



'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase: Optional[int]							=						logging.get_logger(__name__)

lowerCAmelCase: List[str]							=						{'vocab_file': 'spm_char.model'}

lowerCAmelCase: Tuple							=						{
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

lowerCAmelCase: List[Any]							=						{
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}

class 							a__( lowerCamelCase__     ):
				lowercase__			=   VOCAB_FILES_NAMES
				lowercase__			=   PRETRAINED_VOCAB_FILES_MAP
				lowercase__			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__			=   ["""input_ids""", """attention_mask"""]







				def __init__(				self			:  Tuple						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[Any]="<s>"						,      __snake_case			:  Tuple="</s>"						,      __snake_case			:  List[str]="<unk>"						,      __snake_case			:  List[Any]="<pad>"						,      __snake_case			:  Optional[Dict[str, Any]] = None						,      **__snake_case			:  Dict						,      ):
						a						: Dict					       =					{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=__snake_case						,      eos_token=__snake_case						,      unk_token=__snake_case						,      pad_token=__snake_case						,      sp_model_kwargs=self.sp_model_kwargs						,      **__snake_case						,      )

						a						: int					       =					vocab_file

						a						: str					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(__snake_case     )







				@property
				def       lowercase_	(				self			:  List[Any]     ):
						return self.sp_model.get_piece_size()







				def       lowercase_	(				self			:  int     ):
						a						: List[Any]					       =					{self.convert_ids_to_tokens(__snake_case     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab







				def __getstate__(				self			:  Dict     ):
						a						: int					       =					self.__dict__.copy()
						a						: str					       =					None
						return state







				def __setstate__(				self			:  Optional[int]						,      __snake_case			:  Tuple     ):
						a						: Optional[int]					       =					d

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

						a						: Union[str, Any]					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )







				def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  str     ):
						return self.sp_model.encode(__snake_case						,      out_type=__snake_case     )







				def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
						return self.sp_model.piece_to_id(__snake_case     )







				def       lowercase_	(				self			:  List[str]						,      __snake_case			:  str     ):
						a						: Optional[int]					       =					self.sp_model.IdToPiece(__snake_case     )
						return token







				def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  List[str]     ):
						a						: Any					       =					[]
						a						: int					       =					''
						for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
										out_string += self.sp_model.decode(__snake_case     ) + token
										a						: str					       =					[]
								else:
										current_sub_tokens.append(__snake_case     )
						out_string += self.sp_model.decode(__snake_case     )
						return out_string.strip()







				def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  Tuple=None     ):
						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       lowercase_	(				self			:  Any						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None						,      __snake_case			:  bool = False     ):
						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=__snake_case						,      token_ids_a=__snake_case						,      already_has_special_tokens=__snake_case     )

						a						: Optional[Any]					       =					[1]
						if token_ids_a is None:
								return ([0] * len(__snake_case     )) + suffix_ones
						return ([0] * len(__snake_case     )) + ([0] * len(__snake_case     )) + suffix_ones







				def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  str						,      __snake_case			:  Optional[str] = None     ):
						if not os.path.isdir(__snake_case     ):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
								return
						a						: Union[str, Any]					       =					os.path.join(
						    __snake_case						,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

						if os.path.abspath(self.vocab_file     ) != os.path.abspath(__snake_case     ) and os.path.isfile(self.vocab_file     ):
								copyfile(self.vocab_file						,      __snake_case     )
						elif not os.path.isfile(self.vocab_file     ):
								with open(__snake_case						,      'wb'     ) as fi:
										a						: Dict					       =					self.sp_model.serialized_model_proto()
										fi.write(__snake_case     )

						return (out_vocab_file,)




'''simple docstring'''






import torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

        if p_starts is not None:
          a						: List[Any]					       =					torch.vstack((p_starts, p_start)     )
          a						: List[Any]					       =					torch.vstack((p_ends, p_end)     )
        else:
          a						: List[Any]					       =					p_start
          a						: Optional[int]					       =					p_end

      return p_starts, p_ends