




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							int		   =							UNetaDModel(**UpperCAmelCase       )
					__magic_name__					:							Union[str, Any]		   =							checkpoint['''all_modules.0.W'''].data
					__magic_name__					:							Optional[int]		   =							checkpoint['''all_modules.0.W'''].data
					__magic_name__					:							str		   =							checkpoint['''all_modules.1.weight'''].data
					__magic_name__					:							int		   =							checkpoint['''all_modules.1.bias'''].data

					__magic_name__					:							List[str]		   =							checkpoint['''all_modules.2.weight'''].data
					__magic_name__					:							Tuple		   =							checkpoint['''all_modules.2.bias'''].data

					__magic_name__					:							Tuple		   =							checkpoint['''all_modules.3.weight'''].data
					__magic_name__					:							Union[str, Any]		   =							checkpoint['''all_modules.3.bias'''].data

					__magic_name__					:							str		   =							checkpoint[list(checkpoint.keys()       )[-4]].data
					__magic_name__					:							Any		   =							checkpoint[list(checkpoint.keys()       )[-3]].data
					__magic_name__					:							Optional[int]		   =							checkpoint[list(checkpoint.keys()       )[-2]].data
					__magic_name__					:							List[str]		   =							checkpoint[list(checkpoint.keys()       )[-1]].data

					__magic_name__					:							Optional[int]		   =							4

					def set_attention_weights(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							List[str]		   =							old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
										__magic_name__					:							Tuple		   =							old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
										__magic_name__					:							Dict		   =							old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

										__magic_name__					:							str		   =							old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
										__magic_name__					:							Tuple		   =							old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
										__magic_name__					:							Optional[Any]		   =							old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

										__magic_name__					:							Optional[int]		   =							old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
										__magic_name__					:							str		   =							old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

										__magic_name__					:							Any		   =							old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										__magic_name__					:							int		   =							old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

					def set_resnet_weights(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							Tuple		   =							old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
										__magic_name__					:							str		   =							old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
										__magic_name__					:							Dict		   =							old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										__magic_name__					:							Any		   =							old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

										__magic_name__					:							Optional[int]		   =							old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
										__magic_name__					:							Optional[Any]		   =							old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
										__magic_name__					:							Tuple		   =							old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
										__magic_name__					:							List[Any]		   =							old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

										__magic_name__					:							Tuple		   =							old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
										__magic_name__					:							Any		   =							old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

										if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
															__magic_name__					:							Any		   =							old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
															__magic_name__					:							int		   =							old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

					for i, block in enumerate(new_model_architecture.downsample_blocks       ):
										__magic_name__					:							Dict		   =							hasattr(UpperCAmelCase,	'''attentions'''       )
										for j in range(len(block.resnets       )       ):
															set_resnet_weights(block.resnets[j],	UpperCAmelCase,	UpperCAmelCase       )
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j],	UpperCAmelCase,	UpperCAmelCase       )
																				module_index += 1

										if hasattr(UpperCAmelCase,	'''downsamplers'''       ) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down,	UpperCAmelCase,	UpperCAmelCase       )
															module_index += 1
															__magic_name__					:							Union[str, Any]		   =							checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
															__magic_name__					:							int		   =							checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
															module_index += 1

					set_resnet_weights(new_model_architecture.mid_block.resnets[0],	UpperCAmelCase,	UpperCAmelCase       )
					module_index += 1
					set_attention_weights(new_model_architecture.mid_block.attentions[0],	UpperCAmelCase,	UpperCAmelCase       )
					module_index += 1
					set_resnet_weights(new_model_architecture.mid_block.resnets[1],	UpperCAmelCase,	UpperCAmelCase       )
					module_index += 1

