

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def   a   (    )					->   List[Any]:





      """simple docstring"""



      _lowercase			 ='https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
      _lowercase			 =Image.open(requests.get(A__   ,     stream=A__  ).raw  ).convert('RGB'  )

      return image
def   a   (    A__			:     str  )					->   Any:





      """simple docstring"""



      _lowercase			 =[]
      # fmt: off

      # vision encoder
      rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')  )
      rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')  )
      rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')  )
      rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')  )
      rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')  )
      rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')  )

      for i in range(config.vision_config.num_hidden_layers  ):
            rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')  )
            rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')  )

      # QFormer
      rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')  )
      rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')  )

      # fmt: on
      return rename_keys
def   a   (    A__			:     Dict   ,     A__			:     List[str]   ,     A__			:     str  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =dct.pop(A__  )
      _lowercase			 =val
def   a   (    A__			:     int   ,     A__			:     Union[str, Any]  )					->   List[str]:





      """simple docstring"""



      for i in range(config.vision_config.num_hidden_layers  ):
            # read in original q and v biases
            _lowercase			 =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''  )
            _lowercase			 =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''  )

            # next, set bias in the state dict
            _lowercase			 =torch.cat((q_bias, torch.zeros_like(A__   ,     requires_grad=A__  ), v_bias)  )
            _lowercase			 =qkv_bias
def   a   (    A__			:     str   ,     A__			:     Dict  )					->   Any:





      """simple docstring"""



      _lowercase			 =364 if 'coco' in model_name else 224
      _lowercase			 =BlipaVisionConfig(image_size=A__  ).to_dict()

      # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
      # seems like flan-T5 models don't have bos_token_id properly set?
      if "opt-2.7b" in model_name:
            _lowercase			 =OPTConfig.from_pretrained('facebook/opt-2.7b'   ,     eos_token_id=A__  ).to_dict()
      elif "opt-6.7b" in model_name:
            _lowercase			 =OPTConfig.from_pretrained('facebook/opt-6.7b'   ,     eos_token_id=A__  ).to_dict()
      elif "t5-xl" in model_name:
            _lowercase			 =TaConfig.from_pretrained('google/flan-t5-xl'   ,     dense_act_fn='gelu'   ,     bos_token_id=1  ).to_dict()
      elif "t5-xxl" in model_name:
            _lowercase			 =TaConfig.from_pretrained('google/flan-t5-xxl'   ,     dense_act_fn='gelu'   ,     bos_token_id=1  ).to_dict()

      _lowercase			 =BlipaConfig(vision_config=A__   ,     text_config=A__  )

      return config, image_size
@torch.no_grad()
def   a   (    A__			:     Optional[int]   ,     A__			:     str=None   ,     A__			:     Optional[Any]=False  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =(
          AutoTokenizer.from_pretrained('facebook/opt-2.7b'  )
          if 'opt' in model_name
          else AutoTokenizer.from_pretrained('google/flan-t5-xl'  )
      )
      _lowercase			 =tokenizer('\n'   ,     add_special_tokens=A__  ).input_ids[0]
      _lowercase , _lowercase			 =get_blipa_config(A__   ,     eos_token_id=A__  )

      _lowercase			 =BlipaForConditionalGeneration(A__  ).eval()

      _lowercase			 ={
          'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
          'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
          'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
          'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
          'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
          'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
          'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
      }

      _lowercase , _lowercase			 =model_name_to_original[model_name]

      # load original model
      print('Loading original model...'  )
      _lowercase			 ='cuda' if torch.cuda.is_available() else 'cpu'
      _lowercase , _lowercase , _lowercase			 =load_model_and_preprocess(
          name=A__   ,     model_type=A__   ,     is_eval=A__   ,     device=A__  )
      original_model.eval()
      print('Done!'  )

      # update state dict keys
      _lowercase			 =original_model.state_dict()
      _lowercase			 =create_rename_keys(A__  )
      for src, dest in rename_keys:
            rename_key(A__   ,     A__   ,     A__  )

