






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






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






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      str:
							shutil.rmtree(self.tmpdirname       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Union[str, Any]:
							a_		:	Optional[int]									=   self.get_tokenizer()

							a_		:	List[str]									=   BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE       )

							processor.save_pretrained(self.tmpdirname       )
							a_		:	Optional[int]									=   BarkProcessor.from_pretrained(self.tmpdirname       )

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






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[int]:
							a_		:	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	,    )

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

							a_		:	Optional[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       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      List[str]:
							a_		:	int									=   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint	,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,    )

							a_		:	Tuple									=   35
							a_		:	Optional[Any]									=   2
							a_		:	Any									=   8

							a_		:	Any									=   {
							    '''semantic_prompt''': np.ones(__SCREAMING_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
							a_		:	Optional[Any]									=   processor(text=self.input_string	,    voice_preset=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   inputs['''history_prompt''']
							for key in voice_preset:
											self.assertListEqual(voice_preset[key].tolist()	,    processed_voice_preset.get(__SCREAMING_SNAKE_CASE	,    np.array([]       )       ).tolist()       )

							# test loading voice preset from npz file
							a_		:	Dict									=   os.path.join(self.tmpdirname	,    '''file.npz'''       )
							np.savez(__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   processor(text=self.input_string	,    voice_preset=__SCREAMING_SNAKE_CASE       )
							a_		:	str									=   inputs['''history_prompt''']

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

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







			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							a_		:	Union[str, Any]									=   self.get_tokenizer()

							a_		:	str									=   BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   processor(text=self.input_string       )

							a_		:	int									=   tokenizer(
							    self.input_string	,    padding='''max_length'''	,    max_length=256	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    return_attention_mask=__SCREAMING_SNAKE_CASE	,    return_token_type_ids=__SCREAMING_SNAKE_CASE	,    )

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












'''simple docstring'''







def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   []
				a_		:	Optional[Any]									=   []
				a_		:	List[str]									=   {
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				a_		:	int									=   len(__A   ) if (len(__A   ) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8   )    ,		'''Stack'''.center(__A   )    ,		'''Postfix'''.center(__A   )    ,		sep=''' | '''    ,		)
				print('''-''' * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(__A   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(__A   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(__A   ) == 0:
																stack.append(__A   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(__A   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(__A   )  # push x to stack

								print(
								    x.center(8   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		sep=''' | '''    ,		)  # Output in tabular format

				while len(__A   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    ''' '''.center(8   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		sep=''' | '''    ,		)  # Output in tabular format

				return "".join(__A   )  # return Postfix as str





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Union[str, Any]									=   list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(__A   )   ):
								if infix[i] == "(":
												a_		:	List[str]									=   ''')'''  # change "(" to ")"
								elif infix[i] == ")":
												a_		:	str									=   '''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(__A   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		__lowerCAmelCase									=   input('\nEnter an Infix Equation = ')  # Input an Infix equation
		__lowerCAmelCase									=   ''.join(Infix.split())  # Remove spaces from the input
		print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')




