import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		int			    =    UNetaDModel(**UpperCamelCase__						)
    _lowerCamelCase  :		Optional[Any]			    =    checkpoint["""all_modules.0.W"""].data
    _lowerCamelCase  :		Union[str, Any]			    =    checkpoint["""all_modules.0.W"""].data
    _lowerCamelCase  :		Optional[Any]			    =    checkpoint["""all_modules.1.weight"""].data
    _lowerCamelCase  :		str			    =    checkpoint["""all_modules.1.bias"""].data

    _lowerCamelCase  :		Optional[Any]			    =    checkpoint["""all_modules.2.weight"""].data
    _lowerCamelCase  :		List[str]			    =    checkpoint["""all_modules.2.bias"""].data

    _lowerCamelCase  :		int			    =    checkpoint["""all_modules.3.weight"""].data
    _lowerCamelCase  :		Optional[int]			    =    checkpoint["""all_modules.3.bias"""].data

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

    _lowerCamelCase  :		Dict			    =    4

    def set_attention_weights(__A ,			__A ,			__A						):
        _lowerCamelCase  :		Tuple			    =    old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
        _lowerCamelCase  :		Dict			    =    old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
        _lowerCamelCase  :		Tuple			    =    old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

        _lowerCamelCase  :		Optional[Any]			    =    old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
        _lowerCamelCase  :		int			    =    old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
        _lowerCamelCase  :		str			    =    old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

        _lowerCamelCase  :		Any			    =    old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
        _lowerCamelCase  :		List[str]			    =    old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

        _lowerCamelCase  :		List[Any]			    =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
        _lowerCamelCase  :		Any			    =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

    def set_resnet_weights(__A ,			__A ,			__A						):
        _lowerCamelCase  :		List[str]			    =    old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
        _lowerCamelCase  :		Any			    =    old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
        _lowerCamelCase  :		Tuple			    =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
        _lowerCamelCase  :		Tuple			    =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

        _lowerCamelCase  :		Optional[Any]			    =    old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
        _lowerCamelCase  :		List[str]			    =    old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
        _lowerCamelCase  :		str			    =    old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
        _lowerCamelCase  :		Dict			    =    old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

        _lowerCamelCase  :		Dict			    =    old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
        _lowerCamelCase  :		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:
            _lowerCamelCase  :		Tuple			    =    old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
            _lowerCamelCase  :		List[str]			    =    old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

    for i, block in enumerate(new_model_architecture.downsample_blocks						):
        _lowerCamelCase  :		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
            _lowerCamelCase  :		Optional[Any]			    =    checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
            _lowerCamelCase  :		Optional[Any]			    =    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						):
        _lowerCamelCase  :		Tuple			    =    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:
            _lowerCamelCase  :		Optional[Any]			    =    checkpoint[F"""all_modules.{module_index}.weight"""].data
            _lowerCamelCase  :		int			    =    checkpoint[F"""all_modules.{module_index}.bias"""].data
            module_index += 1
            _lowerCamelCase  :		List[str]			    =    checkpoint[F"""all_modules.{module_index}.weight"""].data
            _lowerCamelCase  :		Union[str, Any]			    =    checkpoint[F"""all_modules.{module_index}.bias"""].data
            module_index += 1
            set_resnet_weights(block.resnet_up ,			UpperCamelCase__ ,			UpperCamelCase__						)
            module_index += 1

    _lowerCamelCase  :		Dict			    =    checkpoint[F"""all_modules.{module_index}.weight"""].data
    _lowerCamelCase  :		List[str]			    =    checkpoint[F"""all_modules.{module_index}.bias"""].data
    module_index += 1
    _lowerCamelCase  :		List[Any]			    =    checkpoint[F"""all_modules.{module_index}.weight"""].data
    _lowerCamelCase  :		Dict			    =    checkpoint[F"""all_modules.{module_index}.bias"""].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
    lowerCAmelCase      :				Any     			=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.",
    )

    lowerCAmelCase      :				str     			=parser.parse_args()

    lowerCAmelCase      :				List[str]     			=torch.load(args.checkpoint_path, map_location="cpu")

    with open(args.config_file) as f:
        lowerCAmelCase      :				Union[str, Any]     			=json.loads(f.read())

    lowerCAmelCase      :				Union[str, Any]     			=convert_ncsnpp_checkpoint(
        checkpoint,
        config,
    )

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

    lowerCAmelCase      :				Tuple     			=UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        lowerCAmelCase      :				Union[str, Any]     			=ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

        lowerCAmelCase      :				Optional[Any]     			=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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  features.copy() if features else default_expected_features
				__lowerCAmelCase					=  (
				    Features({feature: Value(UpperCamelCase__   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			features=UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize("""split"""						,			[None, NamedSplit("""train"""   ), """train""", """test"""]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				if issubclass(UpperCamelCase__						,			UpperCamelCase__   ):
								__lowerCAmelCase					=  csv_path
				elif issubclass(UpperCamelCase__						,			UpperCamelCase__   ):
								__lowerCAmelCase					=  [csv_path]
				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=("train",)   )     ->     Optional[int]:







				'''simple docstring'''


				assert isinstance(UpperCamelCase__						,			UpperCamelCase__   )
				for split in splits:
								__lowerCAmelCase					=  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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  features.copy() if features else default_expected_features
				__lowerCAmelCase					=  (
				    Features({feature: Value(UpperCamelCase__   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				__lowerCAmelCase					=  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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				if split:
								__lowerCAmelCase					=  {split: csv_path}
				else:
								__lowerCAmelCase					=  {"""train""": csv_path, """test""": csv_path}
				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  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      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=1   ).write()

				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )
				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )

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





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







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=2   ).write()

				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )
				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )

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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				with pytest.raises(UpperCamelCase__   ):
								CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=0   )





