




"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_					:     str					    =  logging.get_logger(__name__)

a_					:     Any					    =  '''▁'''

a_					:     Optional[int]					    =  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a_					:     Tuple					    =  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a_					:     Tuple					    =  {
    '''facebook/s2t-small-librispeech-asr''': 10_24,
}

a_					:     List[Any]					    =  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a_					:     List[str]					    =  {'''mustc''': MUSTC_LANGS}

class __lowercase(   SCREAMING_SNAKE_CASE_					):




					'''simple docstring'''


					__a    :    List[str]  		=			VOCAB_FILES_NAMES
					__a    :    List[Any]  		=			PRETRAINED_VOCAB_FILES_MAP
					__a    :    Any  		=			MAX_MODEL_INPUT_SIZES
					__a    :    Any  		=			['input_ids', 'attention_mask']

					__a    :    Dict  		=			[]
					def __init__(     self    ,						__a    ,						__a    ,						__a="<s>"    ,						__a="</s>"    ,						__a="<pad>"    ,						__a="<unk>"    ,						__a=False    ,						__a=False    ,						__a=None    ,						__a=None    ,						__a = None    ,						**__a    ,						):
								__lowerCamelCase       :						Optional[Any]													=					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__a    ,						eos_token=__a    ,						unk_token=__a    ,						pad_token=__a    ,						do_upper_case=__a    ,						do_lower_case=__a    ,						tgt_lang=__a    ,						lang_codes=__a    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**__a    ,						)
								__lowerCamelCase       :						Tuple													=					do_upper_case
								__lowerCamelCase       :						Optional[Any]													=					do_lower_case

								__lowerCamelCase       :						Tuple													=					load_json(__a  )
								__lowerCamelCase       :						Optional[int]													=					{v: k for k, v in self.encoder.items()}
								__lowerCamelCase       :						int													=					spm_file
								__lowerCamelCase       :						Union[str, Any]													=					load_spm(__a    ,						self.sp_model_kwargs  )

								if lang_codes is not None:
											__lowerCamelCase       :						Tuple													=					lang_codes
											__lowerCamelCase       :						int													=					LANGUAGES[lang_codes]
											__lowerCamelCase       :						Union[str, Any]													=					[f'''<lang:{lang}>''' for lang in self.langs]
											__lowerCamelCase       :						str													=					{lang: self.sp_model.PieceToId(f'''<lang:{lang}>'''  ) for lang in self.langs}

											__lowerCamelCase       :						Any													=					self.lang_tokens
											__lowerCamelCase       :						Any													=					tgt_lang if tgt_lang is not None else self.langs[0]

											self.set_tgt_lang_special_tokens(self._tgt_lang  )
								else:
											__lowerCamelCase       :						Dict													=					{}
					@property
					def      snake_case_					(     self  ):
								return len(self.encoder  )
					@property
					def      snake_case_					(     self  ):
								return self._tgt_lang
					@tgt_lang.setter
					def      snake_case_					(     self    ,						__a  ):
								__lowerCamelCase       :						Dict													=					new_tgt_lang
								self.set_tgt_lang_special_tokens(__a  )
					def      snake_case_					(     self    ,						__a  ):
								__lowerCamelCase       :						Optional[Any]													=					self.lang_code_to_id[tgt_lang]
								__lowerCamelCase       :						Dict													=					[lang_code_id]
					def      snake_case_					(     self    ,						__a  ):
								return self.sp_model.encode(__a    ,						out_type=__a  )
					def      snake_case_					(     self    ,						__a  ):
								return self.encoder.get(__a    ,						self.encoder[self.unk_token]  )
					def      snake_case_					(     self    ,						__a  ):
								return self.decoder.get(__a    ,						self.unk_token  )
					def      snake_case_					(     self    ,						__a  ):
								__lowerCamelCase       :						Union[str, Any]													=					[]
								__lowerCamelCase       :						Optional[int]													=					''
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														__lowerCamelCase       :						List[str]													=					self.sp_model.decode(__a  )
														out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
														__lowerCamelCase       :						int													=					[]
											else:
														current_sub_tokens.append(__a  )
								__lowerCamelCase       :						List[Any]													=					self.sp_model.decode(__a  )
								out_string += decoded.upper() if self.do_upper_case else decoded
								return out_string.strip()
					def      snake_case_					(     self    ,						__a    ,						__a=None  ):
								if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
					def      snake_case_					(     self    ,						__a    ,						__a = None    ,						__a = False  ):

