





'''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 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					Optional[int]						=			BarkProcessor(tokenizer=_UpperCAmelCase)

          processor.save_pretrained(self.tmpdirname)
          __A		:					Optional[Any]						=			BarkProcessor.from_pretrained(self.tmpdirname)

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


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			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		:					Dict						=			self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)')

          __A		:					List[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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint		,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,      )

          __A		:					str						=			35
          __A		:					Tuple						=			2
          __A		:					str						=			8

          __A		:					Union[str, Any]						=			{
              '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		:					int						=			processor(text=self.input_string		,      voice_preset=_UpperCAmelCase)

          __A		:					int						=			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		:					int						=			os.path.join(self.tmpdirname		,      'file.npz')
          np.savez(_UpperCAmelCase		,      **_UpperCAmelCase)
          __A		:					Optional[int]						=			processor(text=self.input_string		,      voice_preset=_UpperCAmelCase)
          __A		:					Any						=			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		:					List[Any]						=			processor(text=self.input_string		,      voice_preset=self.voice_preset)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.get_tokenizer()

          __A		:					Optional[int]						=			BarkProcessor(tokenizer=_UpperCAmelCase)

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

          __A		:					str						=			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'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

	for i in equation:
		if i.isdigit():
			# RULE 1
			operand_stack.push(int(UpperCAmelCase_							)							)
		elif i in operators:
			# RULE 2
			operator_stack.push(UpperCAmelCase_							)
		elif i == ")":
			# RULE 4
			snake_case__	       =   operator_stack.peek()
			operator_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


if __name__ == "__main__":
			a__					:			Any          =     '''(5 + ((4 * 2) * (2 + 3)))'''
			# answer = 45
			print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



