


'''simple docstring'''




import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				Optional[int]				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= checkpoint

	lowerCAmelCase					:    Any      							= {}

	lowerCAmelCase					:    Any      							= vae_state_dict['encoder.conv_in.weight']
	lowerCAmelCase					:    List[str]      							= vae_state_dict['encoder.conv_in.bias']
	lowerCAmelCase					:    Optional[Any]      							= vae_state_dict['encoder.conv_out.weight']
	lowerCAmelCase					:    Dict      							= vae_state_dict['encoder.conv_out.bias']
	lowerCAmelCase					:    List[str]      							= vae_state_dict['encoder.norm_out.weight']
	lowerCAmelCase					:    Union[str, Any]      							= vae_state_dict['encoder.norm_out.bias']

	lowerCAmelCase					:    Union[str, Any]      							= vae_state_dict['decoder.conv_in.weight']
	lowerCAmelCase					:    Optional[int]      							= vae_state_dict['decoder.conv_in.bias']
	lowerCAmelCase					:    int      							= vae_state_dict['decoder.conv_out.weight']
	lowerCAmelCase					:    Tuple      							= vae_state_dict['decoder.conv_out.bias']
	lowerCAmelCase					:    Dict      							= vae_state_dict['decoder.norm_out.weight']
	lowerCAmelCase					:    List[Any]      							= vae_state_dict['decoder.norm_out.bias']

	lowerCAmelCase					:    Optional[int]      							= vae_state_dict['quant_conv.weight']
	lowerCAmelCase					:    str      							= vae_state_dict['quant_conv.bias']
	lowerCAmelCase					:    List[str]      							= vae_state_dict['post_quant_conv.weight']
	lowerCAmelCase					:    int      							= vae_state_dict['post_quant_conv.bias']

	# Retrieves the keys for the encoder down blocks only
	lowerCAmelCase					:    str      							= len({'.'.join(layer.split('.'				)[:3]				) for layer in vae_state_dict if 'encoder.down' in layer}				)
	lowerCAmelCase					:    List[str]      							= {
	    layer_id: [key for key in vae_state_dict if F"down.{layer_id}" in key] for layer_id in range(_A				)
	}

	# Retrieves the keys for the decoder up blocks only
	lowerCAmelCase					:    Union[str, Any]      							= len({'.'.join(layer.split('.'				)[:3]				) for layer in vae_state_dict if 'decoder.up' in layer}				)
	lowerCAmelCase					:    Union[str, Any]      							= {
	    layer_id: [key for key in vae_state_dict if F"up.{layer_id}" in key] for layer_id in range(_A				)
	}

	for i in range(_A				):
		lowerCAmelCase					:    str      							= [key for key in down_blocks[i] if F"down.{i}" in key and F"down.{i}.downsample" not in key]

		if F"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
			lowerCAmelCase					:    int      							= vae_state_dict.pop(
			    F"encoder.down.{i}.downsample.conv.weight"				)
			lowerCAmelCase					:    Any      							= vae_state_dict.pop(
			    F"encoder.down.{i}.downsample.conv.bias"				)

		lowerCAmelCase					:    str      							= renew_vae_resnet_paths(_A				)
		lowerCAmelCase					:    List[Any]      							= {'old': F"down.{i}.block", 'new': F"down_blocks.{i}.resnets"}
		assign_to_checkpoint(_A					,   _A					,   _A					,   additional_replacements=[meta_path]					,   config=_A				)

	lowerCAmelCase					:    Dict      							= [key for key in vae_state_dict if 'encoder.mid.block' in key]
	lowerCAmelCase					:    Any      							= 2
	for i in range(1					,   num_mid_res_blocks + 1				):
		lowerCAmelCase					:    List[str]      							= [key for key in mid_resnets if F"encoder.mid.block_{i}" in key]

		lowerCAmelCase					:    List[str]      							= renew_vae_resnet_paths(_A				)
		lowerCAmelCase					:    int      							= {'old': F"mid.block_{i}", 'new': F"mid_block.resnets.{i - 1}"}
		assign_to_checkpoint(_A					,   _A					,   _A					,   additional_replacements=[meta_path]					,   config=_A				)

	lowerCAmelCase					:    List[Any]      							= [key for key in vae_state_dict if 'encoder.mid.attn' in key]
	lowerCAmelCase					:    Union[str, Any]      							= renew_vae_attention_paths(_A				)
	lowerCAmelCase					:    Optional[Any]      							= {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
	assign_to_checkpoint(_A					,   _A					,   _A					,   additional_replacements=[meta_path]					,   config=_A				)
	conv_attn_to_linear(_A				)

