






"""simple docstring"""


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowercase_								=	{512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					f'pretrained_models/{model_name}'
  if not os.path.isfile(lowercase__					):
    os.makedirs('''pretrained_models'''						,			exist_ok=lowercase__					)
    __A		     =					f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
    download_url(lowercase__						,			'''pretrained_models'''					)
  __A		     =					torch.load(lowercase__						,			map_location=lambda __UpperCamelCase						,			__UpperCamelCase					: storage					)
  return model



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					download_model(pretrained_models[args.image_size]					)

  __A		     =					state_dict['x_embedder.proj.weight']
  __A		     =					state_dict['x_embedder.proj.bias']
  state_dict.pop('''x_embedder.proj.weight'''					)
  state_dict.pop('''x_embedder.proj.bias'''					)

  for depth in range(2_8					):
    __A		     =					state_dict[
        't_embedder.mlp.0.weight'
    ]
    __A		     =					state_dict[
        't_embedder.mlp.0.bias'
    ]
    __A		     =					state_dict[
        't_embedder.mlp.2.weight'
    ]
    __A		     =					state_dict[
        't_embedder.mlp.2.bias'
    ]
    __A		     =					state_dict[
        'y_embedder.embedding_table.weight'
    ]

    __A		     =					state_dict[
        f'blocks.{depth}.adaLN_modulation.1.weight'
    ]
    __A		     =					state_dict[
        f'blocks.{depth}.adaLN_modulation.1.bias'
    ]

    __A		     =					torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']						,			3						,			dim=0					)
    __A		     =					torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']						,			3						,			dim=0					)

    __A		     =					q
    __A		     =					q_bias
    __A		     =					k
    __A		     =					k_bias
    __A		     =					v
    __A		     =					v_bias

    __A		     =					state_dict[
        f'blocks.{depth}.attn.proj.weight'
    ]
    __A		     =					state_dict[f'blocks.{depth}.attn.proj.bias']

    __A		     =					state_dict[f'blocks.{depth}.mlp.fc1.weight']
    __A		     =					state_dict[f'blocks.{depth}.mlp.fc1.bias']
    __A		     =					state_dict[f'blocks.{depth}.mlp.fc2.weight']
    __A		     =					state_dict[f'blocks.{depth}.mlp.fc2.bias']

    state_dict.pop(f'blocks.{depth}.attn.qkv.weight'					)
    state_dict.pop(f'blocks.{depth}.attn.qkv.bias'					)
    state_dict.pop(f'blocks.{depth}.attn.proj.weight'					)
    state_dict.pop(f'blocks.{depth}.attn.proj.bias'					)
    state_dict.pop(f'blocks.{depth}.mlp.fc1.weight'					)
    state_dict.pop(f'blocks.{depth}.mlp.fc1.bias'					)
    state_dict.pop(f'blocks.{depth}.mlp.fc2.weight'					)
    state_dict.pop(f'blocks.{depth}.mlp.fc2.bias'					)
    state_dict.pop(f'blocks.{depth}.adaLN_modulation.1.weight'					)
    state_dict.pop(f'blocks.{depth}.adaLN_modulation.1.bias'					)

  state_dict.pop('''t_embedder.mlp.0.weight'''					)
  state_dict.pop('''t_embedder.mlp.0.bias'''					)
  state_dict.pop('''t_embedder.mlp.2.weight'''					)
  state_dict.pop('''t_embedder.mlp.2.bias'''					)
  state_dict.pop('''y_embedder.embedding_table.weight'''					)

  __A		     =					state_dict['final_layer.adaLN_modulation.1.weight']
  __A		     =					state_dict['final_layer.adaLN_modulation.1.bias']
  __A		     =					state_dict['final_layer.linear.weight']
  __A		     =					state_dict['final_layer.linear.bias']

  state_dict.pop('''final_layer.linear.weight'''					)
  state_dict.pop('''final_layer.linear.bias'''					)
  state_dict.pop('''final_layer.adaLN_modulation.1.weight'''					)
  state_dict.pop('''final_layer.adaLN_modulation.1.bias'''					)

