):
											return self.sp_model.get_piece_size()




				def     a							(					self       ):
											snake_case_							=    {self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab




				def __getstate__(					self       ):
											snake_case_							=    self.__dict__.copy()
											snake_case_							=    None
											return state




				def __setstate__(					self      ,   snake_case       ):
											snake_case_							=    d

											# for backward compatibility
											if not hasattr(self      ,   'sp_model_kwargs'       ):
																		snake_case_							=    {}

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




				def     a							(					self      ,   snake_case       ):
											return self.sp_model.encode(lowercase_      ,   out_type=lowercase_       )




				def     a							(					self      ,   snake_case       ):
											return self.sp_model.piece_to_id(lowercase_       )




				def     a							(					self      ,   snake_case       ):







											if index < self.sp_model.get_piece_size():
																		snake_case_							=    self.sp_model.IdToPiece(lowercase_       )
											return token




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    []
											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(lowercase_       ) + token
																									snake_case_							=    []
																		else:
																									current_sub_tokens.append(lowercase_       )
											out_string += self.sp_model.decode(lowercase_       )
											return out_string.strip()




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):







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

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase_       ) and os.path.isfile(self.vocab_file       ):
																		copyfile(self.vocab_file      ,   lowercase_       )
											elif not os.path.isfile(self.vocab_file       ):
																		with open(lowercase_      ,   'wb'       ) as fi:
																									snake_case_							=    self.sp_model.serialized_model_proto()
																									fi.write(lowercase_       )

											return (out_vocab_file,)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}


class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'resnet'
    lowerCamelCase			=							['basic', 'bottleneck']


    def __init__(      self :       Optional[Any],lowercase_ :       int=3,lowercase_ :       List[str]=6_4,lowercase_ :       int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8],lowercase_ :       Tuple=[3, 4, 6, 3],lowercase_ :       Union[str, Any]="bottleneck",lowercase_ :       List[str]="relu",lowercase_ :       Tuple=False,lowercase_ :       List[str]=None,lowercase_ :       List[Any]=None,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
        A__          =   num_channels
        A__          =   embedding_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   layer_type
        A__          =   hidden_act
        A__          =   downsample_in_first_stage
        A__          =   ['stem'] + [F'stage{idx}' for idx in range(1,len(lowercase_		) + 1		)]
        A__      ,						A__          =   get_aligned_output_features_output_indices(
            out_features=lowercase_,out_indices=lowercase_,stage_names=self.stage_names		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


    @property
    def 						snake_case__     (      self :       List[Any]		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]		)


    @property
    def 						snake_case__     (      self :       Any		)->    float:





        '''simple docstring'''







        return 1E-3

