


"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      Dict	)							->	int:

	'''simple docstring'''


	snake_case__  :						Any		=      UNetaDModel(**_UpperCAmelCase	)
	snake_case__  :						List[str]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						Union[str, Any]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.bias"].data

	snake_case__  :						str		=      checkpoint["all_modules.2.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.2.bias"].data

	snake_case__  :						Tuple		=      checkpoint["all_modules.3.weight"].data
	snake_case__  :						Any		=      checkpoint["all_modules.3.bias"].data

	snake_case__  :						Any		=      checkpoint[list(checkpoint.keys()	)[-4]].data
	snake_case__  :						Dict		=      checkpoint[list(checkpoint.keys()	)[-3]].data
	snake_case__  :						List[str]		=      checkpoint[list(checkpoint.keys()	)[-2]].data
	snake_case__  :						Union[str, Any]		=      checkpoint[list(checkpoint.keys()	)[-1]].data

	snake_case__  :						Tuple		=      4

	def set_attention_weights(_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      Dict	):
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

		snake_case__  :						Dict		=      old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

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

	def set_resnet_weights(_UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      int	):
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
		snake_case__  :						Optional[int]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

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

		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
		snake_case__  :						Optional[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:
			snake_case__  :						List[str]		=      old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
			snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

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

	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Any		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
	module_index += 1
	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Union[str, 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)









"""simple docstring"""






import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info



def 			A__		(					_UpperCAmelCase    :      Optional[int]	)							->	Tuple:

	'''simple docstring'''


	class 			SCREAMING_SNAKE_CASE_  :




								'''simple docstring'''





								def __init__(						self	,					lowerCamelCase__)   ->	None:







									'''simple docstring'''




									assert isinstance(lowerCamelCase__	,					(bytes, str))
									snake_case__  :						int		=      content







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







									'''simple docstring'''




									return json.loads(self.content)

	def mock_response(*_UpperCAmelCase    :      str				,    **_UpperCAmelCase    :      Optional[int]	):
		assert args[0] == AUTHENTICATED_USER_ENDPOINT
		assert "Authorization" in kwargs["headers"]
		assert kwargs["headers"]["Authorization"].startswith("token "	)
		assert "Accept" in kwargs["headers"]
		return FakeResponse(b"{\"login\":\"test\",\"id\":1}"	)

	monkeypatch.setattr(_UpperCAmelCase				,    "get"				,    _UpperCAmelCase	)
	snake_case__  :						str		=      fetch_github_info("token"	)
	assert result["login"] == "test"
	assert result["id"] == 1





