




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)






def  snake_case(    __magic_name__   )			->							int:





     '''simple docstring'''


     lowercase :       List[Any]       =			VideoMAEConfig()

     set_architecture_configs(__magic_name__			,		__magic_name__   )

     if "finetuned" not in model_name:
          lowercase :       Tuple       =			False

     if "finetuned" in model_name:
          lowercase :       List[str]       =			'''huggingface/label-files'''
          if "kinetics" in model_name:
               lowercase :       List[str]       =			4_00
               lowercase :       Optional[Any]       =			'''kinetics400-id2label.json'''
          elif "ssv2" in model_name:
               lowercase :       List[Any]       =			1_74
               lowercase :       Dict       =			'''something-something-v2-id2label.json'''
          else:
               raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''   )
          lowercase :       Dict       =			json.load(open(hf_hub_download(__magic_name__			,		__magic_name__			,		repo_type='''dataset'''   )			,		'''r'''   )   )
          lowercase :       Union[str, Any]       =			{int(__magic_name__   ): v for k, v in idalabel.items()}
          lowercase :       Optional[int]       =			idalabel
          lowercase :       Any       =			{v: k for k, v in idalabel.items()}

     return config






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Tuple:





     '''simple docstring'''


     if "small" in model_name:
          lowercase :       List[str]       =			3_84
          lowercase :       Union[str, Any]       =			15_36
          lowercase :       List[str]       =			12
          lowercase :       Tuple       =			16
          lowercase :       List[Any]       =			12
          lowercase :       Optional[Any]       =			3
          lowercase :       Tuple       =			1_92
          lowercase :       str       =			7_68
     elif "large" in model_name:
          lowercase :       Tuple       =			10_24
          lowercase :       Any       =			40_96
          lowercase :       List[str]       =			24
          lowercase :       int       =			16
          lowercase :       Any       =			12
          lowercase :       str       =			8
          lowercase :       int       =			5_12
          lowercase :       List[str]       =			20_48
     elif "huge" in model_name:
          lowercase :       Any       =			12_80
          lowercase :       Any       =			51_20
          lowercase :       Dict       =			32
          lowercase :       Union[str, Any]       =			16
          lowercase :       Optional[Any]       =			12
          lowercase :       Tuple       =			8
          lowercase :       List[str]       =			6_40
          lowercase :       Union[str, Any]       =			25_60
     elif "base" not in model_name:
          raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''   )






def  snake_case(    __magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     if "encoder." in name:
          lowercase :       Any       =			name.replace('''encoder.'''			,		''''''   )
     if "cls_token" in name:
          lowercase :       str       =			name.replace('''cls_token'''			,		'''videomae.embeddings.cls_token'''   )
     if "decoder_pos_embed" in name:
          lowercase :       Dict       =			name.replace('''decoder_pos_embed'''			,		'''decoder.decoder_pos_embed'''   )
     if "pos_embed" in name and "decoder" not in name:
          lowercase :       str       =			name.replace('''pos_embed'''			,		'''videomae.embeddings.position_embeddings'''   )
     if "patch_embed.proj" in name:
          lowercase :       str       =			name.replace('''patch_embed.proj'''			,		'''videomae.embeddings.patch_embeddings.projection'''   )
     if "patch_embed.norm" in name:
          lowercase :       Any       =			name.replace('''patch_embed.norm'''			,		'''videomae.embeddings.norm'''   )
     if "decoder.blocks" in name:
          lowercase :       Dict       =			name.replace('''decoder.blocks'''			,		'''decoder.decoder_layers'''   )
     if "blocks" in name:
          lowercase :       Dict       =			name.replace('''blocks'''			,		'''videomae.encoder.layer'''   )
     if "attn.proj" in name:
          lowercase :       Union[str, Any]       =			name.replace('''attn.proj'''			,		'''attention.output.dense'''   )
     if "attn" in name and "bias" not in name:
          lowercase :       Union[str, Any]       =			name.replace('''attn'''			,		'''attention.self'''   )
     if "attn" in name:
          lowercase :       str       =			name.replace('''attn'''			,		'''attention.attention'''   )
     if "norm1" in name:
          lowercase :       List[Any]       =			name.replace('''norm1'''			,		'''layernorm_before'''   )
     if "norm2" in name:
          lowercase :       Optional[int]       =			name.replace('''norm2'''			,		'''layernorm_after'''   )
     if "mlp.fc1" in name:
          lowercase :       str       =			name.replace('''mlp.fc1'''			,		'''intermediate.dense'''   )
     if "mlp.fc2" in name:
          lowercase :       Dict       =			name.replace('''mlp.fc2'''			,		'''output.dense'''   )
     if "decoder_embed" in name:
          lowercase :       Tuple       =			name.replace('''decoder_embed'''			,		'''decoder.decoder_embed'''   )
     if "decoder_norm" in name:
          lowercase :       str       =			name.replace('''decoder_norm'''			,		'''decoder.decoder_norm'''   )
     if "decoder_pred" in name:
          lowercase :       List[str]       =			name.replace('''decoder_pred'''			,		'''decoder.decoder_pred'''   )
     if "norm.weight" in name and "decoder" not in name and "fc" not in name:
          lowercase :       Union[str, Any]       =			name.replace('''norm.weight'''			,		'''videomae.layernorm.weight'''   )
     if "norm.bias" in name and "decoder" not in name and "fc" not in name:
          lowercase :       List[Any]       =			name.replace('''norm.bias'''			,		'''videomae.layernorm.bias'''   )
     if "head" in name and "decoder" not in name:
          lowercase :       Tuple       =			name.replace('''head'''			,		'''classifier'''   )

