
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  __magic_name__ (   __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Optional[int]							)			->   Any:
				__lowerCamelCase    					=	checkpoint

				__lowerCamelCase    					=	{}

				__lowerCamelCase    					=	vae_state_dict['''encoder.conv_in.weight''']
				__lowerCamelCase    					=	vae_state_dict['''encoder.conv_in.bias''']
				__lowerCamelCase    					=	vae_state_dict['''encoder.conv_out.weight''']
				__lowerCamelCase    					=	vae_state_dict['''encoder.conv_out.bias''']
				__lowerCamelCase    					=	vae_state_dict['''encoder.norm_out.weight''']
				__lowerCamelCase    					=	vae_state_dict['''encoder.norm_out.bias''']

				__lowerCamelCase    					=	vae_state_dict['''decoder.conv_in.weight''']
				__lowerCamelCase    					=	vae_state_dict['''decoder.conv_in.bias''']
				__lowerCamelCase    					=	vae_state_dict['''decoder.conv_out.weight''']
				__lowerCamelCase    					=	vae_state_dict['''decoder.conv_out.bias''']
				__lowerCamelCase    					=	vae_state_dict['''decoder.norm_out.weight''']
				__lowerCamelCase    					=	vae_state_dict['''decoder.norm_out.bias''']

				__lowerCamelCase    					=	vae_state_dict['''quant_conv.weight''']
				__lowerCamelCase    					=	vae_state_dict['''quant_conv.bias''']
				__lowerCamelCase    					=	vae_state_dict['''post_quant_conv.weight''']
				__lowerCamelCase    					=	vae_state_dict['''post_quant_conv.bias''']

				# Retrieves the keys for the encoder down blocks only
				__lowerCamelCase    					=	len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in vae_state_dict if '''encoder.down''' in layer}							)
				__lowerCamelCase    					=	{
				    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(__lowerCAmelCase							)
				}

				# Retrieves the keys for the decoder up blocks only
				__lowerCamelCase    					=	len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in vae_state_dict if '''decoder.up''' in layer}							)
				__lowerCamelCase    					=	{
				    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(__lowerCAmelCase							)
				}

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	[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    					=	vae_state_dict.pop(
												    f'''encoder.down.{i}.downsample.conv.weight'''							)
												__lowerCamelCase    					=	vae_state_dict.pop(
												    f'''encoder.down.{i}.downsample.conv.bias'''							)

								__lowerCamelCase    					=	renew_vae_resnet_paths(__lowerCAmelCase							)
								__lowerCamelCase    					=	{'''old''': f'''down.{i}.block''', '''new''': f'''down_blocks.{i}.resnets'''}
								assign_to_checkpoint(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  additional_replacements=[meta_path]		,  config=__lowerCAmelCase							)

				__lowerCamelCase    					=	[key for key in vae_state_dict if '''encoder.mid.block''' in key]
				__lowerCamelCase    					=	2
				for i in range(1		,  num_mid_res_blocks + 1							):
								__lowerCamelCase    					=	[key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]

								__lowerCamelCase    					=	renew_vae_resnet_paths(__lowerCAmelCase							)
								__lowerCamelCase    					=	{'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
								assign_to_checkpoint(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  additional_replacements=[meta_path]		,  config=__lowerCAmelCase							)

				__lowerCamelCase    					=	[key for key in vae_state_dict if '''encoder.mid.attn''' in key]
				__lowerCamelCase    					=	renew_vae_attention_paths(__lowerCAmelCase							)
				__lowerCamelCase    					=	{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
				assign_to_checkpoint(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  additional_replacements=[meta_path]		,  config=__lowerCAmelCase							)
				conv_attn_to_linear(__lowerCAmelCase							)

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	num_up_blocks - 1 - i
								__lowerCamelCase    					=	[
								    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    					=	vae_state_dict[
												    f'''decoder.up.{block_id}.upsample.conv.weight'''
												]
												__lowerCamelCase    					=	vae_state_dict[
												    f'''decoder.up.{block_id}.upsample.conv.bias'''
												]

								__lowerCamelCase    					=	renew_vae_resnet_paths(__lowerCAmelCase							)
								__lowerCamelCase    					=	{'''old''': f'''up.{block_id}.block''', '''new''': f'''up_blocks.{i}.resnets'''}
								assign_to_checkpoint(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  additional_replacements=[meta_path]		,  config=__lowerCAmelCase							)

				__lowerCamelCase    					=	[key for key in vae_state_dict if '''decoder.mid.block''' in key]
				__lowerCamelCase    					=	2
				for i in range(1		,  num_mid_res_blocks + 1							):
								__lowerCamelCase    					=	[key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

								__lowerCamelCase    					=	renew_vae_resnet_paths(__lowerCAmelCase							)
								__lowerCamelCase    					=	{'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
								assign_to_checkpoint(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  additional_replacements=[meta_path]		,  config=__lowerCAmelCase							)

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




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						str		,  )			->   List[str]:
				# Only support V1
				__lowerCamelCase    					=	requests.get(
				    ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''							)
				__lowerCamelCase    					=	io.BytesIO(r.content							)

				__lowerCamelCase    					=	OmegaConf.load(__lowerCAmelCase							)
				__lowerCamelCase    					=	512
				__lowerCamelCase    					=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
				if checkpoint_path.endswith('''safetensors'''							):
								from safetensors import safe_open

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

				# Convert the VAE model.
				__lowerCamelCase    					=	create_vae_diffusers_config(__lowerCAmelCase		,  image_size=__lowerCAmelCase							)
				__lowerCamelCase    					=	custom_convert_ldm_vae_checkpoint(__lowerCAmelCase		,  __lowerCAmelCase							)

				__lowerCamelCase    					=	AutoencoderKL(**__lowerCAmelCase							)
				vae.load_state_dict(__lowerCAmelCase							)
				vae.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    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.")

					SCREAMING_SNAKE_CASE__   :			int												=    parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



