


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Tuple:
						_a     =			UNetaDModel(**_UpperCAmelCase				)
						_a     =			checkpoint['all_modules.0.W'].data
						_a     =			checkpoint['all_modules.0.W'].data
						_a     =			checkpoint['all_modules.1.weight'].data
						_a     =			checkpoint['all_modules.1.bias'].data

						_a     =			checkpoint['all_modules.2.weight'].data
						_a     =			checkpoint['all_modules.2.bias'].data

						_a     =			checkpoint['all_modules.3.weight'].data
						_a     =			checkpoint['all_modules.3.bias'].data

						_a     =			checkpoint[list(checkpoint.keys()				)[-4]].data
						_a     =			checkpoint[list(checkpoint.keys()				)[-3]].data
						_a     =			checkpoint[list(checkpoint.keys()				)[-2]].data
						_a     =			checkpoint[list(checkpoint.keys()				)[-1]].data

						_a     =			4

						def set_attention_weights(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				):
												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
												_a     =			old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

												_a     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

						def set_resnet_weights(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				):
												_a     =			old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

												_a     =			old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
												_a     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

												_a     =			old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
												_a     =			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:
																		_a     =			old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
																		_a     =			old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

						for i, block in enumerate(new_model_architecture.downsample_blocks				):
												_a     =			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
																		_a     =			checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
																		_a     =			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				):
												_a     =			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:
																		_a     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
																		_a     =			checkpoint[f"""all_modules.{module_index}.bias"""].data
																		module_index += 1
																		_a     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
																		_a     =			checkpoint[f"""all_modules.{module_index}.bias"""].data
																		module_index += 1
																		set_resnet_weights(block.resnet_up							,     _UpperCAmelCase							,     _UpperCAmelCase				)
																		module_index += 1

						_a     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
						_a     =			checkpoint[f"""all_modules.{module_index}.bias"""].data
						module_index += 1
						_a     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
						_a     =			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)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_								=    {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    ['CodeGenTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'CodeGenForCausalLM',
		    'CodeGenModel',
		    'CodeGenPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
		from .tokenization_codegen import CodeGenTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_codegen_fast import CodeGenTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_codegen import (
				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CodeGenForCausalLM,
				    CodeGenModel,
				    CodeGenPreTrainedModel,
				)

else:
		import sys

		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