					for i, block in enumerate(new_model_architecture.up_blocks       ):
										__magic_name__					:							Any		   =							hasattr(UpperCAmelCase,	'''attentions'''       )
										for j in range(len(block.resnets       )       ):
															set_resnet_weights(block.resnets[j],	UpperCAmelCase,	UpperCAmelCase       )
															module_index += 1
										if has_attentions:
															set_attention_weights(
															    block.attentions[0],	UpperCAmelCase,	UpperCAmelCase       )  # why can there only be a single attention layer for up?
															module_index += 1

										if hasattr(UpperCAmelCase,	'''resnet_up'''       ) and block.resnet_up is not None:
															__magic_name__					:							str		   =							checkpoint[F'''all_modules.{module_index}.weight'''].data
															__magic_name__					:							Optional[Any]		   =							checkpoint[F'''all_modules.{module_index}.bias'''].data
															module_index += 1
															__magic_name__					:							List[Any]		   =							checkpoint[F'''all_modules.{module_index}.weight'''].data
															__magic_name__					:							List[str]		   =							checkpoint[F'''all_modules.{module_index}.bias'''].data
															module_index += 1
															set_resnet_weights(block.resnet_up,	UpperCAmelCase,	UpperCAmelCase       )
															module_index += 1

					__magic_name__					:							str		   =							checkpoint[F'''all_modules.{module_index}.weight'''].data
					__magic_name__					:							int		   =							checkpoint[F'''all_modules.{module_index}.bias'''].data
					module_index += 1
					__magic_name__					:							str		   =							checkpoint[F'''all_modules.{module_index}.weight'''].data
					__magic_name__					:							List[Any]		   =							checkpoint[F'''all_modules.{module_index}.bias'''].data

					return new_model_architecture.state_dict()


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

				parser.add_argument(
				    '''--checkpoint_path''',
				    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
				    type=str,
				    required=False,
				    help='''Path to the checkpoint to convert.''',
				)

				parser.add_argument(
				    '''--config_file''',
				    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
				    type=str,
				    required=False,
				    help='''The config json file corresponding to the architecture.''',
				)

				parser.add_argument(
				    '''--dump_path''',
				    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
				    type=str,
				    required=False,
				    help='''Path to the output model.''',
				)

				lowercase_		=							parser.parse_args()

				lowercase_		=							torch.load(args.checkpoint_path, map_location='''cpu''')

				with open(args.config_file) as f:
								lowercase_		=							json.loads(f.read())

				lowercase_		=							convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

				if "sde" in config:
								del config["sde"]

				lowercase_		=							UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								lowercase_		=							ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

								lowercase_		=							ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)









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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							Tuple		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							str		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







@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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							List[Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							features.copy() if features else default_expected_features
					__magic_name__					:							str		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							Dict		   =							CsvDatasetReader(UpperCAmelCase,	features=UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							List[str]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	split=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type''',	[str, list]       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					if issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							csv_path
					elif issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							[csv_path]
					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=("train",)       )  ->Tuple:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					for split in splits:
										__magic_name__					:							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					__magic_name__					:							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():
										__magic_name__					:							Optional[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase       )







@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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							Dict		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Dict		   =							features.copy() if features else default_expected_features
					__magic_name__					:							Any		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							List[str]		   =							CsvDatasetReader({'''train''': csv_path},	features=UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase       )







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					if split:
										__magic_name__					:							Dict		   =							{split: csv_path}
					else:
										__magic_name__					:							Any		   =							{'''train''': csv_path, '''test''': csv_path}
					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							Union[str, Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase,	splits=list(path.keys()       )       )
					assert all(dataset[split].split == split for split in path.keys()       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					with open(UpperCAmelCase,	encoding='''utf-8'''       ) as csvfile:
										yield from csv.reader(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							List[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							int		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=1       ).write()

					__magic_name__					:							Optional[int]		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							Any		   =							iter_csv_file(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Optional[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							List[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=2       ).write()

					__magic_name__					:							Tuple		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							iter_csv_file(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							Any		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					with pytest.raises(UpperCAmelCase       ):
										CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=0       )



