




# 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 		lowercase							(     UpperCamelCase__	):
     _a											=		"microsoft/speecht5_tts"
     _a											=		(
         "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											=		"text_reader"
     _a											=		SpeechTaProcessor
     _a											=		SpeechTaForTextToSpeech
     _a											=		SpeechTaHifiGan

     _a											=		["text"]
     _a											=		["audio"]

     def       a__			(						self       )		->							str:
           if self.post_processor is None:
                 _A							:  List[str]    =		"""microsoft/speecht5_hifigan"""
           super().setup()

     def       a__			(						self	,  _a	,  _a=None       )		->							Optional[Any]:
           _A							:  Union[str, Any]    =		self.pre_processor(text=_a	,  return_tensors="""pt"""	,  truncation=_a       )

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

                 _A							:  Optional[int]    =		load_dataset("""Matthijs/cmu-arctic-xvectors"""	,  split="""validation"""       )
                 _A							:  Union[str, Any]    =		torch.tensor(embeddings_dataset[7305]["""xvector"""]       ).unsqueeze(0       )

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

     def       a__			(						self	,  _a       )		->							List[Any]:
           with torch.no_grad():
                 return self.model.generate_speech(**_a       )



     def       a__			(						self	,  _a       )		->							List[str]:
           with torch.no_grad():
                 return self.post_processor(_a       ).cpu().detach()







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

