




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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						"\n".join(SCREAMING_SNAKE_CASE_      )
    Path(SCREAMING_SNAKE_CASE_      ).open('''w'''      ).writelines(SCREAMING_SNAKE_CASE_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """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  _lowerCamelCase						(			_UpperCAmelCase   ):

      """simple docstring"""

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

          __UpperCamelCase  		=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
          __UpperCamelCase  		=						tmp_dir.joinpath('''train.source'''				).open().readlines()
          __UpperCamelCase  		=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
          pack_data_dir(lowercase__ ,       lowercase__ ,       128 ,       lowercase__				)
          __UpperCamelCase  		=						{x.name for x in tmp_dir.iterdir()}
          __UpperCamelCase  		=						{x.name for x in save_dir.iterdir()}
          __UpperCamelCase  		=						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       snake_case	(     self					: Any				):






          if not FAIRSEQ_AVAILABLE:
              return
          __UpperCamelCase  		=						self._get_dataset(max_len=64				)
          __UpperCamelCase  		=						64
          __UpperCamelCase  		=						ds.make_dynamic_sampler(lowercase__ ,       required_batch_size_multiple=lowercase__				)
          __UpperCamelCase  		=						[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
          __UpperCamelCase  		=						DataLoader(lowercase__ ,       batch_sampler=lowercase__ ,       collate_fn=ds.collate_fn ,       num_workers=2				)
          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						[]
          for batch in data_loader:
              __UpperCamelCase  		=						batch["input_ids"].shape
              __UpperCamelCase  		=						src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              __UpperCamelCase  		=						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       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self._get_dataset(max_len=512				)
          __UpperCamelCase  		=						2
          __UpperCamelCase  		=						ds.make_sortish_sampler(lowercase__ ,       shuffle=lowercase__				)

          __UpperCamelCase  		=						DataLoader(lowercase__ ,       batch_size=lowercase__ ,       collate_fn=ds.collate_fn ,       num_workers=2				)
          __UpperCamelCase  		=						DataLoader(lowercase__ ,       batch_size=lowercase__ ,       collate_fn=ds.collate_fn ,       num_workers=2 ,       sampler=lowercase__				)

          __UpperCamelCase  		=						tokenizer.pad_token_id

          def count_pad_tokens(snake_case					: List[Any] ,       snake_case					: Optional[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       snake_case	(     self					: Union[str, Any] ,       snake_case					: List[str]=1000 ,       snake_case					: int=128				):






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

          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(lowercase__				)
          __UpperCamelCase  		=						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       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						self._get_dataset()
          __UpperCamelCase  		=						set(DistributedSortishSampler(lowercase__ ,       256 ,       num_replicas=2 ,       rank=0 ,       add_extra_examples=lowercase__				)				)
          __UpperCamelCase  		=						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       snake_case	(     self					: Tuple ,       snake_case					: List[Any]				):
          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(lowercase__ ,       use_fast=lowercase__				)
          if tok_name == MBART_TINY:
              __UpperCamelCase  		=						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''' ,       )
              __UpperCamelCase  		=						train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
              __UpperCamelCase  		=						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 ,       )
              __UpperCamelCase  		=						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












import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 print("Googling.....")
 a_   		=     "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
 a_   		=     requests.get(url, headers={"UserAgent": UserAgent().random})
 # res.raise_for_status()
 with open("project1a.html", "wb") as out_file:  # only for knowing the class
  for data in res.iter_content(1_0000):
   out_file.write(data)
 a_   		=     BeautifulSoup(res.text, "html.parser")
 a_   		=     list(soup.select(".eZt8xd"))[:5]

 print(len(links))
 for link in links:
  if link.text == "Maps":
   webbrowser.open(link.get("href"))
  else:
   webbrowser.open(f"""https://google.com{link.get('href')}""")






