




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__a          =			logging.get_logger(__name__)

__a          =			'▁'

__a          =			{'vocab_file': 'sentencepiece.bpe.model'}

__a          =			{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__a          =			{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}

class   __a(					lowercase__       ):






    """simple docstring"""





    lowerCAmelCase        =				VOCAB_FILES_NAMES
    lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase        =				['''input_ids''', '''attention_mask''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="<mask>"       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         UpperCAmelCase_      :							Optional[int]       =      AddedToken(_a       ,lstrip=_a       ,rstrip=_a  ) if isinstance(_a       ,_a  ) else mask_token

         UpperCAmelCase_      :							Dict       =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_a       ,eos_token=_a       ,unk_token=_a       ,sep_token=_a       ,cls_token=_a       ,pad_token=_a       ,mask_token=_a       ,sp_model_kwargs=self.sp_model_kwargs       ,**_a       ,)

         UpperCAmelCase_      :							Any       =      spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(str(_a  )  )
         UpperCAmelCase_      :							Optional[int]       =      vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         UpperCAmelCase_      :							List[str]       =      {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         UpperCAmelCase_      :							List[str]       =      1

         UpperCAmelCase_      :							Tuple       =      len(self.sp_model  ) + self.fairseq_offset
         UpperCAmelCase_      :							Dict       =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}





    def __getstate__(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							List[Any]       =      self.__dict__.copy()
         UpperCAmelCase_      :							Optional[Any]       =      None
         UpperCAmelCase_      :							Dict       =      self.sp_model.serialized_model_proto()
         return state





    def __setstate__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      d

         # for backward compatibility
         if not hasattr(self       ,'''sp_model_kwargs'''  ):
              UpperCAmelCase_      :							str       =      {}

         UpperCAmelCase_      :							List[str]       =      spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple:






         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase_      :							List[str]       =      [self.cls_token_id]
         UpperCAmelCase_      :							Any       =      [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		Optional[int]:






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

         if token_ids_a is None:
              return [1] + ([0] * len(_a  )) + [1]
         return [1] + ([0] * len(_a  )) + [1, 1] + ([0] * len(_a  )) + [1]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      [self.sep_token_id]
         UpperCAmelCase_      :							Optional[Any]       =      [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep  ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]





    @property
    def 							a__				(				self  )			->		List[Any]:
         return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      {self.convert_ids_to_tokens(_a  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
         return self.sp_model.encode(_a       ,out_type=_a  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:






         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase_      :							Optional[Any]       =      self.sp_model.PieceToId(_a  )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:






         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      ''.join(_a  ).replace(_a       ,''' '''  ).strip()
         return out_string





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		int:






         if not os.path.isdir(_a  ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
              return
         UpperCAmelCase_      :							Any       =      os.path.join(
             _a       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(_a  ) and os.path.isfile(self.vocab_file  ):
              copyfile(self.vocab_file       ,_a  )
         elif not os.path.isfile(self.vocab_file  ):
              with open(_a       ,'''wb'''  ) as fi:
                   UpperCAmelCase_      :							Union[str, Any]       =      self.sp_model.serialized_model_proto()
                   fi.write(_a  )

         return (out_vocab_file,)





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      r'''\w+[.]\d+'''
     UpperCAmelCase_      :							int       =      re.findall(_lowercase			,						_lowercase				)
     for pat in pats:
          UpperCAmelCase_      :							List[Any]       =      key.replace(_lowercase			,						'''_'''.join(pat.split('''.'''				)				)				)
     return key







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''scale''',)
     if (
         any('''norm''' in str_ for str_ in pt_tuple_key				)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCAmelCase_      :							List[Any]       =      pt_tensor.transpose(2			,						3			,						1			,						0				)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight":
          UpperCAmelCase_      :							Tuple       =      pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''weight''',)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''bias''',)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCAmelCase_      :							str       =      flax_model.init_weights(PRNGKey(_lowercase				)				)

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCAmelCase_      :							Optional[int]       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							List[str]       =      tuple(renamed_pt_key.split('''.'''				)				)

          # Correctly rename weight parameters
          UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      rename_key_and_reshape_tensor(_lowercase			,						_lowercase			,						_lowercase				)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''				)

        # also add unexpected weight so that warning is thrown
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)