





'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


_SCREAMING_SNAKE_CASE    =			os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
_SCREAMING_SNAKE_CASE    =			os.path.join(hf_cache_home, '''accelerate''')
_SCREAMING_SNAKE_CASE    =			os.path.join(cache_dir, '''default_config.yaml''')
_SCREAMING_SNAKE_CASE    =			os.path.join(cache_dir, '''default_config.yaml''')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
	_SCREAMING_SNAKE_CASE    =			default_yaml_config_file
else:
	_SCREAMING_SNAKE_CASE    =			default_json_config_file




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[str] ):
			if config_file is not None:
						if not os.path.isfile(lowerCamelCase_ ):
									raise FileNotFoundError(
									    f"The passed configuration file `{config_file}` does not exist. "
									    '''Please pass an existing file to `accelerate launch`, or use the the default one '''
									    '''created through `accelerate config` and run `accelerate launch` '''
									    '''without the `--config_file` argument.''' )
			else:
						__lowercase   =							default_config_file
			with open(lowerCamelCase_						,				'''r'''						,				encoding='''utf-8''' ) as f:
						if config_file.endswith('''.json''' ):
									if (
									    json.load(lowerCamelCase_ ).get('''compute_environment'''						,				ComputeEnvironment.LOCAL_MACHINE )
									    == ComputeEnvironment.LOCAL_MACHINE
									):
												__lowercase   =							ClusterConfig
									else:
												__lowercase   =							SageMakerConfig
									return config_class.from_json_file(json_file=lowerCamelCase_ )
						else:
									if (
									    yaml.safe_load(lowerCamelCase_ ).get('''compute_environment'''						,				ComputeEnvironment.LOCAL_MACHINE )
									    == ComputeEnvironment.LOCAL_MACHINE
									):
												__lowercase   =							ClusterConfig
									else:
												__lowercase   =							SageMakerConfig
									return config_class.from_yaml_file(yaml_file=lowerCamelCase_ )






@dataclass
class 							__lowercase				:







	'''simple docstring'''

	a			: ComputeEnvironment
	a			: Union[DistributedType, SageMakerDistributedType]
	a			: str
	a			: bool


	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				__lowercase   =							self.__dict__
				# For serialization, it's best to convert Enums to strings (or their underlying value type).
				for key, value in result.items():
							if isinstance(_lowerCamelCase   ,_lowerCamelCase       ):
										__lowercase   =							value.value
							if isinstance(_lowerCamelCase   ,_lowerCamelCase       ) and not bool(_lowerCamelCase       ):
										__lowercase   =							None
				__lowercase   =							{k: v for k, v in result.items() if v is not None}
				return result


	@classmethod
	def   _UpperCAmelCase   (cls   ,_lowerCamelCase=None       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							default_json_config_file if json_file is None else json_file
				with open(_lowerCamelCase   ,'''r'''   ,encoding='''utf-8'''       ) as f:
							__lowercase   =							json.load(_lowerCamelCase       )
				if "compute_environment" not in config_dict:
							__lowercase   =							ComputeEnvironment.LOCAL_MACHINE
				if "mixed_precision" not in config_dict:
							__lowercase   =							'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
				if "fp16" in config_dict:  # Convert the config to the new format.
							del config_dict["fp16"]
				if "dynamo_backend" in config_dict:  # Convert the config to the new format.
							__lowercase   =							config_dict.pop('''dynamo_backend'''       )
							__lowercase   =							{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
				if "use_cpu" not in config_dict:
							__lowercase   =							False
				return cls(**_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				with open(_lowerCamelCase   ,'''w'''   ,encoding='''utf-8'''       ) as f:
							__lowercase   =							json.dumps(self.to_dict()   ,indent=2   ,sort_keys=_lowerCamelCase       ) + '''\n'''
							f.write(_lowerCamelCase       )


	@classmethod
	def   _UpperCAmelCase   (cls   ,_lowerCamelCase=None       )							->							List[str]:







				'''simple docstring'''





