




'''simple docstring'''

import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


a    :							Dict										=    'bert-base-cased'
a    :							str										=    'google/pegasus-xsum'
a    :							Tuple										=    [' Sam ate lunch today.', 'Sams lunch ingredients.']
a    :							int										=    ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
a    :							List[Any]										=    'patrickvonplaten/t5-tiny-random'
a    :							List[Any]										=    'sshleifer/bart-tiny-random'
a    :							Optional[Any]										=    'sshleifer/tiny-mbart'
a    :							Dict										=    'sshleifer/tiny-marian-en-de'


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							'''\n'''.join(__UpperCAmelCase      )
					Path(__UpperCAmelCase      ).open('''w'''      ).writelines(__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(__UpperCAmelCase, F"{split}.source"      ), __UpperCAmelCase      )
										_dump_articles(os.path.join(__UpperCAmelCase, F"{split}.target"      ), __UpperCAmelCase      )
					return tmp_dir






class 		a							(  _lowerCamelCase	):


				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]			,							)
				@slow
				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   int   ):
									snake_case_		     =							AutoTokenizer.from_pretrained(lowercase_   )
									snake_case_		     =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
									snake_case_		     =							max(len(tokenizer.encode(lowercase_   )   ) for a in ARTICLES   )
									snake_case_		     =							max(len(tokenizer.encode(lowercase_   )   ) for a in SUMMARIES   )
									snake_case_		     =							4
									snake_case_		     =							8
									assert max_len_target > max_src_len  # Will be truncated
									assert max_len_source > max_src_len  # Will be truncated
									snake_case_     ,snake_case_		     =							'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
									snake_case_		     =							SeqaSeqDataset(
									    lowercase_			,							data_dir=lowercase_			,							type_path='''train'''			,							max_source_length=lowercase_			,							max_target_length=lowercase_			,							src_lang=lowercase_			,							tgt_lang=lowercase_			,							)
									snake_case_		     =							DataLoader(lowercase_			,							batch_size=2			,							collate_fn=train_dataset.collate_fn   )
									for batch in dataloader:
														assert isinstance(lowercase_			,							lowercase_   )
														assert batch["attention_mask"].shape == batch["input_ids"].shape
														# show that articles were trimmed.
														assert batch["input_ids"].shape[1] == max_src_len
														# show that targets are the same len
														assert batch["labels"].shape[1] == max_tgt_len
														if tok_name != MBART_TINY:
																			continue
														# check language codes in correct place
														snake_case_		     =							shift_tokens_right(batch['''labels''']			,							tokenizer.pad_token_id   )
														assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
														assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
														assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
														assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

														break  # No need to test every batch


				@parameterized.expand([BART_TINY, BERT_BASE_CASED]   )
				def     A_					(						self       :   Any			,							lowercase_       :   Union[str, Any]   ):
									snake_case_		     =							AutoTokenizer.from_pretrained(lowercase_   )
									snake_case_		     =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
									snake_case_		     =							max(len(tokenizer.encode(lowercase_   )   ) for a in ARTICLES   )
									snake_case_		     =							max(len(tokenizer.encode(lowercase_   )   ) for a in SUMMARIES   )
									snake_case_		     =							4
									snake_case_		     =							LegacySeqaSeqDataset(
									    lowercase_			,							data_dir=lowercase_			,							type_path='''train'''			,							max_source_length=20			,							max_target_length=lowercase_			,							)
									snake_case_		     =							DataLoader(lowercase_			,							batch_size=2			,							collate_fn=train_dataset.collate_fn   )
									for batch in dataloader:
														assert batch["attention_mask"].shape == batch["input_ids"].shape
														# show that articles were trimmed.
														assert batch["input_ids"].shape[1] == max_len_source
														assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
														# show that targets were truncated
														assert batch["labels"].shape[1] == trunc_target  # Truncated
														assert max_len_target > trunc_target  # Truncated
														break  # No need to test every batch


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''   )

									snake_case_		     =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
									snake_case_		     =							tmp_dir.joinpath('''train.source'''   ).open().readlines()
									snake_case_		     =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
									pack_data_dir(lowercase_			,							lowercase_			,							128			,							lowercase_   )
									snake_case_		     =							{x.name for x in tmp_dir.iterdir()}
									snake_case_		     =							{x.name for x in save_dir.iterdir()}
									snake_case_		     =							save_dir.joinpath('''train.source'''   ).open().readlines()
									# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
									# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
									assert len(lowercase_   ) < len(lowercase_   )
									assert len(lowercase_   ) == 1
									assert len(packed_examples[0]   ) == sum(len(lowercase_   ) for x in orig_examples   )
									assert orig_paths == new_paths


