



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






def 			snake_case_    ( A_   :						Path,							A_   :						list		):


       '''simple docstring'''





       _lowerCamelCase		:							List[str]            =					'''\n'''.join(SCREAMING_SNAKE_CASE_		)
       Path(SCREAMING_SNAKE_CASE_		).open('''w'''		).writelines(SCREAMING_SNAKE_CASE_		)






def 			snake_case_    ( A_   :						Tuple		):


       '''simple docstring'''





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







class 			__snake_case			(      _a):




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]		,   )
  @slow
  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					AutoTokenizer.from_pretrained(__UpperCamelCase						)
         _lowerCamelCase		:							Tuple            =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
         _lowerCamelCase		:							Dict            =					max(len(tokenizer.encode(__UpperCamelCase						)						) for a in ARTICLES						)
         _lowerCamelCase		:							List[str]            =					max(len(tokenizer.encode(__UpperCamelCase						)						) for a in SUMMARIES						)
         _lowerCamelCase		:							List[str]            =					4
         _lowerCamelCase		:							Optional[Any]            =					8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         _lowerCamelCase     ,       _lowerCamelCase		:							Any            =					'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
         _lowerCamelCase		:							Union[str, Any]            =					SeqaSeqDataset(
             __UpperCamelCase		,   data_dir=__UpperCamelCase		,   type_path='''train'''		,   max_source_length=__UpperCamelCase		,   max_target_length=__UpperCamelCase		,   src_lang=__UpperCamelCase		,   tgt_lang=__UpperCamelCase		,   )
         _lowerCamelCase		:							List[str]            =					DataLoader(__UpperCamelCase		,   batch_size=2		,   collate_fn=train_dataset.collate_fn						)
         for batch in dataloader:
                assert isinstance(__UpperCamelCase		,   __UpperCamelCase						)
                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
                _lowerCamelCase		:							str            =					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 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   __lowerCAmelCase	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					AutoTokenizer.from_pretrained(__UpperCamelCase						)
         _lowerCamelCase		:							Tuple            =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
         _lowerCamelCase		:							Optional[Any]            =					max(len(tokenizer.encode(__UpperCamelCase						)						) for a in ARTICLES						)
         _lowerCamelCase		:							Optional[int]            =					max(len(tokenizer.encode(__UpperCamelCase						)						) for a in SUMMARIES						)
         _lowerCamelCase		:							Any            =					4
         _lowerCamelCase		:							Dict            =					LegacySeqaSeqDataset(
             __UpperCamelCase		,   data_dir=__UpperCamelCase		,   type_path='''train'''		,   max_source_length=2_0		,   max_target_length=__UpperCamelCase		,   )
         _lowerCamelCase		:							List[Any]            =					DataLoader(__UpperCamelCase		,   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 2_0 >= 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 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''						)

         _lowerCamelCase		:							int            =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
         _lowerCamelCase		:							Tuple            =					tmp_dir.joinpath('''train.source'''						).open().readlines()
         _lowerCamelCase		:							List[Any]            =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
         pack_data_dir(__UpperCamelCase		,   __UpperCamelCase		,   1_2_8		,   __UpperCamelCase						)
         _lowerCamelCase		:							int            =					{x.name for x in tmp_dir.iterdir()}
         _lowerCamelCase		:							Dict            =					{x.name for x in save_dir.iterdir()}
         _lowerCamelCase		:							Any            =					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(__UpperCamelCase						) < len(__UpperCamelCase						)
         assert len(__UpperCamelCase						) == 1
         assert len(packed_examples[0]						) == sum(len(__UpperCamelCase						) for x in orig_examples						)
         assert orig_paths == new_paths




  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE		,   reason='''This test requires fairseq'''						)
  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         if not FAIRSEQ_AVAILABLE:
                return
         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							str            =					self._get_dataset(max_len=6_4						)
         _lowerCamelCase		:							Union[str, Any]            =					6_4
         _lowerCamelCase		:							int            =					ds.make_dynamic_sampler(__UpperCamelCase		,   required_batch_size_multiple=__UpperCamelCase						)
         _lowerCamelCase		:							Dict            =					[len(__UpperCamelCase						) for x in batch_sampler]
         assert len(set(__UpperCamelCase						)						) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(__UpperCamelCase						) == len(__UpperCamelCase						)  # no dropped or added examples
         _lowerCamelCase		:							Optional[int]            =					DataLoader(__UpperCamelCase		,   batch_sampler=__UpperCamelCase		,   collate_fn=ds.collate_fn		,   num_workers=2						)
         _lowerCamelCase		:							List[str]            =					[]
         _lowerCamelCase		:							Dict            =					[]
         for batch in data_loader:
                _lowerCamelCase		:							Union[str, Any]            =					batch['''input_ids'''].shape
                _lowerCamelCase		:							Optional[Any]            =					src_shape[0]
                assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                _lowerCamelCase		:							Union[str, Any]            =					np.product(batch['''input_ids'''].shape						)
                num_src_per_batch.append(__UpperCamelCase						)
                if num_src_tokens > (max_tokens * 1.1):
                       failures.append(__UpperCamelCase						)
         assert num_src_per_batch[0] == max(__UpperCamelCase						)
         if failures:
                raise AssertionError(f'''too many tokens in {len(__UpperCamelCase						)} batches'''						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					self._get_dataset(max_len=5_1_2						)
         _lowerCamelCase		:							Optional[Any]            =					2
         _lowerCamelCase		:							List[Any]            =					ds.make_sortish_sampler(__UpperCamelCase		,   shuffle=__UpperCamelCase						)

