





'''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  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		'''ylacombe/bark-small'''
             _lowerCamelCase							:     Optional[Any]	=		tempfile.mkdtemp()
             _lowerCamelCase							:     Tuple	=		'''en_speaker_1'''
             _lowerCamelCase							:     str	=		'''This is a test string'''
             _lowerCamelCase							:     Any	=		'''speaker_embeddings_path.json'''
             _lowerCamelCase							:     List[str]	=		'''speaker_embeddings'''


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.get_tokenizer()

             _lowerCamelCase							:     Dict	=		BarkProcessor(tokenizer=a__)

             processor.save_pretrained(self.tmpdirname)
             _lowerCamelCase							:     Tuple	=		BarkProcessor.from_pretrained(self.tmpdirname)

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


      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		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							:     str	=		self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)''')

             _lowerCamelCase							:     Union[str, 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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		BarkProcessor.from_pretrained(
                 pretrained_processor_name_or_path=self.checkpoint      ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,							)

             _lowerCamelCase							:     Tuple	=		35
             _lowerCamelCase							:     List[str]	=		2
             _lowerCamelCase							:     int	=		8

             _lowerCamelCase							:     Tuple	=		{
                 '''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							:     int	=		processor(text=self.input_string      ,							voice_preset=a__)

             _lowerCamelCase							:     Dict	=		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							:     Union[str, Any]	=		os.path.join(self.tmpdirname      ,							'''file.npz''')
             np.savez(a__      ,							**a__)
             _lowerCamelCase							:     str	=		processor(text=self.input_string      ,							voice_preset=a__)
             _lowerCamelCase							:     Any	=		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							:     Union[str, Any]	=		processor(text=self.input_string      ,							voice_preset=self.voice_preset)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.get_tokenizer()

             _lowerCamelCase							:     List[str]	=		BarkProcessor(tokenizer=a__)

             _lowerCamelCase							:     Union[str, Any]	=		processor(text=self.input_string)

             _lowerCamelCase							:     List[Any]	=		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())








def    __UpperCAmelCase(      lowercase_      ):
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       _lowerCamelCase							:     Tuple	=		len(lowercase_      )
       _lowerCamelCase							:     List[str]	=		max(lowercase_      )
       _lowerCamelCase							:     Any	=		min(lowercase_      )

       # create the counting array
       _lowerCamelCase							:     str	=		coll_max + 1 - coll_min
       _lowerCamelCase							:     Union[str, Any]	=		[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,    lowercase_      ):
              _lowerCamelCase							:     Union[str, Any]	=		counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       _lowerCamelCase							:     Dict	=		[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,    lowercase_      )      ):
              _lowerCamelCase							:     Union[str, Any]	=		collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered




def    __UpperCAmelCase(      lowercase_      ):
       return "".join([chr(lowercase_      ) for i in counting_sort([ord(lowercase_      ) for c in string]      )]      )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))