	for i in range(_A				):
		lowerCAmelCase					:    Dict      							= num_up_blocks - 1 - i
		lowerCAmelCase					:    Optional[Any]      							= [
		    key for key in up_blocks[block_id] if F"up.{block_id}" in key and F"up.{block_id}.upsample" not in key
		]

		if F"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
			lowerCAmelCase					:    List[str]      							= vae_state_dict[
			    F"decoder.up.{block_id}.upsample.conv.weight"
			]
			lowerCAmelCase					:    int      							= vae_state_dict[
			    F"decoder.up.{block_id}.upsample.conv.bias"
			]

		lowerCAmelCase					:    List[str]      							= renew_vae_resnet_paths(_A				)
		lowerCAmelCase					:    List[Any]      							= {'old': F"up.{block_id}.block", 'new': F"up_blocks.{i}.resnets"}
		assign_to_checkpoint(_A					,   _A					,   _A					,   additional_replacements=[meta_path]					,   config=_A				)

	lowerCAmelCase					:    Any      							= [key for key in vae_state_dict if 'decoder.mid.block' in key]
	lowerCAmelCase					:    Dict      							= 2
	for i in range(1					,   num_mid_res_blocks + 1				):
		lowerCAmelCase					:    Any      							= [key for key in mid_resnets if F"decoder.mid.block_{i}" in key]

		lowerCAmelCase					:    str      							= renew_vae_resnet_paths(_A				)
		lowerCAmelCase					:    Any      							= {'old': F"mid.block_{i}", 'new': F"mid_block.resnets.{i - 1}"}
		assign_to_checkpoint(_A					,   _A					,   _A					,   additional_replacements=[meta_path]					,   config=_A				)

	lowerCAmelCase					:    int      							= [key for key in vae_state_dict if 'decoder.mid.attn' in key]
	lowerCAmelCase					:    str      							= renew_vae_attention_paths(_A				)
	lowerCAmelCase					:    int      							= {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
	assign_to_checkpoint(_A					,   _A					,   _A					,   additional_replacements=[meta_path]					,   config=_A				)
	conv_attn_to_linear(_A				)
	return new_checkpoint

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				str					,   )    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= requests.get(
	    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'				)
	lowerCAmelCase					:    Any      							= io.BytesIO(r.content				)

	lowerCAmelCase					:    Optional[int]      							= OmegaConf.load(_A				)
	lowerCAmelCase					:    Optional[int]      							= 5_12
	lowerCAmelCase					:    str      							= 'cuda' if torch.cuda.is_available() else 'cpu'
	if checkpoint_path.endswith('safetensors'				):
		from safetensors import safe_open

		lowerCAmelCase					:    Optional[int]      							= {}
		with safe_open(_A					,   framework='pt'					,   device='cpu'				) as f:
			for key in f.keys():
				lowerCAmelCase					:    Any      							= f.get_tensor(_A				)
	else:
		lowerCAmelCase					:    Optional[Any]      							= torch.load(_A					,   map_location=_A				)['state_dict']

	# Convert the VAE model.
	lowerCAmelCase					:    Any      							= create_vae_diffusers_config(_A					,   image_size=_A				)
	lowerCAmelCase					:    Union[str, Any]      							= custom_convert_ldm_vae_checkpoint(_A					,   _A				)

	lowerCAmelCase					:    int      							= AutoencoderKL(**_A				)
	vae.load_state_dict(_A				)
	vae.save_pretrained(_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[str]		     =	argparse.ArgumentParser()

				parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

				_lowerCAmelCase  :		Optional[int]		     =	parser.parse_args()

				vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :		int		     =	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	[
				    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'NezhaForNextSentencePrediction',
				    'NezhaForMaskedLM',
				    'NezhaForPreTraining',
				    'NezhaForMultipleChoice',
				    'NezhaForQuestionAnswering',
				    'NezhaForSequenceClassification',
				    'NezhaForTokenClassification',
				    'NezhaModel',
				    'NezhaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nezha import (
								    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NezhaForMaskedLM,
								    NezhaForMultipleChoice,
								    NezhaForNextSentencePrediction,
								    NezhaForPreTraining,
								    NezhaForQuestionAnswering,
								    NezhaForSequenceClassification,
								    NezhaForTokenClassification,
								    NezhaModel,
								    NezhaPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase  :		str		     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
