"""simple docstring"""







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 						UpperCamelCase     (       lowercase ,      unittest.TestCase		):
				UpperCAmelCase							: List[str]       =						BartTokenizer
				UpperCAmelCase							: Dict       =						BartTokenizerFast
				UpperCAmelCase							: int       =						True
				UpperCAmelCase							: int       =						filter_roberta_detectors



				def    _lowercase	(self							: Union[str, Any])       ->      List[str]:
					super().setUp()
					__snake_case     :				Optional[Any] 						=					[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__snake_case     :				int 						=					dict(zip(_A      ,    range(len(_A))))
					__snake_case     :				str 						=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__snake_case     :				Any 						=					{'unk_token': '<unk>'}

					__snake_case     :				List[Any] 						=					os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES['vocab_file'])
					__snake_case     :				List[Any] 						=					os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES['merges_file'])
					with open(self.vocab_file      ,    'w'      ,    encoding='utf-8') as fp:
						fp.write(json.dumps(_A) + '\n')
					with open(self.merges_file      ,    'w'      ,    encoding='utf-8') as fp:
						fp.write('\n'.join(_A))



				def    _lowercase	(self							: List[Any]      ,    **_A							: int)       ->      Dict:
					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname      ,    **_A)



				def    _lowercase	(self							: Dict      ,    **_A							: str)       ->      Optional[int]:
					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,    **_A)



				def    _lowercase	(self							: Optional[Any]      ,    _A							: Any)       ->      Tuple:
					return "lower newer", "lower newer"



				@cached_property
				def    _lowercase	(self							: Optional[int])       ->      List[str]:
					return BartTokenizer.from_pretrained('facebook/bart-large')



				@cached_property
				def    _lowercase	(self							: List[Any])       ->      List[Any]:
					return BartTokenizerFast.from_pretrained('facebook/bart-large')



				@require_torch
				def    _lowercase	(self							: int)       ->      int:
					__snake_case     :				List[Any] 						=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__snake_case     :				Dict 						=					[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case     :				List[str] 						=					tokenizer(_A      ,    max_length=len(_A)      ,    padding=_A      ,    return_tensors='pt')
						self.assertIsInstance(_A      ,    _A)

						self.assertEqual((2, 9)      ,    batch.input_ids.shape)
						self.assertEqual((2, 9)      ,    batch.attention_mask.shape)
						__snake_case     :				int 						=					batch.input_ids.tolist()[0]
						self.assertListEqual(_A      ,    _A)
						# Test that special tokens are reset



				@require_torch
				def    _lowercase	(self							: Union[str, Any])       ->      Optional[int]:
					__snake_case     :				Optional[int] 						=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case     :				List[str] 						=					tokenizer(_A      ,    padding=_A      ,    return_tensors='pt')
						# check if input_ids are returned and no labels
						self.assertIn('input_ids'      ,    _A)
						self.assertIn('attention_mask'      ,    _A)
						self.assertNotIn('labels'      ,    _A)
						self.assertNotIn('decoder_attention_mask'      ,    _A)



				@require_torch
				def    _lowercase	(self							: List[str])       ->      Dict:
					__snake_case     :				Dict 						=					[
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case     :				int 						=					tokenizer(text_target=_A      ,    max_length=32      ,    padding='max_length'      ,    return_tensors='pt')
						self.assertEqual(32      ,    targets['input_ids'].shape[1])



				@require_torch
				def    _lowercase	(self							: Any)       ->      Dict:
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case     :				Optional[Any] 						=					tokenizer(
						    ['I am a small frog' * 10_24, 'I am a small frog']      ,    padding=_A      ,    truncation=_A      ,    return_tensors='pt')
						self.assertIsInstance(_A      ,    _A)
						self.assertEqual(batch.input_ids.shape      ,    (2, 10_24))



				@require_torch
				def    _lowercase	(self							: Tuple)       ->      List[Any]:
					__snake_case     :				Tuple 						=					['A long paragraph for summarization.']
					__snake_case     :				Dict 						=					[
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case     :				Optional[Any] 						=					tokenizer(_A      ,    return_tensors='pt')
						__snake_case     :				Dict 						=					tokenizer(text_target=_A      ,    return_tensors='pt')
						__snake_case     :				Optional[int] 						=					inputs['input_ids']
						__snake_case     :				Optional[int] 						=					targets['input_ids']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



				def    _lowercase	(self							: Optional[Any])       ->      List[Any]:
					pass






				def    _lowercase	(self							: Optional[Any])       ->      int:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case     :				Optional[int] 						=					self.rust_tokenizer_class.from_pretrained(_A      ,    **_A)
							__snake_case     :				Optional[Any] 						=					self.tokenizer_class.from_pretrained(_A      ,    **_A)
							__snake_case     :				List[str] 						=					'A, <mask> AllenNLP sentence.'
							__snake_case     :				Dict 						=					tokenizer_r.encode_plus(_A      ,    add_special_tokens=_A      ,    return_token_type_ids=_A)
							__snake_case     :				List[Any] 						=					tokenizer_p.encode_plus(_A      ,    add_special_tokens=_A      ,    return_token_type_ids=_A)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])      ,    sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])      ,    sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])      ,    )

							__snake_case     :				List[str] 						=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							__snake_case     :				Tuple 						=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']      ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']      ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

							self.assertSequenceEqual(
							    _A      ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    _A      ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])






"""simple docstring"""







_a	:	Optional[Any]=					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     List[Any]:

	'''simple docstring'''





	return c.isdigit()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					[]

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(UpperCAmelCase_    ):
			stack.append(int(UpperCAmelCase_    )    )

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			__snake_case     :				Optional[Any] 						=					stack.pop()
			__snake_case     :				int 						=					stack.pop()
			stack.append(calc[c](UpperCAmelCase_     ,				UpperCAmelCase_    )    )

	return stack.pop()


# Driver code
if __name__ == "__main__":
	_a	:	Union[str, Any]=					"+ 9 * 2 6"
	print(evaluate(test_expression))

	_a	:	List[Any]=					"/ * 10 2 + 4 1 "
	print(evaluate(test_expression))





