



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        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    'ylacombe/bark-small'
											A      						=    tempfile.mkdtemp()
											A      						=    'en_speaker_1'
											A      						=    'This is a test string'
											A      						=    'speaker_embeddings_path.json'
											A      						=    'speaker_embeddings'


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  Union[str, Any]       )		->     Optional[int]:
											return AutoTokenizer.from_pretrained(self.checkpoint						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     Union[str, Any]:
											shutil.rmtree(self.tmpdirname       )


				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											A      						=    self.get_tokenizer()

											A      						=    BarkProcessor(tokenizer=__UpperCamelCase       )

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

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


				@slow
				def __UpperCamelCase						(							self      :  Tuple       )		->     int:
											A      						=    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						, )

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

											A      						=    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       )		->     List[str]:
											A      						=    BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint						, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						, )

											A      						=    35
											A      						=    2
											A      						=    8

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

											# test providing already loaded voice_preset
											A      						=    processor(text=self.input_string						, voice_preset=__UpperCamelCase       )

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

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

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

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


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Union[str, Any]:
											A      						=    self.get_tokenizer()

											A      						=    BarkProcessor(tokenizer=__UpperCamelCase       )

											A      						=    processor(text=self.input_string       )

											A      						=    tokenizer(
											    self.input_string						, padding='max_length'						, max_length=256						, add_special_tokens=__UpperCamelCase						, return_attention_mask=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase						, )

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






'''simple docstring'''

import numpy as np







class __a			:





   def __init__(       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							(0, 0)
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0





   def __eq__(       self					:      int					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return self.position == cell.position





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

      '''simple docstring'''

      print(self.position     )







class __a			:





   def __init__(       self					:      int					,lowerCamelCase					:      Optional[int]=(5, 5)     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.zeros(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							world_size[0]
      __SCREAMING_SNAKE_CASE   				=							world_size[1]





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      print(self.w     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          (-1, -1),
          (-1, 0),
          (-1, 1),
          (0, -1),
          (0, 1),
          (1, -1),
          (1, 0),
          (1, 1),
      ]
      __SCREAMING_SNAKE_CASE   				=							cell.position[0]
      __SCREAMING_SNAKE_CASE   				=							cell.position[1]
      __SCREAMING_SNAKE_CASE   				=							[]
      for n in neughbour_cord:
         __SCREAMING_SNAKE_CASE   				=							current_x + n[0]
         __SCREAMING_SNAKE_CASE   				=							current_y + n[1]
         if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            __SCREAMING_SNAKE_CASE   				=							Cell()
            __SCREAMING_SNAKE_CASE   				=							(x, y)
            __SCREAMING_SNAKE_CASE   				=							cell
            neighbours.append(lowerCamelCase     )
      return neighbours





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							[]
   _open.append(__UpperCAmelCase   )

   while _open:
      __SCREAMING_SNAKE_CASE   				=							np.argmin([n.f for n in _open]   )
      __SCREAMING_SNAKE_CASE   				=							_open[min_f]
      _closed.append(_open.pop(__UpperCAmelCase   )   )
      if current == goal:
         break
      for n in world.get_neigbours(__UpperCAmelCase   ):
         for c in _closed:
            if c == n:
               continue
         __SCREAMING_SNAKE_CASE   				=							current.g + 1
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							n.position
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							goal.position
         __SCREAMING_SNAKE_CASE   				=							(ya - ya) ** 2 + (xa - xa) ** 2
         __SCREAMING_SNAKE_CASE   				=							n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							[]
   while current.parent is not None:
      path.append(current.position   )
      __SCREAMING_SNAKE_CASE   				=							current.parent
   path.append(current.position   )
   return path[::-1]


if __name__ == "__main__":
    a   	=    Gridworld()
    # Start position and goal
    a   	=    Cell()
    a   	=    (0, 0)
    a   	=    Cell()
    a   	=    (4, 4)
    print(F'''path from {start.position} to {goal.position}''')
    a   	=    astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        a   	=    1
    print(world.w)





