

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      a      (			A__	:   Path      ,   A__	:   list     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       ='\n'.join(A__     )
      Path(A__     ).open('w'     ).writelines(A__     )
def      a      (			A__	:   Dict     )    ->      Dict:





      """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     (					SCREAMING_SNAKE_CASE							):




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]							,   )
  @slow
  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =AutoTokenizer.from_pretrained(__UpperCAmelCase					)
        _lowercase	       =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
        _lowercase	       =max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in ARTICLES					)
        _lowercase	       =max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in SUMMARIES					)
        _lowercase	       =4
        _lowercase	       =8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        _lowercase       ,       _lowercase	       ='ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
        _lowercase	       =SeqaSeqDataset(
            __UpperCAmelCase							,   data_dir=__UpperCAmelCase							,   type_path='train'							,   max_source_length=__UpperCAmelCase							,   max_target_length=__UpperCAmelCase							,   src_lang=__UpperCAmelCase							,   tgt_lang=__UpperCAmelCase							,   )
        _lowercase	       =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
              _lowercase	       =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							,   lowerCAmelCase					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =AutoTokenizer.from_pretrained(__UpperCAmelCase					)
        _lowercase	       =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
        _lowercase	       =max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in ARTICLES					)
        _lowercase	       =max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in SUMMARIES					)
        _lowercase	       =4
        _lowercase	       =LegacySeqaSeqDataset(
            __UpperCAmelCase							,   data_dir=__UpperCAmelCase							,   type_path='train'							,   max_source_length=20							,   max_target_length=__UpperCAmelCase							,   )
        _lowercase	       =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   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

        _lowercase	       =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
        _lowercase	       =tmp_dir.joinpath('train.source'					).open().readlines()
        _lowercase	       =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
        pack_data_dir(__UpperCAmelCase							,   __UpperCAmelCase							,   128							,   __UpperCAmelCase					)
        _lowercase	       ={x.name for x in tmp_dir.iterdir()}
        _lowercase	       ={x.name for x in save_dir.iterdir()}
        _lowercase	       =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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        if not FAIRSEQ_AVAILABLE:
              return
        _lowercase       ,       _lowercase       ,       _lowercase	       =self._get_dataset(max_len=64					)
        _lowercase	       =64
        _lowercase	       =ds.make_dynamic_sampler(__UpperCAmelCase							,   required_batch_size_multiple=__UpperCAmelCase					)
        _lowercase	       =[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
        _lowercase	       =DataLoader(__UpperCAmelCase							,   batch_sampler=__UpperCAmelCase							,   collate_fn=ds.collate_fn							,   num_workers=2					)
        _lowercase	       =[]
        _lowercase	       =[]
        for batch in data_loader:
              _lowercase	       =batch['input_ids'].shape
              _lowercase	       =src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              _lowercase	       =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   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase       ,       _lowercase       ,       _lowercase	       =self._get_dataset(max_len=512					)
        _lowercase	       =2
        _lowercase	       =ds.make_sortish_sampler(__UpperCAmelCase							,   shuffle=__UpperCAmelCase					)

        _lowercase	       =DataLoader(__UpperCAmelCase							,   batch_size=__UpperCAmelCase							,   collate_fn=ds.collate_fn							,   num_workers=2					)
        _lowercase	       =DataLoader(__UpperCAmelCase							,   batch_size=__UpperCAmelCase							,   collate_fn=ds.collate_fn							,   num_workers=2							,   sampler=__UpperCAmelCase					)

        _lowercase	       =tokenizer.pad_token_id

        def count_pad_tokens(lowerCAmelCase							,   lowerCAmelCase="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   A__     (				self							,   lowerCAmelCase=1_000							,   lowerCAmelCase=128					)  -> Any:
        '''simple docstring'''






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

        _lowercase	       =AutoTokenizer.from_pretrained(__UpperCAmelCase					)
        _lowercase	       =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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase       ,       _lowercase       ,       _lowercase	       =self._get_dataset()
        _lowercase	       =set(DistributedSortishSampler(__UpperCAmelCase							,   256							,   num_replicas=2							,   rank=0							,   add_extra_examples=__UpperCAmelCase					)					)
        _lowercase	       =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   A__     (				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =AutoTokenizer.from_pretrained(__UpperCAmelCase							,   use_fast=__UpperCAmelCase					)
        if tok_name == MBART_TINY:
              _lowercase	       =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'							,   )
              _lowercase	       =train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
              _lowercase	       =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							,   )
              _lowercase	       =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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_groupvit": [
        "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "GroupViTConfig",
        "GroupViTOnnxConfig",
        "GroupViTTextConfig",
        "GroupViTVisionConfig",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GroupViTModel",
         "GroupViTPreTrainedModel",
         "GroupViTTextModel",
         "GroupViTVisionModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFGroupViTModel",
         "TFGroupViTPreTrainedModel",
         "TFGroupViTTextModel",
         "TFGroupViTVisionModel",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