     return name






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          lowercase :       int       =			orig_state_dict.pop(__magic_name__   )

          if key.startswith('''encoder.'''   ):
               lowercase :       Optional[int]       =			key.replace('''encoder.'''			,		''''''   )

          if "qkv" in key:
               lowercase :       List[Any]       =			key.split('''.'''   )
               if key.startswith('''decoder.blocks'''   ):
                    lowercase :       str       =			config.decoder_hidden_size
                    lowercase :       Any       =			int(key_split[2]   )
                    lowercase :       Optional[int]       =			'''decoder.decoder_layers.'''
                    if "weight" in key:
                         lowercase :       int       =			val[:dim, :]
                         lowercase :       Optional[Any]       =			val[dim : dim * 2, :]
                         lowercase :       Tuple       =			val[-dim:, :]
               else:
                    lowercase :       Union[str, Any]       =			config.hidden_size
                    lowercase :       List[Any]       =			int(key_split[1]   )
                    lowercase :       Union[str, Any]       =			'''videomae.encoder.layer.'''
                    if "weight" in key:
                         lowercase :       List[Any]       =			val[:dim, :]
                         lowercase :       List[str]       =			val[dim : dim * 2, :]
                         lowercase :       Dict       =			val[-dim:, :]
          else:
               lowercase :       Any       =			val

     return orig_state_dict






def  snake_case(    )			->							List[str]:





     '''simple docstring'''


     lowercase :       List[Any]       =			hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''			,		filename='''eating_spaghetti.npy'''			,		repo_type='''dataset'''   )
     lowercase :       Dict       =			np.load(__magic_name__   )
     return list(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[int]:





     '''simple docstring'''


     lowercase :       Any       =			get_videomae_config(__magic_name__   )

     if "finetuned" in model_name:
          lowercase :       Union[str, Any]       =			VideoMAEForVideoClassification(__magic_name__   )
     else:
          lowercase :       Any       =			VideoMAEForPreTraining(__magic_name__   )

     # download original checkpoint, hosted on Google Drive
     lowercase :       Optional[Any]       =			'''pytorch_model.bin'''
     gdown.cached_download(__magic_name__			,		__magic_name__			,		quiet=__magic_name__   )
     lowercase :       str       =			torch.load(__magic_name__			,		map_location='''cpu'''   )
     if "model" in files:
          lowercase :       Union[str, Any]       =			files['''model''']
     else:
          lowercase :       Optional[Any]       =			files['''module''']
     lowercase :       List[str]       =			convert_state_dict(__magic_name__			,		__magic_name__   )

     model.load_state_dict(__magic_name__   )
     model.eval()

