


import os
import re
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


snake_case    :   Dict	    =					logging.get_logger(__name__)

snake_case    :   Optional[int]	    =					{'''vocab_file''': '''spiece.model'''}

snake_case    :   List[Any]	    =					{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

snake_case    :   Dict	    =					{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__				=	['input_ids', 'attention_mask']
 SCREAMING_SNAKE_CASE__				=	[]




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase="<unk>" ,  _lowerCamelCase="<s>" ,  _lowerCamelCase="</s>" ,  _lowerCamelCase="<pad>" ,  _lowerCamelCase="[SEP]" ,  _lowerCamelCase="[MASK]" ,  _lowerCamelCase="[CLS]" ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       a						:Union[str, Any]										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else bos_token
       a						:Optional[int]										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else eos_token
       a						:List[str]										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else unk_token
       a						:int										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else pad_token
       a						:int										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else cls_token
       a						:List[str]										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       a						:List[Any]										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else mask_token

       a						:Optional[Any]										=	{} if sp_model_kwargs is None else sp_model_kwargs

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

       a						:Dict										=	vocab_file

       a						:Optional[Any]										=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_lowerCamelCase   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self.sp_model.get_piece_size()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	{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   ):
       a						:Dict										=	self.__dict__.copy()
       a						:List[str]										=	None
       return state




 def __setstate__(		self ,  _lowerCamelCase   ):
       a						:int										=	d

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

       a						:Dict										=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.sp_model.encode(_lowerCamelCase ,  out_type=_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.sp_model.piece_to_id(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	self.sp_model.IdToPiece(_lowerCamelCase   )
       return token




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	[]
       a						:Dict										=	''''''
       a						:Optional[Any]										=	False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(_lowerCamelCase   ) + token
                   a						:Tuple										=	True
                   a						:Any										=	[]
             else:
                   current_sub_tokens.append(_lowerCamelCase   )
                   a						:List[Any]										=	False
       out_string += self.sp_model.decode(_lowerCamelCase   )
       return out_string.strip()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = True ,  **_lowerCamelCase ,  ):
       a						:int										=	kwargs.pop('''use_source_tokenizer''' ,  _lowerCamelCase   )

       a						:Union[str, Any]										=	self.convert_ids_to_tokens(_lowerCamelCase ,  skip_special_tokens=_lowerCamelCase   )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       a						:List[str]										=	[]
       a						:Union[str, Any]										=	[]
       for token in filtered_tokens:
             if skip_special_tokens and token in self.all_special_ids:
                   continue
             if token in self.added_tokens_encoder:
                   if current_sub_text:
                         sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase   )   )
                         a						:str										=	[]
                   sub_texts.append(_lowerCamelCase   )
             else:
                   current_sub_text.append(_lowerCamelCase   )
       if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase   )   )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
             a						:Optional[Any]										=	re.sub(R''' (\[(MASK|SEP)\])''' ,  R'''\1''' ,  ''' '''.join(_lowerCamelCase   )   )
       else:
             a						:str										=	''''''.join(_lowerCamelCase   )

       a						:str										=	(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
             a						:str										=	self.clean_up_tokenization(_lowerCamelCase   )
             return clean_text
       else:
             return text




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       if not os.path.isdir(_lowerCamelCase   ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
             return
       a						:Dict										=	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						:Any										=	self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase   )

       return (out_vocab_file,)




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a						:Optional[int]										=	[self.cls_token_id]
       a						:Any										=	[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = False   ):
       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] + ([0] * len(_lowerCamelCase   )) + [1]




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Dict										=	[self.sep_token_id]
       a						: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   ) * [0] + len(token_ids_a + sep   ) * [1]









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
     from datasets import load_dataset







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'microsoft/speecht5_tts'
 SCREAMING_SNAKE_CASE__				=	(
     'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
     'text to read (in English) and returns a waveform object containing the sound.'
 )
 SCREAMING_SNAKE_CASE__				=	'text_reader'
 SCREAMING_SNAKE_CASE__				=	SpeechTaProcessor
 SCREAMING_SNAKE_CASE__				=	SpeechTaForTextToSpeech
 SCREAMING_SNAKE_CASE__				=	SpeechTaHifiGan

 SCREAMING_SNAKE_CASE__				=	['text']
 SCREAMING_SNAKE_CASE__				=	['audio']




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.post_processor is None:
             a						:List[Any]										=	'''microsoft/speecht5_hifigan'''
       super().setup()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						:Tuple										=	self.pre_processor(text=_lowerCamelCase ,  return_tensors='''pt''' ,  truncation=_lowerCamelCase   )

       if speaker_embeddings is None:
             if not is_datasets_available():
                   raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''   )

             a						:List[Any]										=	load_dataset('''Matthijs/cmu-arctic-xvectors''' ,  split='''validation'''   )
             a						:int										=	torch.tensor(embeddings_dataset[7305]['''xvector''']   ).unsqueeze(0   )

       return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.model.generate_speech(**_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.post_processor(_lowerCamelCase   ).cpu().detach()





