


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_xlnet import XLNetTokenizer
else:
     snake_case    :   int	    =					None


snake_case    :   Any	    =					logging.get_logger(__name__)

snake_case    :   List[str]	    =					{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case    :   List[Any]	    =					{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json''',
    },
}

snake_case    :   Any	    =					{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

snake_case    :   Dict	    =					'''▁'''

# Segments (not really needed)
snake_case    :   List[str]	    =					0
snake_case    :   Tuple	    =					1
snake_case    :   int	    =					2
snake_case    :   Tuple	    =					3
snake_case    :   List[str]	    =					4







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__				=	'left'
 SCREAMING_SNAKE_CASE__				=	XLNetTokenizer




 def __init__(		self ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=False ,  _lowerCamelCase=True ,  _lowerCamelCase=False ,  _lowerCamelCase="<s>" ,  _lowerCamelCase="</s>" ,  _lowerCamelCase="<unk>" ,  _lowerCamelCase="<sep>" ,  _lowerCamelCase="<pad>" ,  _lowerCamelCase="<cls>" ,  _lowerCamelCase="<mask>" ,  _lowerCamelCase=["<eop>", "<eod>"] ,  **_lowerCamelCase ,  ):
       # Mask token behave like a normal word, i.e. include the space before it
       a						:Union[str, Any]										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else mask_token

       super().__init__(
           vocab_file=_lowerCamelCase ,  tokenizer_file=_lowerCamelCase ,  do_lower_case=_lowerCamelCase ,  remove_space=_lowerCamelCase ,  keep_accents=_lowerCamelCase ,  bos_token=_lowerCamelCase ,  eos_token=_lowerCamelCase ,  unk_token=_lowerCamelCase ,  sep_token=_lowerCamelCase ,  pad_token=_lowerCamelCase ,  cls_token=_lowerCamelCase ,  mask_token=_lowerCamelCase ,  additional_special_tokens=_lowerCamelCase ,  **_lowerCamelCase ,  )

       a						:str										=	3
       a						:Any										=	do_lower_case
       a						:Tuple										=	remove_space
       a						:Optional[int]										=	keep_accents
       a						:Any										=	vocab_file
       a						:Any										=	False if not self.vocab_file else True




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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:List[Any]										=	[self.sep_token_id]
       a						:Any										=	[2]

       if token_ids_a is None:
             return len(token_ids_a + sep   ) * [0] + cls_segment_id
       return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''   )

       if not os.path.isdir(_lowerCamelCase   ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
             return
       a						:List[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   ):
             copyfile(self.vocab_file ,  _lowerCamelCase   )

       return (out_vocab_file,)









# 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()