      # some keys can be renamed efficiently
      for key, val in state_dict.copy().items():
            _lowercase			 =state_dict.pop(A__  )
            if key.startswith('Qformer.bert'  ):
                  _lowercase			 =key.replace('Qformer.bert'   ,     'qformer'  )
            if "attention.self" in key:
                  _lowercase			 =key.replace('self'   ,     'attention'  )
            if "opt_proj" in key:
                  _lowercase			 =key.replace('opt_proj'   ,     'language_projection'  )
            if "t5_proj" in key:
                  _lowercase			 =key.replace('t5_proj'   ,     'language_projection'  )
            if key.startswith('opt'  ):
                  _lowercase			 =key.replace('opt'   ,     'language'  )
            if key.startswith('t5'  ):
                  _lowercase			 =key.replace('t5'   ,     'language'  )
            _lowercase			 =val

      # read in qv biases
      read_in_q_v_bias(A__   ,     A__  )

      _lowercase , _lowercase			 =hf_model.load_state_dict(A__   ,     strict=A__  )
      assert len(A__  ) == 0
      assert unexpected_keys == ["qformer.embeddings.position_ids"]

      _lowercase			 =load_demo_image()
      _lowercase			 =vis_processors['eval'](A__  ).unsqueeze(0  ).to(A__  )
      _lowercase			 =tokenizer(['\n']   ,     return_tensors='pt'  ).input_ids.to(A__  )

      # create processor
      _lowercase			 =BlipImageProcessor(
          size={'height': image_size, 'width': image_size}   ,     image_mean=A__   ,     image_std=A__  )
      _lowercase			 =BlipaProcessor(image_processor=A__   ,     tokenizer=A__  )
      _lowercase			 =processor(images=A__   ,     return_tensors='pt'  ).pixel_values.to(A__  )

      # make sure processor creates exact same pixel values
      assert torch.allclose(A__   ,     A__  )

      original_model.to(A__  )
      hf_model.to(A__  )
      with torch.no_grad():
            if "opt" in model_name:
                  _lowercase			 =original_model({'image': original_pixel_values, 'text_input': ['']}  ).logits
                  _lowercase			 =hf_model(A__   ,     A__  ).logits
            else:
                  _lowercase			 =original_model(
                      {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}  ).logits
                  _lowercase			 =input_ids.masked_fill(input_ids == tokenizer.pad_token_id   ,     -100  )
                  _lowercase			 =hf_model(A__   ,     A__   ,     labels=A__  ).logits

      assert original_logits.shape == logits.shape
      print('First values of original logits:'   ,     original_logits[0, :3, :3]  )
      print('First values of HF logits:'   ,     logits[0, :3, :3]  )

      # assert values
      if model_name == "blip2-flan-t5-xl":
            _lowercase			 =torch.tensor(
                [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]   ,     device=A__  )
            assert torch.allclose(logits[0, :3, :3]   ,     A__   ,     atol=1e-4  )
      elif model_name == "blip2-flan-t5-xl-coco":
            _lowercase			 =torch.tensor(
                [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]   ,     device=A__  )
      else:
            # cast to same type
            _lowercase			 =logits.dtype
            assert torch.allclose(original_logits.to(A__  )   ,     A__   ,     atol=1e-2  )
      print('Looks ok!'  )

      print('Generating a caption...'  )
      _lowercase			 =''
      _lowercase			 =tokenizer(A__   ,     return_tensors='pt'  ).input_ids.to(A__  )

      _lowercase			 =original_model.generate({'image': original_pixel_values}  )
      _lowercase			 =hf_model.generate(
          A__   ,     A__   ,     do_sample=A__   ,     num_beams=5   ,     max_length=30   ,     min_length=1   ,     top_p=0.9   ,     repetition_penalty=1.0   ,     length_penalty=1.0   ,     temperature=1   ,     )
      print('Original generation:'   ,     A__  )
      _lowercase			 =input_ids.shape[1]
      _lowercase			 =processor.batch_decode(outputs[:, prompt_length:]   ,     skip_special_tokens=A__  )
      _lowercase			 =[text.strip() for text in output_text]
      print('HF generation:'   ,     A__  )

      if pytorch_dump_folder_path is not None:
            processor.save_pretrained(A__  )
            hf_model.save_pretrained(A__  )

      if push_to_hub:
            processor.push_to_hub(F'''nielsr/{model_name}'''  )
            hf_model.push_to_hub(F'''nielsr/{model_name}'''  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  lowercase_             =     [
      'blip2-opt-2.7b',
      'blip2-opt-6.7b',
      'blip2-opt-2.7b-coco',
      'blip2-opt-6.7b-coco',
      'blip2-flan-t5-xl',
      'blip2-flan-t5-xl-coco',
      'blip2-flan-t5-xxl',
  ]
  parser.add_argument(
      '--model_name',
      default='blip2-opt-2.7b',
      choices=choices,
      type=str,
      help='Path to hf config.json of model to convert',
  )
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
      help='Whether to push the model and processor to the hub after converting',
  )

  lowercase_             =     parser.parse_args()

  convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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  )					->   Tuple:





