





'''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


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






def 					_lowerCamelCase							(					lowercase		: Any       ,				lowercase		: str		)      ->      Dict:
				_a				      =					"\n".join(_lowerCAmelCase		)
				Path(_lowerCAmelCase		).open("w"		).writelines(_lowerCAmelCase		)






def 					_lowerCamelCase							(					lowercase		: Any		)      ->      Optional[int]:
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(_lowerCAmelCase       ,				F'{split}.source'		)       ,				_lowerCAmelCase		)
								_dump_articles(os.path.join(_lowerCAmelCase       ,				F'{split}.target'		)       ,				_lowerCAmelCase		)
				return tmp_dir



class 							__SCREAMING_SNAKE_CASE    (__snake_case ):






				"""simple docstring"""







				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]   ,							)
				@slow
				def     UpperCamelCase__  (    self	:   Tuple   ,							__a	:   Tuple   ):
								_a				      =					AutoTokenizer.from_pretrained(A_   )
								_a				      =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
								_a				      =					max(len(tokenizer.encode(A_   )   ) for a in ARTICLES   )
								_a				      =					max(len(tokenizer.encode(A_   )   ) for a in SUMMARIES   )
								_a				      =					4
								_a				      =					8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								_a				      =					"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
								_a				      =					SeqaSeqDataset(
								    A_   ,							data_dir=A_   ,							type_path="train"   ,							max_source_length=A_   ,							max_target_length=A_   ,							src_lang=A_   ,							tgt_lang=A_   ,							)
								_a				      =					DataLoader(A_   ,							batch_size=2   ,							collate_fn=train_dataset.collate_fn   )
								for batch in dataloader:
												assert isinstance(A_   ,							A_   )
												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
												_a				      =					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     UpperCamelCase__  (    self	:   Dict   ,							__a	:   List[str]   ):
								_a				      =					AutoTokenizer.from_pretrained(A_   )
								_a				      =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
								_a				      =					max(len(tokenizer.encode(A_   )   ) for a in ARTICLES   )
								_a				      =					max(len(tokenizer.encode(A_   )   ) for a in SUMMARIES   )
								_a				      =					4
								_a				      =					LegacySeqaSeqDataset(
								    A_   ,							data_dir=A_   ,							type_path="train"   ,							max_source_length=20   ,							max_target_length=A_   ,							)
								_a				      =					DataLoader(A_   ,							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     UpperCamelCase__  (    self	:   List[str]   ):
								_a				      =					AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"   )

								_a				      =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
								_a				      =					tmp_dir.joinpath("train.source"   ).open().readlines()
								_a				      =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
								pack_data_dir(A_   ,							A_   ,							1_28   ,							A_   )
								_a				      =					{x.name for x in tmp_dir.iterdir()}
								_a				      =					{x.name for x in save_dir.iterdir()}
								_a				      =					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(A_   ) < len(A_   )
								assert len(A_   ) == 1
								assert len(packed_examples[0]   ) == sum(len(A_   ) for x in orig_examples   )
								assert orig_paths == new_paths




				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE   ,							reason="This test requires fairseq"   )
				def     UpperCamelCase__  (    self	:   List[str]   ):




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




				def     UpperCamelCase__  (    self	:   List[str]   ):
								_a				      =					self._get_dataset(max_len=5_12   )
								_a				      =					2
								_a				      =					ds.make_sortish_sampler(A_   ,							shuffle=A_   )

								_a				      =					DataLoader(A_   ,							batch_size=A_   ,							collate_fn=ds.collate_fn   ,							num_workers=2   )
								_a				      =					DataLoader(A_   ,							batch_size=A_   ,							collate_fn=ds.collate_fn   ,							num_workers=2   ,							sampler=A_   )

								_a				      =					tokenizer.pad_token_id

								def count_pad_tokens(__a	:   Optional[int]   ,							__a	:   Dict="input_ids"   ):
												return [batch[k].eq(A_   ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(A_   ,							k="labels"   )   ) < sum(count_pad_tokens(A_   ,							k="labels"   )   )
								assert sum(count_pad_tokens(A_   )   ) < sum(count_pad_tokens(A_   )   )
								assert len(A_   ) == len(A_   )




				def     UpperCamelCase__  (    self	:   int   ,							__a	:   Optional[int]=10_00   ,							__a	:   Tuple=1_28   ):




								if os.getenv("USE_REAL_DATA"   ,							A_   ):
												_a				      =					"examples/seq2seq/wmt_en_ro"
												_a				      =					max_len * 2 * 64
												if not Path(A_   ).joinpath("train.len"   ).exists():
																save_len_file(A_   ,							A_   )
								else:
												_a				      =					"examples/seq2seq/test_data/wmt_en_ro"
												_a				      =					max_len * 4
												save_len_file(A_   ,							A_   )

								_a				      =					AutoTokenizer.from_pretrained(A_   )
								_a				      =					SeqaSeqDataset(
								    A_   ,							data_dir=A_   ,							type_path="train"   ,							max_source_length=A_   ,							max_target_length=A_   ,							n_obs=A_   ,							)
								return ds, max_tokens, tokenizer




				def     UpperCamelCase__  (    self	:   int   ):
								_a				      =					self._get_dataset()
								_a				      =					set(DistributedSortishSampler(A_   ,							2_56   ,							num_replicas=2   ,							rank=0   ,							add_extra_examples=A_   )   )
								_a				      =					set(DistributedSortishSampler(A_   ,							2_56   ,							num_replicas=2   ,							rank=1   ,							add_extra_examples=A_   )   )
								assert idsa.intersection(A_   ) == set()




				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]   ,							)
				def     UpperCamelCase__  (    self	:   Union[str, Any]   ,							__a	:   Union[str, Any]   ):
								_a				      =					AutoTokenizer.from_pretrained(A_   ,							use_fast=A_   )
								if tok_name == MBART_TINY:
												_a				      =					SeqaSeqDataset(
												    A_   ,							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"   ,							)
												_a				      =					train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												_a				      =					SeqaSeqDataset(
												    A_   ,							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   ,							)
												_a				      =					train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(A_   ) == 1 if tok_name == BART_TINY else len(A_   ) == 0







def 						A_							(	_lowerCAmelCase	)		->					bool:
 return str(_lowerCAmelCase	) == str(_lowerCAmelCase	)[::-1]



def 						A_							(	_lowerCAmelCase	)		->					int:
 return int(_lowerCAmelCase	) + int(str(_lowerCAmelCase	)[::-1]	)



def 						A_							(	_lowerCAmelCase = 1_0000	)		->					int:
 UpperCamelCase      :   List[Any]			      =  []
 for num in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Union[str, Any]			      =  0
  UpperCamelCase      :   List[str]			      =  num
  while iterations < 50:
   UpperCamelCase      :   int			      =  sum_reverse(_lowerCAmelCase	)
   iterations += 1
   if is_palindrome(_lowerCAmelCase	):
    break
  else:
   lychrel_nums.append(_lowerCAmelCase	)
 return len(_lowerCAmelCase	)


if __name__ == "__main__":
    print(f"""{solution() = }""")
