




import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

       assert isinstance(lowercase_			,    lowercase_						)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 1
       assert dataset.column_names == ["text"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''			,    [False, True]						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Any:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_			,    keep_in_memory=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)







@pytest.mark.parametrize(
    '''features'''			,    [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]			,    )
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   features.copy() if features else default_expected_features
       A__							=   (
           Features({feature: Value(lowercase_						) for feature, dtype in features.items()}						) if features is not None else None
       )
       A__							=   TextDatasetReader(lowercase_			,    features=lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)







@pytest.mark.parametrize('''split'''			,    [None, NamedSplit('''train'''						), '''train''', '''test''']						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_			,    split=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''			,    [str, list]						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       if issubclass(lowercase_			,    lowercase_						):
              A__							=   text_path
       elif issubclass(lowercase_			,    lowercase_						):
              A__							=   [text_path]
       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_dataset(lowercase_			,    lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=("train",)						)       ->	Union[str, Any]:



       """simple docstring"""

       assert isinstance(lowercase_			,    lowercase_						)
       for split in splits:
              A__							=   dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 1
              assert dataset.column_names == ["text"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''			,    [False, True]						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__							=   TextDatasetReader({'''train''': text_path}			,    cache_dir=lowercase_			,    keep_in_memory=lowercase_						).read()
       _check_text_datasetdict(lowercase_			,    lowercase_						)







@pytest.mark.parametrize(
    '''features'''			,    [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]			,    )
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   tmp_path / '''cache'''
       # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
       A__							=   {'''text''': '''string'''}
       A__							=   features.copy() if features else default_expected_features
       A__							=   (
           Features({feature: Value(lowercase_						) for feature, dtype in features.items()}						) if features is not None else None
       )
       A__							=   TextDatasetReader({'''train''': text_path}			,    features=lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_datasetdict(lowercase_			,    lowercase_						)







@pytest.mark.parametrize('''split'''			,    [None, NamedSplit('''train'''						), '''train''', '''test''']						)
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       if split:
              A__							=   {split: text_path}
       else:
              A__							=   '''train'''
              A__							=   {'''train''': text_path, '''test''': text_path}
       A__							=   tmp_path / '''cache'''
       A__							=   {'''text''': '''string'''}
       A__							=   TextDatasetReader(lowercase_			,    cache_dir=lowercase_						).read()
       _check_text_datasetdict(lowercase_			,    lowercase_			,    splits=list(path.keys()						)						)
       assert all(dataset[split].split == split for split in path.keys()						)








import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       if "cls_token" in name:
              A__							=   name.replace('''cls_token'''			,    '''vit.embeddings.cls_token'''						)
       if "mask_token" in name:
              A__							=   name.replace('''mask_token'''			,    '''decoder.mask_token'''						)
       if "decoder_pos_embed" in name:
              A__							=   name.replace('''decoder_pos_embed'''			,    '''decoder.decoder_pos_embed'''						)
       if "pos_embed" in name and "decoder" not in name:
              A__							=   name.replace('''pos_embed'''			,    '''vit.embeddings.position_embeddings'''						)
       if "patch_embed.proj" in name:
              A__							=   name.replace('''patch_embed.proj'''			,    '''vit.embeddings.patch_embeddings.projection'''						)
       if "patch_embed.norm" in name:
              A__							=   name.replace('''patch_embed.norm'''			,    '''vit.embeddings.norm'''						)
       if "decoder_blocks" in name:
              A__							=   name.replace('''decoder_blocks'''			,    '''decoder.decoder_layers'''						)
       if "blocks" in name:
              A__							=   name.replace('''blocks'''			,    '''vit.encoder.layer'''						)
       if "attn.proj" in name:
              A__							=   name.replace('''attn.proj'''			,    '''attention.output.dense'''						)
       if "attn" in name:
              A__							=   name.replace('''attn'''			,    '''attention.self'''						)
       if "norm1" in name:
              A__							=   name.replace('''norm1'''			,    '''layernorm_before'''						)
       if "norm2" in name:
              A__							=   name.replace('''norm2'''			,    '''layernorm_after'''						)
       if "mlp.fc1" in name:
              A__							=   name.replace('''mlp.fc1'''			,    '''intermediate.dense'''						)
       if "mlp.fc2" in name:
              A__							=   name.replace('''mlp.fc2'''			,    '''output.dense'''						)
       if "decoder_embed" in name:
              A__							=   name.replace('''decoder_embed'''			,    '''decoder.decoder_embed'''						)
       if "decoder_norm" in name:
              A__							=   name.replace('''decoder_norm'''			,    '''decoder.decoder_norm'''						)
       if "decoder_pred" in name:
              A__							=   name.replace('''decoder_pred'''			,    '''decoder.decoder_pred'''						)
       if "norm.weight" in name and "decoder" not in name:
              A__							=   name.replace('''norm.weight'''			,    '''vit.layernorm.weight'''						)
       if "norm.bias" in name and "decoder" not in name:
              A__							=   name.replace('''norm.bias'''			,    '''vit.layernorm.bias'''						)

       return name







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__							=   orig_state_dict.pop(lowercase_						)

              if "qkv" in key:
                     A__							=   key.split('''.'''						)
                     A__							=   int(key_split[1]						)
                     if "decoder_blocks" in key:
                            A__							=   config.decoder_hidden_size
                            A__							=   '''decoder.decoder_layers.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]
                     else:
                            A__							=   config.hidden_size
                            A__							=   '''vit.encoder.layer.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]

              else:
                     A__							=   val

       return orig_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   ViTMAEConfig()
       if "large" in checkpoint_url:
              A__							=   1_024
              A__							=   4_096
              A__							=   24
              A__							=   16
       elif "huge" in checkpoint_url:
              A__							=   14
              A__							=   1_280
              A__							=   5_120
              A__							=   32
              A__							=   16

       A__							=   ViTMAEForPreTraining(lowercase_						)

       A__							=   torch.hub.load_state_dict_from_url(lowercase_			,    map_location='''cpu'''						)['''model''']

       A__							=   ViTMAEImageProcessor(size=config.image_size						)

       A__							=   convert_state_dict(lowercase_			,    lowercase_						)

       model.load_state_dict(lowercase_						)
       model.eval()

       A__							=   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

       A__							=   Image.open(requests.get(lowercase_			,    stream=lowercase_						).raw						)
       A__							=   ViTMAEImageProcessor(size=config.image_size						)
       A__							=   image_processor(images=lowercase_			,    return_tensors='''pt'''						)

       # forward pass
       torch.manual_seed(2						)
       A__							=   model(**lowercase_						)
       A__							=   outputs.logits

       if "large" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]						)
       elif "huge" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]						)
       else:
              A__							=   torch.tensor(
                  [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]						)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]			,    lowercase_			,    atol=1E-4						)

       print(f"""Saving model to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowercase_						)

       print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
       image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


