





'''simple docstring'''







# 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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__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   UpperCamelCase__   (  self     :					int					):
								if self.post_processor is None:
												_a								= "microsoft/speecht5_hifigan"
								super().setup()




				def   UpperCamelCase__   (  self     :					Any					,				__a     :					Optional[int]					,				__a     :					Dict=None					):
								_a								= 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								= load_dataset("Matthijs/cmu-arctic-xvectors"					,				split="validation"					)
												_a								= torch.tensor(embeddings_dataset[73_05]["xvector"]					).unsqueeze(0					)

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




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					List[Any]					):
								with torch.no_grad():
												return self.model.generate_speech(**__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					Optional[Any]					):
								with torch.no_grad():
												return self.post_processor(__a					).cpu().detach()

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCamelCase__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(
          description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."							)
      parser.add_argument("--file_path",  type=__A,  default="data/dump.txt",  help="The path to the data."							)
      parser.add_argument("--tokenizer_type",  type=__A,  default="bert",  choices=["bert", "roberta", "gpt2"]							)
      parser.add_argument("--tokenizer_name",  type=__A,  default="bert-base-uncased",  help="The tokenizer to use."							)
      parser.add_argument("--dump_file",  type=__A,  default="data/dump",  help="The dump file prefix."							)
      UpperCAmelCase__								=  parser.parse_args()

      logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""							)
      if args.tokenizer_type == "bert":
            UpperCAmelCase__								=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase__								=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase__								=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

      logger.info(f"""Loading text from {args.file_path}"""							)
      with open(args.file_path,  "r",  encoding="utf8"							) as fp:
            UpperCAmelCase__								=  fp.readlines()

      logger.info("Start encoding"							)
      logger.info(f"""{len(__A							)} examples to process."""							)

      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  10_000
      UpperCAmelCase__								=  time.time()
      for text in data:
            UpperCAmelCase__								=  f"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase__								=  tokenizer.encode(__A,  add_special_tokens=__A							)
            rslt.append(__A							)

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase__								=  time.time()
                  logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""							)
                  UpperCAmelCase__								=  time.time()
      logger.info("Finished binarization"							)
      logger.info(f"""{len(__A							)} examples processed."""							)

      UpperCAmelCase__								=  f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase__								=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            UpperCAmelCase__								=  [np.uintaa(__A							) for d in rslt]
      else:
            UpperCAmelCase__								=  [np.intaa(__A							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f"""Dump to {dp_file}"""							)
      with open(__A,  "wb"							) as handle:
            pickle.dump(rslt_,  __A,  protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
      main()



