




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


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			List[str]           =					{
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase__      :			List[str]           =					{
    "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"
        )
    },
}

lowercase__      :			Union[str, Any]           =					{"Helsinki-NLP/opus-mt-en-de": 512}
lowercase__      :			Optional[int]           =					{}




class 			a__       (					UpperCamelCase__      ):
     a		:			Optional[int]							=	VOCAB_FILES_NAMES
     a		:			Union[str, Any]							=	PRETRAINED_VOCAB_FILES_MAP
     a		:			int							=	PRETRAINED_INIT_CONFIGURATION
     a		:			str							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:			List[Any]							=	["""input_ids""", """attention_mask"""]
     a		:			Union[str, Any]							=	re.compile(""">>.+<<"""      )  # type: re.Pattern







     def __init__(	self  ,		A  ,		A  ,		A  ,		A=None  ,		A=None  ,		A=None  ,		A="<unk>"  ,		A="</s>"  ,		A="<pad>"  ,		A=512  ,		A = None  ,		A=False  ,		**A  ,		)  ->    None:



          '''simple docstring'''



          a      =      {} 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=A  ,		target_lang=A  ,		unk_token=A  ,		eos_token=A  ,		pad_token=A  ,		model_max_length=A  ,		sp_model_kwargs=self.sp_model_kwargs  ,		target_vocab_file=A  ,		separate_vocabs=A  ,		**A  ,		)
          assert Path(A ).exists(), F'''cannot find spm source {source_spm}'''

          a      =      separate_vocabs
          a      =      load_json(A )
          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:
               a      =      load_json(A )
               a      =      {v: k for k, v in self.target_encoder.items()}
               a      =      []
          else:
               a      =      {v: k for k, v in self.encoder.items()}
               a      =      [k for k in self.encoder if k.startswith(">>" ) and k.endswith("<<" )]

          a      =      source_lang
          a      =      target_lang
          a      =      [source_spm, target_spm]

          # load SentencePiece model for pre-processing
          a      =      load_spm(A  ,		self.sp_model_kwargs )
          a      =      load_spm(A  ,		self.sp_model_kwargs )
          a      =      self.spm_source
          a      =      self.encoder

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

          self._setup_normalizer()







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          try:
               from sacremoses import MosesPunctNormalizer

               a      =      MosesPunctNormalizer(self.source_lang ).normalize
          except (ImportError, FileNotFoundError):
               warnings.warn("Recommended: pip install sacremoses." )
               a      =      lambda A : x







     def 			lowerCAmelCase_				(	self  ,		A )  ->    str:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    int:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Dict:



          '''simple docstring'''



          a      =      self.language_code_re.match(A )
          a      =      [match.group(0 )] if match else []
          return code, self.language_code_re.sub(""  ,		A )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    List[str]:



          '''simple docstring'''



          a  ,      a      =      self.remove_language_code(A )
          a      =      self.current_spm.encode(A  ,		out_type=A )
          return code + pieces







     def 			lowerCAmelCase_				(	self  ,		A )  ->    str:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		**A )  ->    str:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		**A )  ->    Dict:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A )  ->    str:



          '''simple docstring'''



          a      =      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          a      =      []
          a      =      ""
          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(A ) + token + " "
                    a      =      []
               else:
                    current_sub_tokens.append(A )
          out_string += sp_model.decode_pieces(A )
          return out_string.strip()







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=None )  ->    List[int]:



          '''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 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          a      =      self.spm_source
          a      =      self.encoder







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.spm_target
          if self.separate_vocabs:
               a      =      self.target_encoder







     @property
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return len(self.encoder )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None )  ->    Tuple[str]:



          '''simple docstring'''



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

          if self.separate_vocabs:
               a      =      os.path.join(
                   A  ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]  ,		)
               a      =      os.path.join(
                   A  ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]  ,		)
               save_json(self.encoder  ,		A )
               save_json(self.target_encoder  ,		A )
               saved_files.append(A )
               saved_files.append(A )
          else:
               a      =      os.path.join(
                   A  ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] )
               save_json(self.encoder  ,		A )
               saved_files.append(A )

          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]  ,		):
               a      =      os.path.join(
                   A  ,		(filename_prefix + "-" if filename_prefix else "") + spm_save_filename )
               if os.path.abspath(A ) != os.path.abspath(A ) and os.path.isfile(A ):
                    copyfile(A  ,		A )
                    saved_files.append(A )
               elif not os.path.isfile(A ):
                    with open(A  ,		"wb" ) as fi:
                         a      =      spm_model.serialized_model_proto()
                         fi.write(A )
                    saved_files.append(A )

          return tuple(A )







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          return self.get_src_vocab()







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



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



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







     def __getstate__(	self )  ->    Dict:



          '''simple docstring'''



          a      =      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  ,		A )  ->    None:



          '''simple docstring'''



          a      =      d

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

          a  ,      a      =      (load_spm(A  ,		self.sp_model_kwargs ) for f in self.spm_files)
          a      =      self.spm_source
          self._setup_normalizer()







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    Any:



          '''simple docstring'''



          return 1







     def 			lowerCAmelCase_				(	self  ,		A )  ->    str:



          '''simple docstring'''



          a      =      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 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = False )  ->    List[int]:



          '''simple docstring'''



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







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     sentencepiece.SentencePieceProcessor:
     a      =      sentencepiece.SentencePieceProcessor(**__UpperCamelCase)
     spm.Load(__UpperCamelCase)
     return spm

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     None:
     with open(__UpperCamelCase		,					"w") as f:
          json.dump(__UpperCamelCase		,					__UpperCamelCase		,					indent=2)





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     Union[Dict, List]:
     with open(__UpperCamelCase		,					"r") as f:
          return json.load(__UpperCamelCase)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