				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,							reason='''This test requires fairseq'''   )
				def     A_					(						self       :   int   ):
									if not FAIRSEQ_AVAILABLE:
														return
									snake_case_     ,snake_case_     ,snake_case_		     =							self._get_dataset(max_len=64   )
									snake_case_		     =							64
									snake_case_		     =							ds.make_dynamic_sampler(lowercase_			,							required_batch_size_multiple=lowercase_   )
									snake_case_		     =							[len(lowercase_   ) for x in batch_sampler]
									assert len(set(lowercase_   )   ) > 1  # it's not dynamic batch size if every batch is the same length
									assert sum(lowercase_   ) == len(lowercase_   )  # no dropped or added examples
									snake_case_		     =							DataLoader(lowercase_			,							batch_sampler=lowercase_			,							collate_fn=ds.collate_fn			,							num_workers=2   )
									snake_case_		     =							[]
									snake_case_		     =							[]
									for batch in data_loader:
														snake_case_		     =							batch['''input_ids'''].shape
														snake_case_		     =							src_shape[0]
														assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
														snake_case_		     =							np.product(batch['''input_ids'''].shape   )
														num_src_per_batch.append(lowercase_   )
														if num_src_tokens > (max_tokens * 1.1):
																			failures.append(lowercase_   )
									assert num_src_per_batch[0] == max(lowercase_   )
									if failures:
														raise AssertionError(F"too many tokens in {len(lowercase_   )} batches"   )


				def     A_					(						self       :   Tuple   ):
									snake_case_     ,snake_case_     ,snake_case_		     =							self._get_dataset(max_len=512   )
									snake_case_		     =							2
									snake_case_		     =							ds.make_sortish_sampler(lowercase_			,							shuffle=lowercase_   )

									snake_case_		     =							DataLoader(lowercase_			,							batch_size=lowercase_			,							collate_fn=ds.collate_fn			,							num_workers=2   )
									snake_case_		     =							DataLoader(lowercase_			,							batch_size=lowercase_			,							collate_fn=ds.collate_fn			,							num_workers=2			,							sampler=lowercase_   )

									snake_case_		     =							tokenizer.pad_token_id

									def count_pad_tokens(lowercase_       :   Dict			,							lowercase_       :   List[Any]="input_ids"   ):
														return [batch[k].eq(lowercase_   ).sum().item() for batch in data_loader]

									assert sum(count_pad_tokens(lowercase_			,							k='''labels'''   )   ) < sum(count_pad_tokens(lowercase_			,							k='''labels'''   )   )
									assert sum(count_pad_tokens(lowercase_   )   ) < sum(count_pad_tokens(lowercase_   )   )
									assert len(lowercase_   ) == len(lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Any=1000			,							lowercase_       :   Optional[Any]=128   ):
									if os.getenv('''USE_REAL_DATA'''			,							lowercase_   ):
														snake_case_		     =							'''examples/seq2seq/wmt_en_ro'''
														snake_case_		     =							max_len * 2 * 64
														if not Path(lowercase_   ).joinpath('''train.len'''   ).exists():
																			save_len_file(lowercase_			,							lowercase_   )
									else:
														snake_case_		     =							'''examples/seq2seq/test_data/wmt_en_ro'''
														snake_case_		     =							max_len * 4
														save_len_file(lowercase_			,							lowercase_   )

									snake_case_		     =							AutoTokenizer.from_pretrained(lowercase_   )
									snake_case_		     =							SeqaSeqDataset(
									    lowercase_			,							data_dir=lowercase_			,							type_path='''train'''			,							max_source_length=lowercase_			,							max_target_length=lowercase_			,							n_obs=lowercase_			,							)
									return ds, max_tokens, tokenizer


				def     A_					(						self       :   Tuple   ):
									snake_case_     ,snake_case_     ,snake_case_		     =							self._get_dataset()
									snake_case_		     =							set(DistributedSortishSampler(lowercase_			,							256			,							num_replicas=2			,							rank=0			,							add_extra_examples=lowercase_   )   )
									snake_case_		     =							set(DistributedSortishSampler(lowercase_			,							256			,							num_replicas=2			,							rank=1			,							add_extra_examples=lowercase_   )   )
									assert idsa.intersection(lowercase_   ) == set()


				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]			,							)
				def     A_					(						self       :   int			,							lowercase_       :   int   ):
									snake_case_		     =							AutoTokenizer.from_pretrained(lowercase_			,							use_fast=lowercase_   )
									if tok_name == MBART_TINY:
														snake_case_		     =							SeqaSeqDataset(
														    lowercase_			,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )			,							type_path='''train'''			,							max_source_length=4			,							max_target_length=8			,							src_lang='''EN'''			,							tgt_lang='''FR'''			,							)
														snake_case_		     =							train_dataset.dataset_kwargs
														assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
														snake_case_		     =							SeqaSeqDataset(
														    lowercase_			,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )			,							type_path='''train'''			,							max_source_length=4			,							max_target_length=8			,							)
														snake_case_		     =							train_dataset.dataset_kwargs
														assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
														assert len(lowercase_   ) == 1 if tok_name == BART_TINY else len(lowercase_   ) == 0







'''simple docstring'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					snake_case_		     =							{
					    '''^''': op.pow,
					    '''*''': op.mul,
					    '''/''': div,
					    '''+''': op.add,
					    '''-''': op.sub,
					}  # operators & their respective operation

					# print table header
					print('''Symbol'''.center(8      ), '''Action'''.center(12      ), '''Stack''', sep=''' | '''      )
					print('''-''' * (30 + len(__UpperCAmelCase      ))      )

					for x in post_fix:
										if x.isdigit():  # if x in digit
															stack.append(__UpperCAmelCase      )  # append x to stack
															# output in tabular format
															print(x.rjust(8      ), ('''push(''' + x + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )
										else:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + a + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # evaluate the 2 values popped from stack & push result to stack
															# output in tabular format
															print(
															    x.rjust(8      ), ('''push(''' + a + x + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


if __name__ == "__main__":
							a    :							Any										=    input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
							print('\n\tResult = ', solve(Postfix))