         _lowerCamelCase		:							Any            =					DataLoader(__UpperCamelCase		,   batch_size=__UpperCamelCase		,   collate_fn=ds.collate_fn		,   num_workers=2						)
         _lowerCamelCase		:							List[Any]            =					DataLoader(__UpperCamelCase		,   batch_size=__UpperCamelCase		,   collate_fn=ds.collate_fn		,   num_workers=2		,   sampler=__UpperCamelCase						)

         _lowerCamelCase		:							Optional[int]            =					tokenizer.pad_token_id

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

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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		Tuple=1_0_0_0		,   __lowerCAmelCase	:		Any=1_2_8						):






         """simple docstring"""






         if os.getenv('''USE_REAL_DATA'''		,   __UpperCamelCase						):
                _lowerCamelCase		:							List[Any]            =					'''examples/seq2seq/wmt_en_ro'''
                _lowerCamelCase		:							Tuple            =					max_len * 2 * 6_4
                if not Path(__UpperCamelCase						).joinpath('''train.len'''						).exists():
                       save_len_file(__UpperCamelCase		,   __UpperCamelCase						)
         else:
                _lowerCamelCase		:							Tuple            =					'''examples/seq2seq/test_data/wmt_en_ro'''
                _lowerCamelCase		:							Union[str, Any]            =					max_len * 4
                save_len_file(__UpperCamelCase		,   __UpperCamelCase						)

         _lowerCamelCase		:							int            =					AutoTokenizer.from_pretrained(__UpperCamelCase						)
         _lowerCamelCase		:							List[Any]            =					SeqaSeqDataset(
             __UpperCamelCase		,   data_dir=__UpperCamelCase		,   type_path='''train'''		,   max_source_length=__UpperCamelCase		,   max_target_length=__UpperCamelCase		,   n_obs=__UpperCamelCase		,   )
         return ds, max_tokens, tokenizer




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							str            =					self._get_dataset()
         _lowerCamelCase		:							List[Any]            =					set(DistributedSortishSampler(__UpperCamelCase		,   2_5_6		,   num_replicas=2		,   rank=0		,   add_extra_examples=__UpperCamelCase						)						)
         _lowerCamelCase		:							Dict            =					set(DistributedSortishSampler(__UpperCamelCase		,   2_5_6		,   num_replicas=2		,   rank=1		,   add_extra_examples=__UpperCamelCase						)						)
         assert idsa.intersection(__UpperCamelCase						) == set()




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]		,   )
  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					AutoTokenizer.from_pretrained(__UpperCamelCase		,   use_fast=__UpperCamelCase						)
         if tok_name == MBART_TINY:
                _lowerCamelCase		:							str            =					SeqaSeqDataset(
                    __UpperCamelCase		,   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'''		,   )
                _lowerCamelCase		:							Optional[Any]            =					train_dataset.dataset_kwargs
                assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
                _lowerCamelCase		:							int            =					SeqaSeqDataset(
                    __UpperCamelCase		,   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		,   )
                _lowerCamelCase		:							Optional[Any]            =					train_dataset.dataset_kwargs
                assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
                assert len(__UpperCamelCase						) == 1 if tok_name == BART_TINY else len(__UpperCamelCase						) == 0




import os

# Precomputes a list of the 100 first triangular numbers
A		: List[Any]								=     [int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 	UpperCamelCase__  (     )  ->     str:
						_lowercase					       =					os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_       )       )
						_lowercase					       =					os.path.join(SCREAMING_SNAKE_CASE_		,   """words.txt"""       )

						_lowercase					       =					""""""
						with open(SCREAMING_SNAKE_CASE_       ) as f:
												_lowercase					       =					f.readline()

						_lowercase					       =					[word.strip("""\""""       ) for word in words.strip("""\r\n"""       ).split(""","""       )]
						_lowercase					       =					[
						    word
						    for word in [sum(ord(SCREAMING_SNAKE_CASE_       ) - 64 for x in word       ) for word in words]
						    if word in TRIANGULAR_NUMBERS
						]
						return len(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		print(solution())