     # verify model on basic input
     lowercase :       Any       =			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,		image_std=[0.5, 0.5, 0.5]   )
     lowercase :       Optional[Any]       =			prepare_video()
     lowercase :       Dict       =			image_processor(__magic_name__			,		return_tensors='''pt'''   )

     if "finetuned" not in model_name:
          lowercase :       int       =			hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''			,		filename='''bool_masked_pos.pt'''   )
          lowercase :       Union[str, Any]       =			torch.load(__magic_name__   )

     lowercase :       Optional[int]       =			model(**__magic_name__   )
     lowercase :       Any       =			outputs.logits

     lowercase :       Tuple       =			[
         '''videomae-small-finetuned-kinetics''',
         '''videomae-small-finetuned-ssv2''',
         # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
         '''videomae-base-short''',
         '''videomae-base-short-finetuned-kinetics''',
         '''videomae-base''',
         '''videomae-base-finetuned-kinetics''',
         '''videomae-large''',
         '''videomae-large-finetuned-kinetics''',
         '''videomae-huge-finetuned-kinetics''',
         # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
         '''videomae-base-short-ssv2''',
         '''videomae-base-short-finetuned-ssv2''',
         '''videomae-base-ssv2''',
         '''videomae-base-finetuned-ssv2''',
     ]

     # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
     if model_name == "videomae-small-finetuned-kinetics":
          lowercase :       Union[str, Any]       =			torch.Size([1, 4_00]   )
          lowercase :       List[Any]       =			torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]   )
     elif model_name == "videomae-small-finetuned-ssv2":
          lowercase :       List[str]       =			torch.Size([1, 1_74]   )
          lowercase :       int       =			torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]   )
     elif model_name == "videomae-base":
          lowercase :       Dict       =			torch.Size([1, 14_08, 15_36]   )
          lowercase :       Union[str, Any]       =			torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]   )
     elif model_name == "videomae-base-short":
          lowercase :       int       =			torch.Size([1, 14_08, 15_36]   )
          lowercase :       int       =			torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   )
          # we verified the loss both for normalized and unnormalized targets for this one
          lowercase :       str       =			torch.tensor([0.5_1_4_2]   ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]   )
     elif model_name == "videomae-large":
          lowercase :       str       =			torch.Size([1, 14_08, 15_36]   )
          lowercase :       Optional[Any]       =			torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]   )
     elif model_name == "videomae-large-finetuned-kinetics":
          lowercase :       Tuple       =			torch.Size([1, 4_00]   )
          lowercase :       Optional[int]       =			torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]   )
     elif model_name == "videomae-huge-finetuned-kinetics":
          lowercase :       Optional[int]       =			torch.Size([1, 4_00]   )
          lowercase :       List[Any]       =			torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]   )
     elif model_name == "videomae-base-short-finetuned-kinetics":
          lowercase :       Any       =			torch.Size([1, 4_00]   )
          lowercase :       List[str]       =			torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]   )
     elif model_name == "videomae-base-finetuned-kinetics":
          lowercase :       str       =			torch.Size([1, 4_00]   )
          lowercase :       int       =			torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]   )
     elif model_name == "videomae-base-short-ssv2":
          lowercase :       str       =			torch.Size([1, 14_08, 15_36]   )
          lowercase :       List[str]       =			torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]   )
     elif model_name == "videomae-base-short-finetuned-ssv2":
          lowercase :       Tuple       =			torch.Size([1, 1_74]   )
          lowercase :       int       =			torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]   )
     elif model_name == "videomae-base-ssv2":
          lowercase :       Optional[int]       =			torch.Size([1, 14_08, 15_36]   )
          lowercase :       Any       =			torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]   )
     elif model_name == "videomae-base-finetuned-ssv2":
          lowercase :       Dict       =			torch.Size([1, 1_74]   )
          lowercase :       Optional[Any]       =			torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]   )
     else:
          raise ValueError(F"""Model name not supported. Should be one of {model_names}"""   )

     # verify logits
     assert logits.shape == expected_shape
     if "finetuned" in model_name:
          assert torch.allclose(logits[0, :3]			,		__magic_name__			,		atol=1e-4   )
     else:
          print('''Logits:'''			,		logits[0, :3, :3]   )
          assert torch.allclose(logits[0, :3, :3]			,		__magic_name__			,		atol=1e-4   )
     print('''Logits ok!'''   )

     # verify loss, if applicable
     if model_name == "videomae-base-short":
          lowercase :       Any       =			outputs.loss
          assert torch.allclose(__magic_name__			,		__magic_name__			,		atol=1e-4   )
          print('''Loss ok!'''   )

     if pytorch_dump_folder_path is not None:
          print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""   )
          image_processor.save_pretrained(__magic_name__   )
          model.save_pretrained(__magic_name__   )

     if push_to_hub:
          print('''Pushing to the hub...'''   )
          model.push_to_hub(__magic_name__			,		organization='''nielsr'''   )


if __name__ == "__main__":
 lowerCAmelCase_						=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
     type=str,
     help=(
         'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
         ' download link.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default='/Users/nielsrogge/Documents/VideoMAE/Test',
     type=str,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 lowerCAmelCase_						=			parser.parse_args()
 convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, 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


