"""simple docstring"""




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
)
_a      :		Any           =   logging.getLogger(__name__)





def 							SCREAMING_SNAKE_CASE     (			)       ->      Union[str, Any]:
  _lowerCAmelCase						:       List[Any]  			=     argparse.ArgumentParser(
      description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""	)
  parser.add_argument("""--file_path"""			,type=_lowerCamelCase			,default="""data/dump.txt"""			,help="""The path to the data."""	)
  parser.add_argument("""--tokenizer_type"""			,type=_lowerCamelCase			,default="""bert"""			,choices=["""bert""", """roberta""", """gpt2"""]	)
  parser.add_argument("""--tokenizer_name"""			,type=_lowerCamelCase			,default="""bert-base-uncased"""			,help="""The tokenizer to use."""	)
  parser.add_argument("""--dump_file"""			,type=_lowerCamelCase			,default="""data/dump"""			,help="""The dump file prefix."""	)
  _lowerCAmelCase						:       int  			=     parser.parse_args()

  logger.info(f"Loading Tokenizer ({args.tokenizer_name})"	)
  if args.tokenizer_type == "bert":
    _lowerCAmelCase						:       int  			=     BertTokenizer.from_pretrained(args.tokenizer_name	)
    _lowerCAmelCase						:       List[Any]  			=     tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
    _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
  elif args.tokenizer_type == "roberta":
    _lowerCAmelCase						:       Optional[Any]  			=     RobertaTokenizer.from_pretrained(args.tokenizer_name	)
    _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
    _lowerCAmelCase						:       Dict  			=     tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
  elif args.tokenizer_type == "gpt2":
    _lowerCAmelCase						:       Union[str, Any]  			=     GPTaTokenizer.from_pretrained(args.tokenizer_name	)
    _lowerCAmelCase						:       Any  			=     tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
    _lowerCAmelCase						:       List[Any]  			=     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:
    _lowerCAmelCase						:       Tuple  			=     fp.readlines()

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

  _lowerCAmelCase						:       Dict  			=     []
  _lowerCAmelCase						:       List[Any]  			=     0
  _lowerCAmelCase						:       List[str]  			=     10000
  _lowerCAmelCase						:       str  			=     time.time()
  for text in data:
    _lowerCAmelCase						:       Optional[int]  			=     f"{bos} {text.strip()} {sep}"
    _lowerCAmelCase						:       Dict  			=     tokenizer.encode(_lowerCamelCase			,add_special_tokens=_lowerCamelCase	)
    rslt.append(_lowerCamelCase	)

    iter += 1
    if iter % interval == 0:
      _lowerCAmelCase						:       int  			=     time.time()
      logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"	)
      _lowerCAmelCase						:       List[Any]  			=     time.time()
  logger.info("""Finished binarization"""	)
  logger.info(f"{len(_lowerCamelCase	)} examples processed."	)

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


if __name__ == "__main__":
     main()





"""simple docstring"""




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer.get_vocab()  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )
       processor.save_pretrained(
           self.tmpdirname				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     speaker_embeddings_directory=self.speaker_embeddings_directory				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor.from_pretrained(
           self.tmpdirname				,     self.speaker_embeddings_dict_path				,     bos_token="""(BOS)"""				,     eos_token="""(EOS)"""				,     )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer_add_kwargs.get_vocab()  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """semantic_prompt""": np.ones(a__  ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
       }

       # test providing already loaded voice_preset
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from npz file
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=a__				,     return_attention_mask=a__				,     return_token_type_ids=a__				,     )

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]				,     encoded_processor[key].squeeze().tolist()  )




