





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        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       """ylacombe/bark-small"""
   __lowerCAmelCase					:	Union[str, Any]      =       tempfile.mkdtemp()
   __lowerCAmelCase					:	int      =       """en_speaker_1"""
   __lowerCAmelCase					:	Optional[Any]      =       """This is a test string"""
   __lowerCAmelCase					:	Union[str, Any]      =       """speaker_embeddings_path.json"""
   __lowerCAmelCase					:	Union[str, Any]      =       """speaker_embeddings"""






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






   '''simple docstring'''

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






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






   '''simple docstring'''

   shutil.rmtree(self.tmpdirname      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       self.get_tokenizer()

   __lowerCAmelCase					:	List[str]      =       BarkProcessor(tokenizer=__snake_case      )

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

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






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






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[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					:	Dict      =       self.get_tokenizer(bos_token="""(BOS)"""       ,  eos_token="""(EOS)"""      )

   __lowerCAmelCase					:	str      =       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 				UpperCAmelCase__						(							self			:					int      )->int:






   '''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       ,  )

   __lowerCAmelCase					:	Any      =       35
   __lowerCAmelCase					:	Optional[int]      =       2
   __lowerCAmelCase					:	List[str]      =       8

   __lowerCAmelCase					:	Any      =       {
       """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
   __lowerCAmelCase					:	Any      =       processor(text=self.input_string       ,  voice_preset=__snake_case      )

   __lowerCAmelCase					:	str      =       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
   __lowerCAmelCase					:	str      =       os.path.join(self.tmpdirname       ,  """file.npz"""      )
   np.savez(__snake_case       ,  **__snake_case      )
   __lowerCAmelCase					:	Dict      =       processor(text=self.input_string       ,  voice_preset=__snake_case      )
   __lowerCAmelCase					:	Optional[Any]      =       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
   __lowerCAmelCase					:	List[str]      =       processor(text=self.input_string       ,  voice_preset=self.voice_preset      )






  def 				UpperCAmelCase__						(							self			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.get_tokenizer()

   __lowerCAmelCase					:	Optional[Any]      =       BarkProcessor(tokenizer=__snake_case      )

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

   __lowerCAmelCase					:	Dict      =       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()      )






from __future__ import annotations

import time

import numpy as np

_UpperCAmelCase             =							[8, 5, 9, 7]
_UpperCAmelCase             =							[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_UpperCAmelCase             =							[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class        snake_case_    :






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					list[int]       ,  _snake_case			:					list[list[int]]       ,  _snake_case			:					list[list[int]]       ,  )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       claim_vector
   __lowerCAmelCase					:	List[Any]      =       allocated_resources_table
   __lowerCAmelCase					:	str      =       maximum_claim_table






  def 				UpperCAmelCase__						(							self			:					Tuple      )->list[int]:






   '''simple docstring'''

   return [
       sum(p_item[i] for p_item in self.__allocated_resources_table      )
       for i in range(len(self.__allocated_resources_table[0]      )      )
   ]






  def 				UpperCAmelCase__						(							self			:					int      )->list[int]:






   '''simple docstring'''

   return np.array(self.__claim_vector      ) - np.array(
       self.__processes_resource_summation()      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->list[list[int]]:






   '''simple docstring'''

   return [
       list(np.array(self.__maximum_claim_table[i]      ) - np.array(_snake_case      )      )
       for i, allocated_resource in enumerate(self.__allocated_resources_table      )
   ]






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->dict[int, list[int]]:






   '''simple docstring'''

   return {self.__need().index(_snake_case      ): i for i in self.__need()}






  def 				UpperCAmelCase__						(							self			:					Dict       ,  **_snake_case			:					Optional[Any]      )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self.__need()
   __lowerCAmelCase					:	Any      =       self.__allocated_resources_table
   __lowerCAmelCase					:	List[Any]      =       self.__available_resources()
   __lowerCAmelCase					:	Optional[Any]      =       self.__need_index_manager()
   for kw, val in kwargs.items():
    if kw and val is True:
     self.__pretty_data()
   print("""_""" * 50 + """\n"""      )
   while need_list:
    __lowerCAmelCase					:	Optional[Any]      =       False
    for each_need in need_list:
     __lowerCAmelCase					:	Optional[int]      =       True
     for index, need in enumerate(_snake_case      ):
      if need > available_resources[index]:
       __lowerCAmelCase					:	int      =       False
       break
     if execution:
      __lowerCAmelCase					:	int      =       True
      # get the original index of the process from ind_ctrl db
      for original_need_index, need_clone in need_index_manager.items():
       if each_need == need_clone:
        __lowerCAmelCase					:	Any      =       original_need_index
      print(F'''Process {process_number + 1} is executing.'''      )
      # remove the process run from stack
      need_list.remove(_snake_case      )
      # update available/freed resources stack
      __lowerCAmelCase					:	int      =       np.array(_snake_case      ) + np.array(
          alloc_resources_table[process_number]      )
      print(
          """Updated available resource stack for processes: """
          + """ """.join([str(_snake_case      ) for x in available_resources]      )      )
      break
    if safe:
     print("""The process is in a safe state.\n"""      )
    else:
     print("""System in unsafe state. Aborting...\n"""      )
     break






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






   '''simple docstring'''

   print(""" """ * 9 + """Allocated Resource Table"""      )
   for item in self.__allocated_resources_table:
    print(
        F'''P{self.__allocated_resources_table.index(_snake_case      ) + 1}'''
        + """ """.join(F'''{it:>8}''' for it in item      )
        + """\n"""      )
   print(""" """ * 9 + """System Resource Table"""      )
   for item in self.__maximum_claim_table:
    print(
        F'''P{self.__maximum_claim_table.index(_snake_case      ) + 1}'''
        + """ """.join(F'''{it:>8}''' for it in item      )
        + """\n"""      )
   print(
       """Current Usage by Active Processes: """
       + """ """.join(str(_snake_case      ) for x in self.__claim_vector      )      )
   print(
       """Initial Available Resources:       """
       + """ """.join(str(_snake_case      ) for x in self.__available_resources()      )      )
   time.sleep(1      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()