



'''simple docstring'''


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


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			'''▁'''

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

lowercase							       =			{
    '''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'''
        ),
    }
}

lowercase							       =			{
    '''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 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Dict  =      VOCAB_FILES_NAMES
 __magic_name__					: Optional[int]  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: str  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: List[Any]  =      ["input_ids", "attention_mask"]




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_="<s>"							,  lowerCAmelCase_="</s>"							,  lowerCAmelCase_="</s>"							,  lowerCAmelCase_="<s>"							,  lowerCAmelCase_="<unk>"							,  lowerCAmelCase_="<pad>"							,  lowerCAmelCase_="<mask>"							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else mask_token

      a_       		={} if sp_model_kwargs is None else sp_model_kwargs

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

      a_       		=spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(str(lowerCAmelCase_))
      a_       		=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
      a_       		={"<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
      a_       		=1

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




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



      """simple docstring"""







      a_       		=self.__dict__.copy()
      a_       		=None
      a_       		=self.sp_model.serialized_model_proto()
      return state




 def __setstate__(   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		=d

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

      a_       		=spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto)




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



      """simple docstring"""







      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a_       		=[self.cls_token_id]
      a_       		=[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep




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



      """simple docstring"""







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

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




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



      """simple docstring"""







      a_       		=[self.sep_token_id]
      a_       		=[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       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      return len(self.sp_model) + self.fairseq_offset + 1  # Add the <mask> token




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      a_       		={self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab




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



      """simple docstring"""







      return self.sp_model.encode(lowerCAmelCase_							,  out_type=lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      a_       		=self.sp_model.PieceToId(lowerCAmelCase_)

      # 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       lowercase_  (   self							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







      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       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		="".join(lowerCAmelCase_).replace(lowerCAmelCase_							,  " ").strip()
      return out_string




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



      """simple docstring"""







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

      if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_) and os.path.isfile(self.vocab_file):
           copyfile(self.vocab_file							,  lowerCAmelCase_)
      elif not os.path.isfile(self.vocab_file):
           with open(lowerCAmelCase_							,  "wb") as fi:
                a_       		=self.sp_model.serialized_model_proto()
                fi.write(lowerCAmelCase_)

      return (out_vocab_file,)







'''simple docstring'''


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


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: int  =      "switch_transformers"
 __magic_name__					: List[Any]  =      ["past_key_values"]
 __magic_name__					: Union[str, Any]  =      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




 def __init__(   self							,  lowerCAmelCase_=3_2_1_2_8							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=2_0_4_8							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=8							,  lowerCAmelCase_=False							,  lowerCAmelCase_=0.0_1							,  lowerCAmelCase_="float32"							,  lowerCAmelCase_=False							,  lowerCAmelCase_=3_2							,  lowerCAmelCase_=1_2_8							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_=1e-6							,  lowerCAmelCase_=0.0_0_1							,  lowerCAmelCase_=0.0_0_1							,  lowerCAmelCase_=1.0							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=True							,  lowerCAmelCase_=0							,  lowerCAmelCase_=1							,  **lowerCAmelCase_							,  ) ->      Optional[int]:



      """simple docstring"""







      a_       		=vocab_size
      a_       		=d_model
      a_       		=d_kv
      a_       		=d_ff

      a_       		=num_sparse_encoder_layers

      a_       		=num_layers
      a_       		=(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      a_       		=num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
           a_       		=self.num_layers // self.num_sparse_encoder_layers
      else:
           a_       		=self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
           a_       		=self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
           a_       		=self.num_decoder_layers  # HACK: this will create 0 sparse layers

      a_       		=num_heads
      a_       		=num_experts
      a_       		=expert_capacity
      a_       		=router_bias
      a_       		=router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
      a_       		=router_dtype

      a_       		=router_ignore_padding_tokens
      a_       		=relative_attention_num_buckets
      a_       		=relative_attention_max_distance

      a_       		=dropout_rate
      a_       		=layer_norm_epsilon
      a_       		=initializer_factor
      a_       		=feed_forward_proj
      a_       		=use_cache
      a_       		=add_router_probs

      a_       		=router_z_loss_coef
      a_       		=router_aux_loss_coef

      a_       		=self.feed_forward_proj.split("-")
      a_       		=act_info[-1]
      a_       		=act_info[0] == "gated"

      if len(lowerCAmelCase_) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_) > 2:
           raise ValueError(
               f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'")

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           a_       		="gelu_new"

      super().__init__(
          pad_token_id=lowerCAmelCase_							,  eos_token_id=lowerCAmelCase_							,  is_encoder_decoder=lowerCAmelCase_							,  **lowerCAmelCase_							,  )


