


import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


a : List[Any]		=				get_tests_dir('''fixtures/test_sentencepiece.model''')
a : int		=				get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

a : str		=				"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_       (				SCREAMING_SNAKE_CASE__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				CamembertTokenizer
					__UpperCAmelCase            =				CamembertTokenizerFast
					__UpperCAmelCase            =				True
					__UpperCAmelCase            =				True






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								__lowercase 					=   CamembertTokenizer(snake_case__						)
								tokenizer.save_pretrained(self.tmpdirname						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   "<pad>"
								__lowercase 					=   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__						) ,       snake_case__						)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__						) ,       snake_case__						)






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   list(self.get_tokenizer().get_vocab().keys()						)

								self.assertEqual(vocab_keys[0] ,       '''<s>NOTUSED'''						)
								self.assertEqual(vocab_keys[1] ,       '''<pad>'''						)
								self.assertEqual(vocab_keys[-1] ,       '''<mask>'''						)
								self.assertEqual(len(snake_case__						) ,       1_0_0_4						)






					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								self.assertEqual(self.get_tokenizer().vocab_size ,       1_0_0_5						)






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







								'''simple docstring'''

								__lowercase 					=   CamembertTokenizer(snake_case__						)
								tokenizer.save_pretrained(self.tmpdirname						)
								__lowercase 					=   CamembertTokenizerFast.from_pretrained(self.tmpdirname						)

								__lowercase 					=   "I was born in 92000, and this is falsé."

								__lowercase 					=   tokenizer.encode(snake_case__						)
								__lowercase 					=   rust_tokenizer.encode(snake_case__						)
								self.assertListEqual(snake_case__ ,       snake_case__						)

								__lowercase 					=   tokenizer.encode(snake_case__ ,       add_special_tokens=snake_case__						)
								__lowercase 					=   rust_tokenizer.encode(snake_case__ ,       add_special_tokens=snake_case__						)
								self.assertListEqual(snake_case__ ,       snake_case__						)

								# <unk> tokens are not the same for `rust` than for `slow`.
								# Because spm gives back raw token instead of `unk` in EncodeAsPieces
								# tokens = tokenizer.tokenize(sequence)
								__lowercase 					=   tokenizer.convert_ids_to_tokens(snake_case__						)
								__lowercase 					=   rust_tokenizer.tokenize(snake_case__						)
								self.assertListEqual(snake_case__ ,       snake_case__						)






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								if not self.test_rust_tokenizer:
											return

								__lowercase 					=   self.get_tokenizer()
								__lowercase 					=   self.get_rust_tokenizer()

								__lowercase 					=   "I was born in 92000, and this is falsé."

								__lowercase 					=   tokenizer.tokenize(snake_case__						)
								__lowercase 					=   rust_tokenizer.tokenize(snake_case__						)
								self.assertListEqual(snake_case__ ,       snake_case__						)

								__lowercase 					=   tokenizer.encode(snake_case__ ,       add_special_tokens=snake_case__						)
								__lowercase 					=   rust_tokenizer.encode(snake_case__ ,       add_special_tokens=snake_case__						)
								self.assertListEqual(snake_case__ ,       snake_case__						)

								__lowercase 					=   self.get_rust_tokenizer()
								__lowercase 					=   tokenizer.encode(snake_case__						)
								__lowercase 					=   rust_tokenizer.encode(snake_case__						)
								self.assertListEqual(snake_case__ ,       snake_case__						)






					@slow
					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# camembert is a french model. So we also use french texts.
								__lowercase 					=   [
								    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
								    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
								    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
								    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
								    "telles que la traduction et la synthèse de texte.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=snake_case__ ,       model_name='''camembert-base''' ,       revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf''' ,       sequences=snake_case__ ,       )






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   {
			    '''^''': 3,
			    '''*''': 2,
			    '''/''': 2,
			    '''%''': 2,
			    '''+''': 1,
			    '''-''': 1,
			}  # Priority of each operator
			__lowercase 					=   len(_UpperCamelCase  ) if (len(_UpperCamelCase  ) > 7) else 7

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

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

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

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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   list(infix[::-1]  )  # reverse the infix equation

			for i in range(len(_UpperCamelCase  )  ):
						if infix[i] == "(":
									__lowercase 					=   ''')'''  # change "(" to ")"
						elif infix[i] == ")":
									__lowercase 					=   '''('''  # change ")" to "("

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


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