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






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       List[str]       =			'''\n'''.join(__magic_name__   )
     Path(__magic_name__   ).open('''w'''   ).writelines(__magic_name__   )






def  snake_case(    __magic_name__   )			->							Optional[int]:





     '''simple docstring'''


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



class 						_A (  _lowerCamelCase				):






  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]				,						)
  @slow
  def  __a      (  self		:     List[str]				,						_A		:     Optional[Any]				)		->   Dict:






       """simple docstring"""







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






       """simple docstring"""







       lowercase :       int       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       int       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       lowercase :       Optional[int]       =			max(len(tokenizer.encode(_A				)				) for a in ARTICLES				)
       lowercase :       List[Any]       =			max(len(tokenizer.encode(_A				)				) for a in SUMMARIES				)
       lowercase :       List[Any]       =			4
       lowercase :       Any       =			LegacySeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=20				,						max_target_length=_A				,						)
       lowercase :       Optional[Any]       =			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		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

       lowercase :       List[str]       =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       lowercase :       Optional[int]       =			tmp_dir.joinpath('''train.source'''				).open().readlines()
       lowercase :       List[Any]       =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
       pack_data_dir(_A				,						_A				,						128				,						_A				)
       lowercase :       Dict       =			{x.name for x in tmp_dir.iterdir()}
       lowercase :       Optional[Any]       =			{x.name for x in save_dir.iterdir()}
       lowercase :       int       =			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		:     Any				)		->   Dict:






       """simple docstring"""







       if not FAIRSEQ_AVAILABLE:
            return
       lowercase      ,	lowercase      ,	lowercase :       Any       =			self._get_dataset(max_len=64				)
       lowercase :       List[Any]       =			64
       lowercase :       Any       =			ds.make_dynamic_sampler(_A				,						required_batch_size_multiple=_A				)
       lowercase :       Tuple       =			[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
       lowercase :       str       =			DataLoader(_A				,						batch_sampler=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       Optional[int]       =			[]
       lowercase :       str       =			[]
       for batch in data_loader:
            lowercase :       Tuple       =			batch['''input_ids'''].shape
            lowercase :       List[Any]       =			src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            lowercase :       Dict       =			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		:     int				)		->   Any:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Tuple       =			self._get_dataset(max_len=512				)
       lowercase :       Tuple       =			2
       lowercase :       Union[str, Any]       =			ds.make_sortish_sampler(_A				,						shuffle=_A				)

       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				,						sampler=_A				)

       lowercase :       int       =			tokenizer.pad_token_id

       def count_pad_tokens(_A		:     List[Any]				,						_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  __a      (  self		:     Any				,						_A		:     Union[str, Any]=1_000				,						_A		:     str=128				)		->   List[Any]:






       """simple docstring"""







       if os.getenv('''USE_REAL_DATA'''				,						_A				):
            lowercase :       Optional[Any]       =			'''examples/seq2seq/wmt_en_ro'''
            lowercase :       Optional[int]       =			max_len * 2 * 64
            if not Path(_A				).joinpath('''train.len'''				).exists():
                 save_len_file(_A				,						_A				)
       else:
            lowercase :       Tuple       =			'''examples/seq2seq/test_data/wmt_en_ro'''
            lowercase :       Optional[Any]       =			max_len * 4
            save_len_file(_A				,						_A				)

       lowercase :       Optional[Any]       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       Union[str, Any]       =			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		:     List[str]				)		->   List[str]:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Union[str, Any]       =			self._get_dataset()
       lowercase :       int       =			set(DistributedSortishSampler(_A				,						256				,						num_replicas=2				,						rank=0				,						add_extra_examples=_A				)				)
       lowercase :       Dict       =			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		:     Union[str, Any]				,						_A		:     Tuple				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			AutoTokenizer.from_pretrained(_A				,						use_fast=_A				)
       if tok_name == MBART_TINY:
            lowercase :       Tuple       =			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'''				,						)
            lowercase :       Union[str, Any]       =			train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
            lowercase :       List[Any]       =			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				,						)
            lowercase :       Dict       =			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