  # DiT XL/2
  __A		     =					TransformeraDModel(
      sample_size=args.image_size // 8						,			num_layers=2_8						,			attention_head_dim=7_2						,			in_channels=4						,			out_channels=8						,			patch_size=2						,			attention_bias=lowercase__						,			num_attention_heads=1_6						,			activation_fn='''gelu-approximate'''						,			num_embeds_ada_norm=1_0_0_0						,			norm_type='''ada_norm_zero'''						,			norm_elementwise_affine=lowercase__						,			)
  transformer.load_state_dict(lowercase__						,			strict=lowercase__					)

  __A		     =					DDIMScheduler(
      num_train_timesteps=1_0_0_0						,			beta_schedule='''linear'''						,			prediction_type='''epsilon'''						,			clip_sample=lowercase__						,			)

  __A		     =					AutoencoderKL.from_pretrained(args.vae_model					)

  __A		     =					DiTPipeline(transformer=lowercase__						,			vae=lowercase__						,			scheduler=lowercase__					)

  if args.save:
    pipeline.save_pretrained(args.checkpoint_path					)


if __name__ == "__main__":
       lowercase_								=	argparse.ArgumentParser()

       parser.add_argument(
           '--image_size',
           default=256,
           type=int,
           required=False,
           help='Image size of pretrained model, either 256 or 512.',
       )
       parser.add_argument(
           '--vae_model',
           default='stabilityai/sd-vae-ft-ema',
           type=str,
           required=False,
           help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
       )
       parser.add_argument(
           '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
       )
       parser.add_argument(
           '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
       )

       lowercase_								=	parser.parse_args()
       main(args)











'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    lowercase_   (		lowercase__					,	lowercase__    ) ->List[str]:
						assert isinstance(lowercase__					,	lowercase__    )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'					,	[False, True]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





@pytest.mark.parametrize(
    'features'					,	[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					,	)
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: str	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: Union[str, Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: List[str]	       =		features.copy() if features else default_expected_features
						_snake_case: str	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Optional[int]	       =		CsvDatasetReader(lowercase__					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						_snake_case: Dict	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	split=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'					,	[str, list]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						if issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Optional[Any]	       =		csv_path
						elif issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Tuple	       =		[csv_path]
						_snake_case: Tuple	       =		tmp_path / 'cache'
						_snake_case: Any	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Union[str, Any]	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__=("train",)    ) ->str:
						assert isinstance(lowercase__					,	lowercase__    )
						for split in splits:
												_snake_case: Optional[int]	       =		dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'					,	[False, True]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: Optional[Any]	       =		tmp_path / 'cache'
						_snake_case: Optional[Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Dict	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__    )





@pytest.mark.parametrize(
    'features'					,	[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					,	)
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: str	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		features.copy() if features else default_expected_features
						_snake_case: int	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Tuple	       =		CsvDatasetReader({'train': csv_path}					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						if split:
												_snake_case: Optional[Any]	       =		{split: csv_path}
						else:
												_snake_case: Tuple	       =		{'train': csv_path, 'test': csv_path}
						_snake_case: Union[str, Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__					,	splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )





def    lowercase_   (		lowercase__    ) ->Union[str, Any]:
						with open(lowercase__					,	encoding='utf-8'    ) as csvfile:
												yield from csv.reader(lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: Optional[Any]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=1    ).write()

						_snake_case: Optional[int]	       =		iter_csv_file(lowercase__    )
						_snake_case: List[Any]	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						_snake_case: List[str]	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: List[str]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=2    ).write()

						_snake_case: Optional[Any]	       =		iter_csv_file(lowercase__    )
						_snake_case: Tuple	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Any:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: Any	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: str	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						with pytest.raises(lowercase__    ):
												CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=0    )





