





'''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


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






def        _lowerCAmelCase			(  __snake_case  : Path ,							__snake_case  : list			)     ->     Dict:
     __A						: Tuple      =       '\n'.join(__snake_case			)
     Path(__snake_case			).open('w'			).writelines(__snake_case			)






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







class  SCREAMING_SNAKE_CASE    (a__							):


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

          '''simple docstring'''






          __A						: Union[str, Any]      =       AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						: int      =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          __A						: List[Any]      =       max(len(tokenizer.encode(_UpperCAmelCase)) for a in ARTICLES)
          __A						: Tuple      =       max(len(tokenizer.encode(_UpperCAmelCase)) for a in SUMMARIES)
          __A						: List[Any]      =       4
          __A						: Union[str, Any]      =       8
          assert max_len_target > max_src_len  # Will be truncated
          assert max_len_source > max_src_len  # Will be truncated
          __A ,__A						: Any      =       'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
          __A						: Tuple      =       SeqaSeqDataset(
              _UpperCAmelCase						,	data_dir=_UpperCAmelCase						,	type_path='train'						,	max_source_length=_UpperCAmelCase						,	max_target_length=_UpperCAmelCase						,	src_lang=_UpperCAmelCase						,	tgt_lang=_UpperCAmelCase						,	)
          __A						: List[Any]      =       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
               __A						: Any      =       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						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						: Any      =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          __A						: int      =       max(len(tokenizer.encode(_UpperCAmelCase)) for a in ARTICLES)
          __A						: List[Any]      =       max(len(tokenizer.encode(_UpperCAmelCase)) for a in SUMMARIES)
          __A						: Optional[Any]      =       4
          __A						: int      =       LegacySeqaSeqDataset(
              _UpperCAmelCase						,	data_dir=_UpperCAmelCase						,	type_path='train'						,	max_source_length=20						,	max_target_length=_UpperCAmelCase						,	)
          __A						: str      =       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 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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       AutoTokenizer.from_pretrained('facebook/mbart-large-cc25')

          __A						: Optional[int]      =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
          __A						: Union[str, Any]      =       tmp_dir.joinpath('train.source').open().readlines()
          __A						: str      =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
          pack_data_dir(_UpperCAmelCase						,	_UpperCAmelCase						,	128						,	_UpperCAmelCase)
          __A						: List[str]      =       {x.name for x in tmp_dir.iterdir()}
          __A						: List[Any]      =       {x.name for x in save_dir.iterdir()}
          __A						: Union[str, 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):

          '''simple docstring'''






          if not FAIRSEQ_AVAILABLE:
               return
          __A ,__A ,__A						: List[Any]      =       self._get_dataset(max_len=64)
          __A						: Any      =       64
          __A						: Union[str, Any]      =       ds.make_dynamic_sampler(_UpperCAmelCase						,	required_batch_size_multiple=_UpperCAmelCase)
          __A						: Optional[int]      =       [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
          __A						: Optional[int]      =       DataLoader(_UpperCAmelCase						,	batch_sampler=_UpperCAmelCase						,	collate_fn=ds.collate_fn						,	num_workers=2)
          __A						: List[str]      =       []
          __A						: Optional[int]      =       []
          for batch in data_loader:
               __A						: str      =       batch['input_ids'].shape
               __A						: List[Any]      =       src_shape[0]
               assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
               __A						: List[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):

          '''simple docstring'''






          __A ,__A ,__A						: Tuple      =       self._get_dataset(max_len=512)
          __A						: List[str]      =       2
          __A						: Dict      =       ds.make_sortish_sampler(_UpperCAmelCase						,	shuffle=_UpperCAmelCase)

          __A						: int      =       DataLoader(_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	collate_fn=ds.collate_fn						,	num_workers=2)
          __A						: Dict      =       DataLoader(_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	collate_fn=ds.collate_fn						,	num_workers=2						,	sampler=_UpperCAmelCase)

          __A						: List[str]      =       tokenizer.pad_token_id

          def count_pad_tokens(_UpperCAmelCase						,	_UpperCAmelCase="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						,	_UpperCAmelCase=1000						,	_UpperCAmelCase=128):

          '''simple docstring'''






          if os.getenv('USE_REAL_DATA'						,	_UpperCAmelCase):
               __A						: Any      =       'examples/seq2seq/wmt_en_ro'
               __A						: Tuple      =       max_len * 2 * 64
               if not Path(_UpperCAmelCase).joinpath('train.len').exists():
                    save_len_file(_UpperCAmelCase						,	_UpperCAmelCase)
          else:
               __A						: Optional[int]      =       'examples/seq2seq/test_data/wmt_en_ro'
               __A						: Optional[int]      =       max_len * 4
               save_len_file(_UpperCAmelCase						,	_UpperCAmelCase)

          __A						: Union[str, Any]      =       AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						: 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):

          '''simple docstring'''






          __A ,__A ,__A						: List[str]      =       self._get_dataset()
          __A						: Tuple      =       set(DistributedSortishSampler(_UpperCAmelCase						,	256						,	num_replicas=2						,	rank=0						,	add_extra_examples=_UpperCAmelCase))
          __A						: Any      =       set(DistributedSortishSampler(_UpperCAmelCase						,	256						,	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						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Tuple      =       AutoTokenizer.from_pretrained(_UpperCAmelCase						,	use_fast=_UpperCAmelCase)
          if tok_name == MBART_TINY:
               __A						: Optional[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						,	src_lang='EN'						,	tgt_lang='FR'						,	)
               __A						: Optional[int]      =       train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
               __A						: Any      =       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						,	)
               __A						: int      =       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






'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()