
'''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   =       logging.get_logger(__name__)

__lowerCAmelCase   =       {"""vocab_file""": """spm_char.model"""}

__lowerCAmelCase   =       {
    """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   =       {
    """microsoft/speecht5_asr""": 1_0_2_4,
    """microsoft/speecht5_tts""": 1_0_2_4,
    """microsoft/speecht5_vc""": 1_0_2_4,
}







class UpperCAmelCase__      (					lowerCAmelCase_      ):





  """simple docstring"""

  __UpperCAmelCase							:							str    					=   VOCAB_FILES_NAMES
  __UpperCAmelCase							:							Optional[Any]    					=   PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase							:							Dict    					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase							:							List[Any]    					=   ['input_ids', 'attention_mask']


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


     '''simple docstring'''






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

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

     _a							:      Optional[Any]     =  vocab_file

     _a							:      int     =  spm.SentencePieceProcessor(**self.sp_model_kwargs			)
     self.sp_model.Load(__lowerCAmelCase			)


  @property
  def     __lowercase						(						self :	List[str]			):


     '''simple docstring'''






     return self.sp_model.get_piece_size()


  def     __lowercase						(						self :	List[Any]			):


     '''simple docstring'''






     _a							:      Dict     =  {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
     vocab.update(self.added_tokens_encoder			)
     return vocab


  def __getstate__(						self :	List[Any]			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  self.__dict__.copy()
     _a							:      Union[str, Any]     =  None
     return state


  def __setstate__(						self :	Any				,_a :	List[str]			):


     '''simple docstring'''






     _a							:      int     =  d

     # for backward compatibility
     if not hasattr(self				,'sp_model_kwargs'			):
        _a							:      Optional[int]     =  {}

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


  def     __lowercase						(						self :	int				,_a :	str			):


     '''simple docstring'''






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


  def     __lowercase						(						self :	List[Any]				,_a :	Tuple			):


     '''simple docstring'''






     return self.sp_model.piece_to_id(__lowerCAmelCase			)


  def     __lowercase						(						self :	Optional[int]				,_a :	str			):


     '''simple docstring'''






     _a							:      Any     =  self.sp_model.IdToPiece(__lowerCAmelCase			)
     return token


  def     __lowercase						(						self :	Dict				,_a :	Optional[Any]			):


     '''simple docstring'''






     _a							:      List[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(__lowerCAmelCase			) + token
           _a							:      List[Any]     =  []
        else:
           current_sub_tokens.append(__lowerCAmelCase			)
     out_string += self.sp_model.decode(__lowerCAmelCase			)
     return out_string.strip()


  def     __lowercase						(						self :	Union[str, Any]				,_a :	str				,_a :	int=None			):


     '''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     __lowercase						(						self :	Union[str, Any]				,_a :	List[int]				,_a :	Optional[List[int]] = None				,_a :	bool = False			):


     '''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			)

     _a							:      Union[str, Any]     =  [1]
     if token_ids_a is None:
        return ([0] * len(__lowerCAmelCase			)) + suffix_ones
     return ([0] * len(__lowerCAmelCase			)) + ([0] * len(__lowerCAmelCase			)) + suffix_ones


  def     __lowercase						(						self :	Union[str, Any]				,_a :	str				,_a :	Optional[str] = None			):


     '''simple docstring'''






     if not os.path.isdir(__lowerCAmelCase			):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
        return
     _a							:      Union[str, Any]     =  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							:      int     =  self.sp_model.serialized_model_proto()
           fi.write(__lowerCAmelCase			)

     return (out_vocab_file,)




"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				'dpt'


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any]=768 ,					__lowerCAmelCase						:	str=12 ,					__lowerCAmelCase						:	Optional[int]=12 ,					__lowerCAmelCase						:	Dict=3072 ,					__lowerCAmelCase						:	List[Any]="gelu" ,					__lowerCAmelCase						:	Tuple=0.0 ,					__lowerCAmelCase						:	str=0.0 ,					__lowerCAmelCase						:	List[Any]=0.02 ,					__lowerCAmelCase						:	Optional[Any]=1e-1_2 ,					__lowerCAmelCase						:	List[str]=384 ,					__lowerCAmelCase						:	Tuple=16 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Optional[Any]=True ,					__lowerCAmelCase						:	Optional[Any]=[2, 5, 8, 11] ,					__lowerCAmelCase						:	Any="project" ,					__lowerCAmelCase						:	Dict=[4, 2, 1, 0.5] ,					__lowerCAmelCase						:	int=[96, 192, 384, 768] ,					__lowerCAmelCase						:	Dict=256 ,					__lowerCAmelCase						:	Optional[Any]=-1 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	List[Any]=0.4 ,					__lowerCAmelCase						:	List[str]=255 ,					__lowerCAmelCase						:	List[Any]=0.1 ,					__lowerCAmelCase						:	Optional[int]=[1, 1024, 24, 24] ,					__lowerCAmelCase						:	List[Any]=[0, 1] ,					__lowerCAmelCase						:	Dict=None ,					**__lowerCAmelCase						:	str ,					):
      super().__init__(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   is_hybrid

      if self.is_hybrid:
        if backbone_config is None:
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   {
              """global_padding""": """same""",
              """layer_type""": """bottleneck""",
              """depths""": [3, 4, 9],
              """out_features""": ["""stage1""", """stage2""", """stage3"""],
              """embedding_dynamic_padding""": True,
          }
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          _UpperCAmelCase    			=   backbone_config
        else:
          raise ValueError(
              f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

        _UpperCAmelCase    			=   backbone_featmap_shape
        _UpperCAmelCase    			=   neck_ignore_stages

        if readout_type != "project":
          raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""       )
      else:
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   []

      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   layer_norm_eps
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   qkv_bias
      _UpperCAmelCase    			=   backbone_out_indices
      if readout_type not in ["ignore", "add", "project"]:
        raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""       )
      _UpperCAmelCase    			=   readout_type
      _UpperCAmelCase    			=   reassemble_factors
      _UpperCAmelCase    			=   neck_hidden_sizes
      _UpperCAmelCase    			=   fusion_hidden_size
      _UpperCAmelCase    			=   head_in_index
      _UpperCAmelCase    			=   use_batch_norm_in_fusion_residual
      # auxiliary head attributes (semantic segmentation)
      _UpperCAmelCase    			=   use_auxiliary_head
      _UpperCAmelCase    			=   auxiliary_loss_weight
      _UpperCAmelCase    			=   semantic_loss_ignore_index
      _UpperCAmelCase    			=   semantic_classifier_dropout


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   copy.deepcopy(self.__dict__       )

      if output["backbone_config"] is not None:
        _UpperCAmelCase    			=   self.backbone_config.to_dict()

      _UpperCAmelCase    			=   self.__class__.model_type
      return output
