


'''simple docstring'''
import os
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							      =     logging.get_logger(__name__)

__snake_case							      =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

__snake_case							      =     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

__snake_case							      =     {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__snake_case							      =     '''▁'''
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				VOCAB_FILES_NAMES
		_a     =				PRETRAINED_VOCAB_FILES_MAP
		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     =				['input_ids', 'attention_mask']




		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_="<s>"    ,   UpperCamelCase_="</s>"    ,   UpperCamelCase_="</s>"    ,   UpperCamelCase_="<s>"    ,   UpperCamelCase_="<unk>"    ,   UpperCamelCase_="<pad>"    ,   UpperCamelCase_="<mask>"    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= AddedToken(UpperCamelCase_    ,   lstrip=UpperCamelCase_    ,   rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_    ,   UpperCamelCase_							) else mask_token

							UpperCamelCase__   :Tuple												= {} if sp_model_kwargs is None else sp_model_kwargs

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

							UpperCamelCase__   :List[str]												= vocab_file
							UpperCamelCase__   :List[Any]												= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(str(UpperCamelCase_							)							)

							UpperCamelCase__   :Optional[int]												= {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							UpperCamelCase__   :Optional[int]												= len(self.sp_model							) - 1
							UpperCamelCase__   :Optional[int]												= {v: k for k, v in self.fairseq_tokens_to_ids.items()}




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCamelCase__   :Optional[int]												= [self.cls_token_id]
							UpperCamelCase__   :List[Any]												= [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False							):

							'''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_							)

							if token_ids_a is None:
												return [1] + ([0] * len(UpperCamelCase_							)) + [1]
							return [1] + ([0] * len(UpperCamelCase_							)) + [1, 1] + ([0] * len(UpperCamelCase_							)) + [1]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= [self.sep_token_id]
							UpperCamelCase__   :int												= [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 + sep + token_ids_a + sep							) * [0]




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return len(self.sp_model							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							UpperCamelCase__   :List[Any]												= self.sp_model.PieceToId(UpperCamelCase_							)

							return spm_id if spm_id else self.unk_token_id




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= []
							UpperCamelCase__   :Optional[int]												= ''''''
							UpperCamelCase__   :str												= 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(UpperCamelCase_							) + token
																	UpperCamelCase__   :List[str]												= True
																	UpperCamelCase__   :Any												= []
												else:
																	current_sub_tokens.append(UpperCamelCase_							)
																	UpperCamelCase__   :Union[str, Any]												= False
							out_string += self.sp_model.decode(UpperCamelCase_							)
							return out_string.strip()




		def __getstate__(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.__dict__.copy()
							UpperCamelCase__   :str												= None
							return state




		def __setstate__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= d

							# for backward compatibility
							if not hasattr(self    ,   '''sp_model_kwargs'''							):
												UpperCamelCase__   :List[str]												= {}

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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							if not os.path.isdir(UpperCamelCase_							):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
												return
							UpperCamelCase__   :Optional[int]												= 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__   :Optional[Any]												= self.sp_model.serialized_model_proto()
																	fi.write(UpperCamelCase_							)

							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		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "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."
    )
    _a		:						Dict=				"text_reader"
    _a		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

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

                  lowercase							:     Tuple       	=      load_dataset("""Matthijs/cmu-arctic-xvectors"""		,split="""validation"""      )
                  lowercase							:     List[str]       	=      torch.tensor(embeddings_dataset[7305]["""xvector"""]      ).unsqueeze(0      )

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.post_processor(snake_case      ).cpu().detach()