      """simple docstring"""



      _lowercase			 ='\n'.join(A__  )
      Path(A__  ).open('w'  ).writelines(A__  )
def   a   (    A__			:     str  )					->   Union[str, 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  (      SCREAMING_SNAKE_CASE							):




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






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in ARTICLES      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) 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(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=lowerCAmelCase							,     max_target_length=lowerCAmelCase							,     src_lang=lowerCAmelCase							,     tgt_lang=lowerCAmelCase							,     )
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=2							,     collate_fn=train_dataset.collate_fn      )
        for batch in dataloader:
              assert isinstance(lowerCAmelCase							,     lowerCAmelCase      )
              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      )						->				str:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in ARTICLES      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in SUMMARIES      )
        _lowercase			 =4
        _lowercase			 =LegacySeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=20							,     max_target_length=lowerCAmelCase							,     )
        _lowercase			 =DataLoader(lowerCAmelCase							,     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      )						->				List[str]:
        '''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(lowerCAmelCase							,     lowerCAmelCase							,     128							,     lowerCAmelCase      )
        _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(lowerCAmelCase      ) < len(lowerCAmelCase      )
        assert len(lowerCAmelCase      ) == 1
        assert len(packed_examples[0]      ) == sum(len(lowerCAmelCase      ) 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
        _lowercase , _lowercase , _lowercase			 =self._get_dataset(max_len=64      )
        _lowercase			 =64
        _lowercase			 =ds.make_dynamic_sampler(lowerCAmelCase							,     required_batch_size_multiple=lowerCAmelCase      )
        _lowercase			 =[len(lowerCAmelCase      ) for x in batch_sampler]
        assert len(set(lowerCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(lowerCAmelCase      ) == len(lowerCAmelCase      )  # no dropped or added examples
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_sampler=lowerCAmelCase							,     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(lowerCAmelCase      )
              if num_src_tokens > (max_tokens * 1.1):
                    failures.append(lowerCAmelCase      )
        assert num_src_per_batch[0] == max(lowerCAmelCase      )
        if failures:
              raise AssertionError(F'''too many tokens in {len(lowerCAmelCase      )} batches'''      )




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






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

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

        _lowercase			 =tokenizer.pad_token_id

        def count_pad_tokens(lowerCAmelCase							,     lowerCAmelCase="input_ids"      ):
              return [batch[k].eq(lowerCAmelCase      ).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(lowerCAmelCase							,     k='labels'      )      ) < sum(count_pad_tokens(lowerCAmelCase							,     k='labels'      )      )
        assert sum(count_pad_tokens(lowerCAmelCase      )      ) < sum(count_pad_tokens(lowerCAmelCase      )      )
        assert len(lowerCAmelCase      ) == len(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase=1_000							,     lowerCAmelCase=128      )						->				Union[str, Any]:
        '''simple docstring'''






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

        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =SeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=lowerCAmelCase							,     max_target_length=lowerCAmelCase							,     n_obs=lowerCAmelCase							,     )
        return ds, max_tokens, tokenizer




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase , _lowercase , _lowercase			 =self._get_dataset()
        _lowercase			 =set(DistributedSortishSampler(lowerCAmelCase							,     256							,     num_replicas=2							,     rank=0							,     add_extra_examples=lowerCAmelCase      )      )
        _lowercase			 =set(DistributedSortishSampler(lowerCAmelCase							,     256							,     num_replicas=2							,     rank=1							,     add_extra_examples=lowerCAmelCase      )      )
        assert idsa.intersection(lowerCAmelCase      ) == set()




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






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase							,     use_fast=lowerCAmelCase      )
        if tok_name == MBART_TINY:
              _lowercase			 =SeqaSeqDataset(
                  lowerCAmelCase							,     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(
                  lowerCAmelCase							,     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(lowerCAmelCase      ) == 1 if tok_name == BART_TINY else len(lowerCAmelCase      ) == 0


