

'''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				:     Optional[int]			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='ylacombe/bark-small'
       _SCREAMING_SNAKE_CASE							   =tempfile.mkdtemp()
       _SCREAMING_SNAKE_CASE							   ='en_speaker_1'
       _SCREAMING_SNAKE_CASE							   ='This is a test string'
       _SCREAMING_SNAKE_CASE							   ='speaker_embeddings_path.json'
       _SCREAMING_SNAKE_CASE							   ='speaker_embeddings'





  def 						A  (		self				:     Union[str, Any]							, **_a				:     Optional[Any]			)     ->   Tuple:





       '''simple docstring'''






       return AutoTokenizer.from_pretrained(self.checkpoint							, **__snake_case			)





  def 						A  (		self				:     Union[str, Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       shutil.rmtree(self.tmpdirname			)





  def 						A  (		self				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer()

       _SCREAMING_SNAKE_CASE							   =BarkProcessor(tokenizer=__snake_case			)

       processor.save_pretrained(self.tmpdirname			)
       _SCREAMING_SNAKE_CASE							   =BarkProcessor.from_pretrained(self.tmpdirname			)

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





  @slow
  def 						A  (		self				:     List[str]			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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							, )

       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer(bos_token='(BOS)'							, eos_token='(EOS)'			)

       _SCREAMING_SNAKE_CASE							   =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				:     int			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							, )

       _SCREAMING_SNAKE_CASE							   =35
       _SCREAMING_SNAKE_CASE							   =2
       _SCREAMING_SNAKE_CASE							   =8

       _SCREAMING_SNAKE_CASE							   ={
           'semantic_prompt': np.ones(__snake_case			),
           'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)			),
           'fine_prompt': np.ones((nb_codebooks_total, seq_len)			),
       }

       # test providing already loaded voice_preset
       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string							, voice_preset=__snake_case			)

       _SCREAMING_SNAKE_CASE							   =inputs['history_prompt']
       for key in voice_preset:
            self.assertListEqual(voice_preset[key].tolist()							, processed_voice_preset.get(__snake_case							, np.array([]			)			).tolist()			)

       # test loading voice preset from npz file
       _SCREAMING_SNAKE_CASE							   =os.path.join(self.tmpdirname							, 'file.npz'			)
       np.savez(__snake_case							, **__snake_case			)
       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string							, voice_preset=__snake_case			)
       _SCREAMING_SNAKE_CASE							   =inputs['history_prompt']

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

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





  def 						A  (		self				:     int			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer()

       _SCREAMING_SNAKE_CASE							   =BarkProcessor(tokenizer=__snake_case			)

       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string			)

       _SCREAMING_SNAKE_CASE							   =tokenizer(
           self.input_string							, padding='max_length'							, max_length=256							, add_special_tokens=__snake_case							, return_attention_mask=__snake_case							, return_token_type_ids=__snake_case							, )

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



'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter


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

if __name__ == "__main__":
     lowerCamelCase			:				Optional[Any] 			=					argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0_5_2_2, type=int)
     lowerCamelCase			:				Optional[Any] 			=					parser.parse_args()

     logger.info(f'''Loading data from {args.data_file}''')
     with open(args.data_file, "rb") as fp:
          lowerCamelCase			:				Optional[int] 			=					pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     lowerCamelCase			:				Dict 			=					Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     lowerCamelCase			:				Tuple 			=					[0] * args.vocab_size
     for k, v in counter.items():
          lowerCamelCase			:				Any 			=					v

     logger.info(f'''Dump to {args.token_counts_dump}''')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