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

								__lowerCamelCase       :						Tuple													=					[1] * len(self.prefix_tokens  )
								__lowerCamelCase       :						Tuple													=					[1]
								if token_ids_a is None:
											return prefix_ones + ([0] * len(__a  )) + suffix_ones
								return prefix_ones + ([0] * len(__a  )) + ([0] * len(__a  )) + suffix_ones
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						int													=					self.encoder.copy()
								vocab.update(self.added_tokens_encoder  )
								return vocab
					def __getstate__(     self  ):
								__lowerCamelCase       :						List[Any]													=					self.__dict__.copy()
								__lowerCamelCase       :						Optional[Any]													=					None
								return state
					def __setstate__(     self    ,						__a  ):
								__lowerCamelCase       :						List[str]													=					d

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

								__lowerCamelCase       :						List[Any]													=					load_spm(self.spm_file    ,						self.sp_model_kwargs  )





					def      snake_case_					(     self    ,						__a    ,						__a = None  ):
								__lowerCamelCase       :						Optional[int]													=					Path(__a  )
								assert save_dir.is_dir(), f'''{save_directory} should be a directory'''
								__lowerCamelCase       :						List[str]													=					save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
								)
								__lowerCamelCase       :						Union[str, Any]													=					save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
								)

								save_json(self.encoder    ,						__a  )

								if os.path.abspath(self.spm_file  ) != os.path.abspath(__a  ) and os.path.isfile(self.spm_file  ):
											copyfile(self.spm_file    ,						__a  )
								elif not os.path.isfile(self.spm_file  ):
											with open(__a    ,						'wb'  ) as fi:
														__lowerCamelCase       :						Dict													=					self.sp_model.serialized_model_proto()
														fi.write(__a  )

								return (str(__a  ), str(__a  ))





def     UpperCAmelCase					(      A__:     str				,					A__:     Dict[str, Any]  )	->  sentencepiece.SentencePieceProcessor:
			__lowerCamelCase       :						List[Any]													=					sentencepiece.SentencePieceProcessor(**_UpperCAmelCase  )
			spm.Load(str(_UpperCAmelCase  )  )
			return spm





def     UpperCAmelCase					(      A__:     str  )	->  Union[Dict, List]:
			with open(_UpperCAmelCase				,					'r'  ) as f:
						return json.load(_UpperCAmelCase  )






def     UpperCAmelCase					(      A__:     Union[str, Any]				,					A__:     str  )	->  None:
			with open(_UpperCAmelCase				,					'w'  ) as f:
						json.dump(_UpperCAmelCase				,					_UpperCAmelCase				,					indent=2  )








'''simple docstring'''





import copy

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


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



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''upernet'''
 def __init__(   self						:							Optional[int]      ,     __a						:							Dict=None      ,     __a						:							List[str]=512      ,     __a						:							int=0.0_2      ,     __a						:							Optional[Any]=[1, 2, 3, 6]      ,     __a						:							Union[str, Any]=True      ,     __a						:							Any=0.4      ,     __a						:							int=384      ,     __a						:							int=256      ,     __a						:							str=1      ,     __a						:							List[Any]=False      ,     __a						:							Dict=255      ,     **__a						:							Dict      ,     )      ->   Optional[int]:







       '''simple docstring'''
       super().__init__(**__a  )

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

       __snake_case :   Any													=    backbone_config
       __snake_case :   Tuple													=    hidden_size
       __snake_case :   str													=    initializer_range
       __snake_case :   Dict													=    pool_scales
       __snake_case :   Optional[Any]													=    use_auxiliary_head
       __snake_case :   Optional[Any]													=    auxiliary_loss_weight
       __snake_case :   Optional[int]													=    auxiliary_in_channels
       __snake_case :   Dict													=    auxiliary_channels
       __snake_case :   Any													=    auxiliary_num_convs
       __snake_case :   Optional[Any]													=    auxiliary_concat_input
       __snake_case :   List[str]													=    loss_ignore_index





 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    copy.deepcopy(self.__dict__  )
       __snake_case :   str													=    self.backbone_config.to_dict()
       __snake_case :   Optional[int]													=    self.__class__.model_type
       return output



