





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class 				_UpperCamelCase       (						enum.Enum  ):
       _UpperCamelCase					:       Tuple       = '''timm'''
       _UpperCamelCase					:       Union[str, Any]       = '''transformers'''




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Any:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"	)

       if out_features is not None:
              if not isinstance(UpperCamelCase_       ,		(list,)	):
                     raise ValueError(F'''out_features must be a list {type(UpperCamelCase_	)}'''	)
              if any(feat not in stage_names for feat in out_features	):
                     raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''	)

       if out_indices is not None:
              if not isinstance(UpperCamelCase_       ,		(list, tuple)	):
                     raise ValueError(F'''out_indices must be a list or tuple, got {type(UpperCamelCase_	)}'''	)
              if any(idx >= len(UpperCamelCase_	) for idx in out_indices	):
                     raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"	)

       if out_features is not None and out_indices is not None:
              if len(UpperCamelCase_	) != len(UpperCamelCase_	):
                     raise ValueError("out_features and out_indices should have the same length if both are set"	)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError("out_features and out_indices should correspond to the same stages if both are set"	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		)  ->		int:
       if out_indices is None and out_features is None:
              UpperCamelCase_ 							=	[len(UpperCamelCase_	) - 1]
              UpperCamelCase_ 							=	[stage_names[-1]]
       elif out_indices is None and out_features is not None:
              UpperCamelCase_ 							=	[stage_names.index(UpperCamelCase_	) for layer in out_features]
       elif out_features is None and out_indices is not None:
              UpperCamelCase_ 							=	[stage_names[idx] for idx in out_indices]
       return out_features, out_indices




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		)  ->		Tuple[List[str], List[int]]:
       verify_out_features_out_indices(out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       UpperCamelCase_							,			UpperCamelCase_ 							=	_align_output_features_output_indices(
           out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=UpperCamelCase_       ,		out_indices=UpperCamelCase_       ,		stage_names=UpperCamelCase_	)
       return output_features, output_indices



class 				_UpperCamelCase       :
       _UpperCamelCase					:       Optional[BackboneType]       = None

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Dict		)							->			None:




              """simple docstring"""


              if getattr(self       , "_backbone"       , _SCREAMING_SNAKE_CASE		) is None:
                     raise ValueError("self._backbone must be set before calling _init_timm_backbone"		)

              # These will diagree with the defaults for the transformers models e.g. for resnet50
              # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
              # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
              UpperCamelCase_ 							=	[stage["module"] for stage in self._backbone.feature_info.info]
              UpperCamelCase_ 							=	[stage["num_chs"] for stage in self._backbone.feature_info.info]
              UpperCamelCase_ 							=	self._backbone.feature_info.out_indices
              UpperCamelCase_ 							=	self._backbone.feature_info.module_name()

              # We verify the out indices and out features are valid
              verify_out_features_out_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	out_features, out_indices

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "stage_names"		)
              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "out_features"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "out_indices"       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	stage_names
              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=_SCREAMING_SNAKE_CASE		)
              # Number of channels for each stage. This is set in the transformer backbone model init
              UpperCamelCase_ 							=	None

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	config

              UpperCamelCase_ 							=	getattr(_SCREAMING_SNAKE_CASE       , "use_timm_backbone"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

              if self.backbone_type == BackboneType.TIMM:
                     self._init_timm_backbone(_SCREAMING_SNAKE_CASE		)
              elif self.backbone_type == BackboneType.TRANSFORMERS:
                     self._init_transformers_backbone(_SCREAMING_SNAKE_CASE		)
              else:
                     raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''		)

       @property
       def lowercase			(		self:			str		)							->			List[str]:




              """simple docstring"""


              return self._out_features

       @out_features.setter
       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			List[str]		)							->			str:




              """simple docstring"""


              return self._out_indices

       @out_indices.setter
       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[Tuple[int], List[int]]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Optional[Any]:




              """simple docstring"""


              return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names		)}

       @property
       def lowercase			(		self:			Tuple		)							->			List[str]:




              """simple docstring"""


              return [self.out_feature_channels[name] for name in self.out_features]

       def lowercase			(		self:			Optional[int]       , *_SCREAMING_SNAKE_CASE:			Dict       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(inspect.signature(self.forward		).parameters		)
              UpperCamelCase_ 							=	{k: v for k, v in kwargs.items() if k in signature}
              return self(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , _SCREAMING_SNAKE_CASE:			Optional[bool] = None       , )							->			List[Any]:




              """simple docstring"""


              raise NotImplementedError("This method should be implemented by the derived class."		)



       def lowercase			(		self:			Optional[Any]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	super().to_dict()
              UpperCamelCase_ 							=	output.pop("_out_features"		)
              UpperCamelCase_ 							=	output.pop("_out_indices"		)
              return output



class 				_UpperCamelCase       :

       @property
       def lowercase			(		self:			List[str]		)							->			Any:




              """simple docstring"""


              return self._out_features

       @out_features.setter
       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)

       @property
       def lowercase			(		self:			Any		)							->			Dict:




              """simple docstring"""


              return self._out_indices

       @out_indices.setter
       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[Tuple[int], List[int]]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_aligned_output_features_output_indices(
                  out_features=_SCREAMING_SNAKE_CASE       , out_indices=_SCREAMING_SNAKE_CASE       , stage_names=self.stage_names		)



       def lowercase			(		self:			Tuple		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	super().to_dict()
              UpperCamelCase_ 							=	output.pop("_out_features"		)
              UpperCamelCase_ 							=	output.pop("_out_indices"		)
              return output








'''simple docstring'''






import json
import os
import re
import warnings
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 :			Optional[Any]							   =      logging.get_logger(__name__)

A :			Optional[Any]							   =      {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A :			Tuple							   =      {
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

A :			Optional[int]							   =      {'Helsinki-NLP/opus-mt-en-de': 512}
A :			Any							   =      {}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_INIT_CONFIGURATION
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     re.compile(">>.+<<"	)  # type: re.Pattern


					def __init__(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[Any]=None							,  __snake_case						:	int=None							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	Optional[int]="<unk>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Tuple="<pad>"							,  __snake_case						:	List[str]=5_12							,  __snake_case						:	Optional[Dict[str, Any]] = None							,  __snake_case						:	Union[str, Any]=False							,  **__snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
											    source_lang=__snake_case							,  target_lang=__snake_case							,  unk_token=__snake_case							,  eos_token=__snake_case							,  pad_token=__snake_case							,  model_max_length=__snake_case							,  sp_model_kwargs=self.sp_model_kwargs							,  target_vocab_file=__snake_case							,  separate_vocabs=__snake_case							,  **__snake_case							,  )
											assert Path(__snake_case							).exists(), f'''cannot find spm source {source_spm}'''

											_snake_case: List[Any]	       =		separate_vocabs
											_snake_case: List[Any]	       =		load_json(__snake_case							)
											if self.unk_token not in self.encoder:
																	raise KeyError('<unk> token must be in vocab'							)
											assert self.pad_token in self.encoder

											if separate_vocabs:
																	_snake_case: List[str]	       =		load_json(__snake_case							)
																	_snake_case: Optional[int]	       =		{v: k for k, v in self.target_encoder.items()}
																	_snake_case: Tuple	       =		[]
											else:
																	_snake_case: Any	       =		{v: k for k, v in self.encoder.items()}
																	_snake_case: list	       =		[k for k in self.encoder if k.startswith('>>'							) and k.endswith('<<'							)]

											_snake_case: List[Any]	       =		source_lang
											_snake_case: List[str]	       =		target_lang
											_snake_case: List[str]	       =		[source_spm, target_spm]

											# load SentencePiece model for pre-processing
											_snake_case: Optional[Any]	       =		load_spm(__snake_case							,  self.sp_model_kwargs							)
											_snake_case: List[str]	       =		load_spm(__snake_case							,  self.sp_model_kwargs							)
											_snake_case: str	       =		self.spm_source
											_snake_case: int	       =		self.encoder

											# Multilingual target side: default to using first supported language code.

											self._setup_normalizer()


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											try:
																	from sacremoses import MosesPunctNormalizer

																	_snake_case: List[Any]	       =		MosesPunctNormalizer(self.source_lang							).normalize
											except (ImportError, FileNotFoundError):
																	warnings.warn('Recommended: pip install sacremoses.'							)
																	_snake_case: List[Any]	       =		lambda __snake_case							: x


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											return self.punc_normalizer(__snake_case							) if x else ""


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											return self.current_encoder.get(__snake_case							,  self.current_encoder[self.unk_token]							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.language_code_re.match(__snake_case							)
											_snake_case: list	       =		[match.group(0							)] if match else []
											return code, self.language_code_re.sub(''							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[Any]	       =		self.remove_language_code(__snake_case							)
											_snake_case: Union[str, Any]	       =		self.current_spm.encode(__snake_case							,  out_type=__snake_case							)
											return code + pieces


