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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase							=       {
    "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"
        )
    },
}

UpperCAmelCase							=       {"Helsinki-NLP/opus-mt-en-de": 5_1_2}
UpperCAmelCase							=       {}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE	: Optional[int]						    =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE	: Dict						    =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE	: Optional[Any]						    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE	: str						    =      ["input_ids", "attention_mask"]
   _SCREAMING_SNAKE_CASE	: Optional[int]						    =      re.compile(">>.+<<"							)  # type: re.Pattern



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




         '''simple docstring'''




         snake_case_     :			Tuple       =       {} 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}"

         snake_case_     :			Union[str, Any]       =       separate_vocabs
         snake_case_     :			Union[str, Any]       =       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:
               snake_case_     :			Tuple       =       load_json(A__   )
               snake_case_     :			Any       =       {v: k for k, v in self.target_encoder.items()}
               snake_case_     :			Optional[Any]       =       []
         else:
               snake_case_     :			Dict       =       {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_     :			Optional[Any]       =       source_lang
         snake_case_     :			List[Any]       =       target_lang
         snake_case_     :			Dict       =       [source_spm, target_spm]

         # load SentencePiece model for pre-processing
         snake_case_     :			Dict       =       load_spm(A__			,	self.sp_model_kwargs   )
         snake_case_     :			List[Any]       =       load_spm(A__			,	self.sp_model_kwargs   )
         snake_case_     :			List[str]       =       self.spm_source
         snake_case_     :			Dict       =       self.encoder

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

         self._setup_normalizer()



   def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     int:




         '''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 A__   : x



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




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   List[str]   )							->     int:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   List[Any]			,	A__   :   str   )							->     str:




         '''simple docstring'''




         snake_case_     :			Union[str, Any]       =       self.language_code_re.match(A__   )
         snake_case_     :			list       =       [match.group(0   )] if match else []
         return code, self.language_code_re.sub(""			,	A__   )



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   str   )							->     List[str]:




         '''simple docstring'''




         snake_case_  ,snake_case_     :			List[str]       =       self.remove_language_code(A__   )
         snake_case_     :			Tuple       =       self.current_spm.encode(A__			,	out_type=A__   )
         return code + pieces



   def 					UpperCAmelCase__       (				self   :   int			,	A__   :   int   )							->     str:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Dict			,	A__   :   int			,	**A__   :   str   )							->     Optional[int]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Tuple			,	A__   :   Any			,	**A__   :   str   )							->     str:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[str]   )							->     str:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         snake_case_     :			Dict       =       []
         snake_case_     :			str       =       ""
         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 + " "
                     snake_case_     :			Union[str, Any]       =       []
               else:
                     current_sub_tokens.append(A__   )
         out_string += sp_model.decode_pieces(A__   )
         return out_string.strip()



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   Dict			,	A__   :   int=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 					UpperCAmelCase__       (				self   :   List[str]   )							->     Optional[int]:




         '''simple docstring'''




         snake_case_     :			Union[str, Any]       =       self.spm_source
         snake_case_     :			Optional[Any]       =       self.encoder



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




         '''simple docstring'''




         snake_case_     :			Any       =       self.spm_target
         if self.separate_vocabs:
               snake_case_     :			List[str]       =       self.target_encoder



   @property
   def 					UpperCAmelCase__       (				self   :   Tuple   )							->     int:




         '''simple docstring'''




         return len(self.encoder   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   str			,	A__   :   Optional[str] = None   )							->     Tuple[str]:




         '''simple docstring'''




         if not os.path.isdir(A__   ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
               return
         snake_case_     :			int       =       []

         if self.separate_vocabs:
               snake_case_     :			Any       =       os.path.join(
                   A__			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]			,	)
               snake_case_     :			int       =       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:
               snake_case_     :			List[str]       =       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]			,	):
               snake_case_     :			str       =       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:
                           snake_case_     :			List[Any]       =       spm_model.serialized_model_proto()
                           fi.write(A__   )
                     saved_files.append(A__   )

         return tuple(A__   )



   def 					UpperCAmelCase__       (				self   :   str   )							->     Dict:




         '''simple docstring'''




         return self.get_src_vocab()



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




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     Any:




         '''simple docstring'''




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



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




         '''simple docstring'''




         snake_case_     :			List[Any]       =       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   :   Dict			,	A__   :   Dict   )							->     None:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       d

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

         snake_case_  ,snake_case_     :			int       =       (load_spm(A__			,	self.sp_model_kwargs   ) for f in self.spm_files)
         snake_case_     :			str       =       self.spm_source
         self._setup_normalizer()



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	*A__   :   Any			,	**A__   :   Any   )							->     List[str]:




         '''simple docstring'''




         return 1



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Tuple   )							->     Union[str, Any]:




         '''simple docstring'''




         snake_case_     :			int       =       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 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   List			,	A__   :   Optional[List] = None			,	A__   :   bool = 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_			(		lowerCAmelCase_: str ,	lowerCAmelCase_: Dict[str, Any]     ):
      snake_case_     :			Union[str, Any]       =       sentencepiece.SentencePieceProcessor(**lowerCAmelCase_     )
      spm.Load(lowerCAmelCase_     )
      return spm






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Union[str, Any] ,	lowerCAmelCase_: str     ):
      with open(lowerCAmelCase_ ,	"w"     ) as f:
            json.dump(lowerCAmelCase_ ,	lowerCAmelCase_ ,	indent=2     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: str     ):
      with open(lowerCAmelCase_ ,	"r"     ) as f:
            return json.load(lowerCAmelCase_     )




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



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




         '''simple docstring'''




         if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )
         else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )



