






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 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       int       )		->    List[Any]:


				'''simple docstring'''




				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(lowercase__       )
				}

				# 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(lowercase__       )
				}

				for i in range(lowercase__       ):
								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(lowercase__       )
								lowerCAmelCase__      			=						{'''old''': f'down.{i}.block', '''new''': f'down_blocks.{i}.resnets'}
								assign_to_checkpoint(lowercase__     ,					lowercase__     ,					lowercase__     ,					additional_replacements=[meta_path]     ,					config=lowercase__       )

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

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

				for i in range(lowercase__       ):
								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(lowercase__       )
								lowerCAmelCase__      			=						{'''old''': f'up.{block_id}.block', '''new''': f'up_blocks.{i}.resnets'}
								assign_to_checkpoint(lowercase__     ,					lowercase__     ,					lowercase__     ,					additional_replacements=[meta_path]     ,					config=lowercase__       )

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

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





def 	lowerCAmelCase_		(lowercase__						:       Tuple     ,					lowercase__						:       Optional[Any]     ,					)		->    Tuple:


				'''simple docstring'''




				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(lowercase__       )
				lowerCAmelCase__      			=						5_12
				lowerCAmelCase__      			=						'''cuda''' if torch.cuda.is_available() else '''cpu'''
				if checkpoint_path.endswith('''safetensors'''       ):
								from safetensors import safe_open

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

				# Convert the VAE model.
				lowerCAmelCase__      			=						create_vae_diffusers_config(lowercase__     ,					image_size=lowercase__       )
				lowerCAmelCase__      			=						custom_convert_ldm_vae_checkpoint(lowercase__     ,					lowercase__       )

				lowerCAmelCase__      			=						AutoencoderKL(**lowercase__       )
				vae.load_state_dict(lowercase__       )
				vae.save_pretrained(lowercase__       )


if __name__ == "__main__":
			_UpperCAmelCase			:		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.")

			_UpperCAmelCase			:		Tuple   				=    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_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





