

"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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
@require_tokenizers
class      _UpperCAmelCase   (							_lowerCAmelCase		,    unittest.TestCase       ):
	a__		: int					=					LEDTokenizer
	a__		: Optional[Any]					=					LEDTokenizerFast
	a__		: List[Any]					=					True



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

		__UpperCAmelCase      		= os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES['''vocab_file''']						)
		__UpperCAmelCase      		= 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(_lowercase						) + '''\n'''						)
		with open(self.merges_file			,    '''w'''			,    encoding='''utf-8'''						) as fp:
			fp.write('''\n'''.join(_lowercase						)						)



	def        a  (  self     :       str			,    **_lowercase     :       Any						):
		kwargs.update(self.special_tokens_map						)
		return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Optional[int]			,    **_lowercase     :       int						):
		kwargs.update(self.special_tokens_map						)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Union[str, Any]			,    _lowercase     :       List[Any]						):
		return "lower newer", "lower newer"



	@cached_property
	def        a  (  self     :       int						):
		return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''						)



	@cached_property
	def        a  (  self     :       Union[str, Any]						):
		return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''						)



	@require_torch
	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
		__UpperCAmelCase      		= [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]:
			__UpperCAmelCase      		= tokenizer(_lowercase			,    max_length=len(_lowercase						)			,    padding=_lowercase			,    return_tensors='''pt'''						)
			self.assertIsInstance(_lowercase			,    _lowercase						)

			self.assertEqual((2, 9)			,    batch.input_ids.shape						)
			self.assertEqual((2, 9)			,    batch.attention_mask.shape						)
			__UpperCAmelCase      		= batch.input_ids.tolist()[0]
			self.assertListEqual(_lowercase			,    _lowercase						)



	@require_torch
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			__UpperCAmelCase      		= tokenizer(_lowercase			,    padding=_lowercase			,    return_tensors='''pt'''						)
			self.assertIn('''input_ids'''			,    _lowercase						)
			self.assertIn('''attention_mask'''			,    _lowercase						)
			self.assertNotIn('''labels'''			,    _lowercase						)
			self.assertNotIn('''decoder_attention_mask'''			,    _lowercase						)



	@require_torch
	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= [
		    '''Summary of the text.''',
		    '''Another summary.''',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			__UpperCAmelCase      		= tokenizer(text_target=_lowercase			,    max_length=32			,    padding='''max_length'''			,    return_tensors='''pt'''						)
			self.assertEqual(32			,    targets['''input_ids'''].shape[1]						)



	@require_torch
	def        a  (  self     :       Any						):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			__UpperCAmelCase      		= tokenizer(
			    ['''I am a small frog''' * 10_24, '''I am a small frog''']			,    padding=_lowercase			,    truncation=_lowercase			,    return_tensors='''pt'''						)
			self.assertIsInstance(_lowercase			,    _lowercase						)
			self.assertEqual(batch.input_ids.shape			,    (2, 51_22)						)



	@require_torch
	def        a  (  self     :       str						):
		__UpperCAmelCase      		= ['''A long paragraph for summarization.''']
		__UpperCAmelCase      		= [
		    '''Summary of the text.''',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			__UpperCAmelCase      		= tokenizer(_lowercase			,    return_tensors='''pt'''						)
			__UpperCAmelCase      		= tokenizer(text_target=_lowercase			,    return_tensors='''pt'''						)
			__UpperCAmelCase      		= inputs['''input_ids''']
			__UpperCAmelCase      		= 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()						)



	@require_torch
	def        a  (  self     :       List[str]						):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			__UpperCAmelCase      		= ['''Summary of the text.''', '''Another summary.''']
			__UpperCAmelCase      		= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

			__UpperCAmelCase      		= tokenizer(_lowercase			,    padding=_lowercase						)
			__UpperCAmelCase      		= [[0] * len(_lowercase						) for x in encoded_output['''input_ids''']]
			__UpperCAmelCase      		= tokenizer.pad(_lowercase						)
			self.assertSequenceEqual(outputs['''global_attention_mask''']			,    _lowercase						)



	def        a  (  self     :       Tuple						):
		pass



	def        a  (  self     :       Dict						):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
				__UpperCAmelCase      		= self.rust_tokenizer_class.from_pretrained(_lowercase			,    **_lowercase						)
				__UpperCAmelCase      		= self.tokenizer_class.from_pretrained(_lowercase			,    **_lowercase						)
				__UpperCAmelCase      		= '''A, <mask> AllenNLP sentence.'''
				__UpperCAmelCase      		= tokenizer_r.encode_plus(_lowercase			,    add_special_tokens=_lowercase			,    return_token_type_ids=_lowercase						)
				__UpperCAmelCase      		= tokenizer_p.encode_plus(_lowercase			,    add_special_tokens=_lowercase			,    return_token_type_ids=_lowercase						)
				self.assertEqual(sum(tokens_r['''token_type_ids''']						)			,    sum(tokens_p['''token_type_ids''']						)						)
				self.assertEqual(
				    sum(tokens_r['''attention_mask''']						) / len(tokens_r['''attention_mask''']						)			,    sum(tokens_p['''attention_mask''']						) / len(tokens_p['''attention_mask''']						)			,    )

				__UpperCAmelCase      		= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']						)
				__UpperCAmelCase      		= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']						)
				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(
				    _lowercase			,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']						)
				self.assertSequenceEqual(
				    _lowercase			,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']						)







'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

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

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


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



