



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




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						'''\n'''.join(__A)
     Path(__A).open('''w''').writelines(__A)




def lowerCAmelCase						(__A):



     """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 __A    (						A		):



 '''simple docstring'''







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]				,     )
 @slow
 def       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _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						,					_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       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _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       a__						(self )					->  str:


      """simple docstring"""



      _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				,     128				,     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       a__						(self )					->  int:


      """simple docstring"""



      if not FAIRSEQ_AVAILABLE:
           return
      _a						,					_a						,					_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       a__						(self )					->  Dict:


      """simple docstring"""



      _a						,					_a						,					_a											=						self._get_dataset(max_len=512 )
      _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				,     A="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       a__						(self				,     A=1_000				,     A=128 )					->  int:


      """simple docstring"""



      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       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a						,					_a						,					_a											=						self._get_dataset()
      _a											=						set(DistributedSortishSampler(A				,     256				,     num_replicas=2				,     rank=0				,     add_extra_examples=A ) )
      _a											=						set(DistributedSortishSampler(A				,     256				,     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       a__						(self				,     A )					->  Tuple:


      """simple docstring"""



      _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








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 'pegasus'
 __lowerCamelCase					: Tuple    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self				,     A=50_265				,     A=1_024				,     A=12				,     A=4_096				,     A=16				,     A=12				,     A=4_096				,     A=16				,     A=0.0				,     A=0.0				,     A=True				,     A=True				,     A="gelu"				,     A=1_024				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=0				,     A=False				,     A=0				,     A=1				,     A=1				,     **A				,     )					->  str:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						max_position_embeddings
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						use_cache
      _a											=						encoder_layers
      _a											=						scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=A				,     eos_token_id=A				,     is_encoder_decoder=A				,     decoder_start_token_id=A				,     forced_eos_token_id=A				,     **A				,     )



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model



