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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{'vocab_file': 'spm_char.model'}

UpperCamelCase__											=					{
    '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',
    }
}

UpperCamelCase__											=					{
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Any      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : int      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Dict      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : str      =			['input_ids', 'attention_mask']


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



         UpperCAmelCase__								=  {} if sp_model_kwargs is None else sp_model_kwargs

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

         UpperCAmelCase__								=  vocab_file

         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(__UpperCAmelCase		)


   @property
   def 			lowercase_      (self     :						Optional[Any]		)						->				Optional[int]:
         """simple docstring"""



         return self.sp_model.get_piece_size()


   def 			lowercase_      (self     :						List[Any]		)						->				Any:
         """simple docstring"""



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


   def __getstate__(self     :						Tuple		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.__dict__.copy()
         UpperCAmelCase__								=  None
         return state


   def __setstate__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  d

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

         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.vocab_file		)


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						str		)						->				List[str]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         return self.sp_model.piece_to_id(__UpperCAmelCase		)


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						Dict		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.sp_model.IdToPiece(__UpperCAmelCase		)
         return token


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[Any]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  ""
         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(__UpperCAmelCase		) + token
                     UpperCAmelCase__								=  []
               else:
                     current_sub_tokens.append(__UpperCAmelCase		)
         out_string += self.sp_model.decode(__UpperCAmelCase		)
         return out_string.strip()


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Optional[Any]=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 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						bool = False		)						->				List[int]:
         """simple docstring"""



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

         UpperCAmelCase__								=  [1]
         if token_ids_a is None:
               return ([0] * len(__UpperCAmelCase		)) + suffix_ones
         return ([0] * len(__UpperCAmelCase		)) + ([0] * len(__UpperCAmelCase		)) + suffix_ones





   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



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

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

         return (out_vocab_file,)




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.base_model(
             input_ids=__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase       ,  token_type_ids=__UpperCAmelCase       ,  position_ids=__UpperCAmelCase       ,  head_mask=__UpperCAmelCase       ,  inputs_embeds=__UpperCAmelCase       ,  encoder_hidden_states=__UpperCAmelCase       ,  encoder_attention_mask=__UpperCAmelCase       ,  output_attentions=__UpperCAmelCase       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=__UpperCAmelCase       ,  )

         if self.has_pre_transformation:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.transformation_pre(__UpperCAmelCase		)

               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
         else:
               UpperCAmelCase__								=  self.transformation(outputs.last_hidden_state		)
               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )



