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_     :			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_     :			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_     )
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: bool = False     ):
      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(lowerCAmelCase_     )
      np.random.seed(lowerCAmelCase_     )
      torch.manual_seed(lowerCAmelCase_     )
      if is_xpu_available():
            torch.xpu.manual_seed_all(lowerCAmelCase_     )
      elif is_npu_available():
            torch.npu.manual_seed_all(lowerCAmelCase_     )
      else:
            torch.cuda.manual_seed_all(lowerCAmelCase_     )
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[RNGType] = None ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      # Get the proper rng state
      if rng_type == RNGType.TORCH:
            snake_case_     :			List[Any]       =       torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            snake_case_     :			Dict       =       torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            snake_case_     :			Tuple       =       torch.tensor(xm.get_rng_state()     )
      elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            snake_case_     :			List[str]       =       torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            snake_case_     :			List[str]       =       torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            snake_case_     :			Optional[Any]       =       generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      snake_case_     :			List[Any]       =       AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            snake_case_     :			int       =       rng_state.to(xm.xla_device()     )
            xm.collective_broadcast([rng_state]     )
            xm.mark_step()
            snake_case_     :			List[str]       =       rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
            snake_case_     :			Dict       =       rng_state.to(state.device     )
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )
            snake_case_     :			Dict       =       rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()     )
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Union[str, RNGType]] ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      for rng_type in rng_types:
            synchronize_rng_state(RNGType(lowerCAmelCase_     ) ,	generator=lowerCAmelCase_     )



