
import requests
from bsa import BeautifulSoup



def 						lowercase_      (       A__			,		A__		)							->							str:
    """simple docstring"""

    snake_case								= BeautifulSoup(requests.get(A__			,		params=A__		).content			,		"html.parser"		)
    snake_case								= soup.find("div"			,		attrs={"class": "gs_ri"}		)
    snake_case								= div.find("div"			,		attrs={"class": "gs_fl"}		).find_all("a"		)
    return anchors[2].get_text()


if __name__ == "__main__":
   _A					 =			{
       "title": (
           "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
           "capacitance, volumetric capacitance, and energy density"
       ),
       "journal": "Chem. Mater.",
       "volume": 30,
       "pages": "3979-3990",
       "year": 20_18,
       "hl": "en",
   }
   print(get_citation("https://scholar.google.com/scholar_lookup", params=params))



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



def 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

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







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) 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(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= 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
             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 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= 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 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 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         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(_A    ,   _A    ,   1_2_8    ,   _A       )
         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(_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				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [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
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   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(_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				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=_A       )

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="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				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= 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				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   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				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= 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"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= 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    ,   )
             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(_A       ) == 1 if tok_name == BART_TINY else len(_A       ) == 0