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	int							):





											'''simple docstring'''





											return self.decoder.get(__snake_case							,  self.unk_token							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Tuple							,  **__snake_case						:	int							):





											'''simple docstring'''





											return super().batch_decode(__snake_case							,  **__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Any							,  **__snake_case						:	List[str]							):





											'''simple docstring'''





											return super().decode(__snake_case							,  **__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	List[str]							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
											_snake_case: List[Any]	       =		[]
											_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 += sp_model.decode_pieces(__snake_case							) + token + " "
																							_snake_case: Any	       =		[]
																	else:
																							current_sub_tokens.append(__snake_case							)
											out_string += sp_model.decode_pieces(__snake_case							)
											return out_string.strip()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]=None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.spm_source
											_snake_case: List[str]	       =		self.encoder


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: int	       =		self.spm_target
											if self.separate_vocabs:
																	_snake_case: Union[str, Any]	       =		self.target_encoder


					@property
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											return len(self.encoder							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											if not os.path.isdir(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
																	return
											_snake_case: str	       =		[]

											if self.separate_vocabs:
																	_snake_case: Optional[int]	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']							,  )
																	_snake_case: Optional[int]	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']							,  )
																	save_json(self.encoder							,  __snake_case							)
																	save_json(self.target_encoder							,  __snake_case							)
																	saved_files.append(__snake_case							)
																	saved_files.append(__snake_case							)
											else:
																	_snake_case: int	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']							)
																	save_json(self.encoder							,  __snake_case							)
																	saved_files.append(__snake_case							)

											for spm_save_filename, spm_orig_path, spm_model in zip(
											    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]							,  self.spm_files							,  [self.spm_source, self.spm_target]							,  ):
																	_snake_case: Any	       =		os.path.join(
																	    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + spm_save_filename							)
																	if os.path.abspath(__snake_case							) != os.path.abspath(__snake_case							) and os.path.isfile(__snake_case							):
																							copyfile(__snake_case							,  __snake_case							)
																							saved_files.append(__snake_case							)
																	elif not os.path.isfile(__snake_case							):
																							with open(__snake_case							,  'wb'							) as fi:
																													_snake_case: Any	       =		spm_model.serialized_model_proto()
																													fi.write(__snake_case							)
																							saved_files.append(__snake_case							)

											return tuple(__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											return self.get_src_vocab()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											return dict(self.encoder							,  **self.added_tokens_encoder							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											return dict(self.target_encoder							,  **self.added_tokens_decoder							)


					def __getstate__(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: str	       =		self.__dict__.copy()
											state.update(
											    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}							)
											return state


					def __setstate__(       self						:	str							,  __snake_case						:	Dict							):





											'''simple docstring'''





											_snake_case: str	       =		d

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

											_snake_case       ,    _snake_case: Dict	       =		(load_spm(__snake_case							,  self.sp_model_kwargs							) for f in self.spm_files)
											_snake_case: str	       =		self.spm_source
											self._setup_normalizer()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  *__snake_case						:	Tuple							,  **__snake_case						:	Tuple							):





											'''simple docstring'''





											return 1


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Dict	       =		set(self.all_special_ids							)  # call it once instead of inside list comp
											all_special_ids.remove(self.unk_token_id							)  # <unk> is only sometimes special
											return [1 if x in all_special_ids else 0 for x in seq]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List							,  __snake_case						:	Optional[List] = None							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											if already_has_special_tokens:
																	return self._special_token_mask(__snake_case							)
											elif token_ids_a is None:
																	return self._special_token_mask(__snake_case							) + [1]
											else:
																	return self._special_token_mask(token_ids_a + token_ids_a							) + [1]





def    lowercase_   (		lowercase__					,	lowercase__    ) ->sentencepiece.SentencePieceProcessor:
						_snake_case: Union[str, Any]	       =		sentencepiece.SentencePieceProcessor(**lowercase__    )
						spm.Load(lowercase__    )
						return spm





def    lowercase_   (		lowercase__					,	lowercase__    ) ->None:
						with open(lowercase__					,	'w'    ) as f:
												json.dump(lowercase__					,	lowercase__					,	indent=2    )





def    lowercase_   (		lowercase__    ) ->Union[Dict, List]:
						with open(lowercase__					,	'r'    ) as f:
												return json.load(lowercase__    )