				__lowercase   =							default_yaml_config_file if yaml_file is None else yaml_file
				with open(_lowerCamelCase   ,'''r'''   ,encoding='''utf-8'''       ) as f:
							__lowercase   =							yaml.safe_load(_lowerCamelCase       )
				if "compute_environment" not in config_dict:
							__lowercase   =							ComputeEnvironment.LOCAL_MACHINE

				if "mixed_precision" not in config_dict:
							__lowercase   =							'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
				if isinstance(config_dict['''mixed_precision''']   ,_lowerCamelCase       ) and not config_dict["mixed_precision"]:
							__lowercase   =							'''no'''
				if "fp16" in config_dict:  # Convert the config to the new format.
							del config_dict["fp16"]
				if "dynamo_backend" in config_dict:  # Convert the config to the new format.
							__lowercase   =							config_dict.pop('''dynamo_backend'''       )
							__lowercase   =							{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
				if "use_cpu" not in config_dict:
							__lowercase   =							False
				return cls(**_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,'''w'''   ,encoding='''utf-8'''       ) as f:
							yaml.safe_dump(self.to_dict()   ,_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





				if isinstance(self.compute_environment   ,_lowerCamelCase       ):
							__lowercase   =							ComputeEnvironment(self.compute_environment       )
				if isinstance(self.distributed_type   ,_lowerCamelCase       ):
							if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
										__lowercase   =							SageMakerDistributedType(self.distributed_type       )
							else:
										__lowercase   =							DistributedType(self.distributed_type       )
				if self.dynamo_config is None:
							__lowercase   =							{}






@dataclass
class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: int
	a			: int      =				0
	a			: int      =				1
	a			: Optional[str]      =				None
	a			: Optional[str]      =				None
	a			: Optional[int]      =				None
	a			: Optional[str]      =				"static"
	a			: Optional[bool]      =				False
	a			: str      =				"main"

	# args for deepspeed_plugin
	a			: dict      =				None
	# args for fsdp
	a			: dict      =				None
	# args for megatron_lm
	a			: dict      =				None
	# args for ipex
	a			: dict      =				None
	# args for TPU
	a			: bool      =				False

	# args for TPU pods
	a			: str      =				None
	a			: str      =				None
	a			: bool      =				False
	a			: bool      =				False
	a			: str      =				None
	a			: List[str]      =				None
	a			: List[str]      =				None
	a			: List[str]      =				None

	# args for dynamo
	a			: dict      =				None


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







				'''simple docstring'''





				if self.deepspeed_config is None:
							__lowercase   =							{}
				if self.fsdp_config is None:
							__lowercase   =							{}
				if self.megatron_lm_config is None:
							__lowercase   =							{}
				if self.ipex_config is None:
							__lowercase   =							{}
				return super().__post_init__()






@dataclass
class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: str
	a			: str
	a			: Optional[str]      =				None
	a			: Optional[str]      =				None
	a			: str      =				"us-east-1"
	a			: int      =				1
	a			: str      =				"all"
	a			: str      =				F"""accelerate-sagemaker-{num_machines}"""
	a			: str      =				SAGEMAKER_PYTORCH_VERSION
	a			: str      =				SAGEMAKER_TRANSFORMERS_VERSION
	a			: str      =				SAGEMAKER_PYTHON_VERSION
	a			: str      =				None
	a			: str      =				None
	a			: dict      =				None
	a			: dict      =				None












'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.1.weight'''].data
			__lowercase   =							checkpoint['''all_modules.1.bias'''].data

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

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

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

			__lowercase   =							4

			def set_attention_weights(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

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

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

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

			def set_resnet_weights(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Any ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

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

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

						if hasattr(lowerCamelCase_						,				'''downsamplers''' ) and block.downsamplers is not None:
									set_resnet_weights(block.resnet_down						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1
									__lowercase   =							checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
									module_index += 1

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

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

						if hasattr(lowerCamelCase_						,				'''resnet_up''' ) and block.resnet_up is not None:
									__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
									module_index += 1
									__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
									module_index += 1
									set_resnet_weights(block.resnet_up						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1

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

			return new_model_architecture.state_dict()


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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.''',
	)

	_SCREAMING_SNAKE_CASE    =			parser.parse_args()

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

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

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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





