



"""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      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    List[Any]												=					checkpoint

				lowerCAmelCase__					:    Any												=					{}

				lowerCAmelCase__					:    Optional[Any]												=					vae_state_dict['''encoder.conv_in.weight''']
				lowerCAmelCase__					:    Dict												=					vae_state_dict['''encoder.conv_in.bias''']
				lowerCAmelCase__					:    Any												=					vae_state_dict['''encoder.conv_out.weight''']
				lowerCAmelCase__					:    Tuple												=					vae_state_dict['''encoder.conv_out.bias''']
				lowerCAmelCase__					:    List[Any]												=					vae_state_dict['''encoder.norm_out.weight''']
				lowerCAmelCase__					:    Optional[Any]												=					vae_state_dict['''encoder.norm_out.bias''']

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

				lowerCAmelCase__					:    str												=					vae_state_dict['''quant_conv.weight''']
				lowerCAmelCase__					:    List[str]												=					vae_state_dict['''quant_conv.bias''']
				lowerCAmelCase__					:    Optional[Any]												=					vae_state_dict['''post_quant_conv.weight''']
				lowerCAmelCase__					:    List[Any]												=					vae_state_dict['''post_quant_conv.bias''']

				# Retrieves the keys for the encoder down blocks only
				lowerCAmelCase__					:    Optional[Any]												=					len({'''.'''.join(layer.split('''.''')[:3]) for layer in vae_state_dict if '''encoder.down''' in layer})
				lowerCAmelCase__					:    Union[str, Any]												=					{
				    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__					:    str												=					len({'''.'''.join(layer.split('''.''')[:3]) for layer in vae_state_dict if '''decoder.up''' in layer})
				lowerCAmelCase__					:    int												=					{
				    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__					:    int												=					[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__					:    Any												=					vae_state_dict.pop(
												    f"""encoder.down.{i}.downsample.conv.weight""")
												lowerCAmelCase__					:    Union[str, Any]												=					vae_state_dict.pop(
												    f"""encoder.down.{i}.downsample.conv.bias""")

								lowerCAmelCase__					:    List[str]												=					renew_vae_resnet_paths(lowerCamelCase_)
								lowerCAmelCase__					:    Dict												=					{'''old''': f"""down.{i}.block""", '''new''': f"""down_blocks.{i}.resnets"""}
								assign_to_checkpoint(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,additional_replacements=[meta_path]	,config=lowerCamelCase_)

				lowerCAmelCase__					:    Optional[int]												=					[key for key in vae_state_dict if '''encoder.mid.block''' in key]
				lowerCAmelCase__					:    Optional[Any]												=					2
				for i in range(1	,num_mid_res_blocks + 1):
								lowerCAmelCase__					:    Union[str, Any]												=					[key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

								lowerCAmelCase__					:    Optional[int]												=					renew_vae_resnet_paths(lowerCamelCase_)
								lowerCAmelCase__					:    int												=					{'''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__					:    List[Any]												=					[key for key in vae_state_dict if '''encoder.mid.attn''' in key]
				lowerCAmelCase__					:    Dict												=					renew_vae_attention_paths(lowerCamelCase_)
				lowerCAmelCase__					:    Any												=					{'''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__					:    int												=					num_up_blocks - 1 - i
								lowerCAmelCase__					:    int												=					[
								    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__					:    Tuple												=					vae_state_dict[
												    f"""decoder.up.{block_id}.upsample.conv.weight"""
												]
												lowerCAmelCase__					:    Union[str, Any]												=					vae_state_dict[
												    f"""decoder.up.{block_id}.upsample.conv.bias"""
												]

								lowerCAmelCase__					:    Optional[int]												=					renew_vae_resnet_paths(lowerCamelCase_)
								lowerCAmelCase__					:    Tuple												=					{'''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__					:    Union[str, Any]												=					[key for key in vae_state_dict if '''decoder.mid.block''' in key]
				lowerCAmelCase__					:    Tuple												=					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__					:    Optional[Any]												=					renew_vae_resnet_paths(lowerCamelCase_)
								lowerCAmelCase__					:    List[Any]												=					{'''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__					:    Optional[Any]												=					[key for key in vae_state_dict if '''decoder.mid.attn''' in key]
				lowerCAmelCase__					:    List[Any]												=					renew_vae_attention_paths(lowerCamelCase_)
				lowerCAmelCase__					:    Any												=					{'''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      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		str	,):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					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(lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					512
				lowerCAmelCase__					:    str												=					'''cuda''' if torch.cuda.is_available() else '''cpu'''
				if checkpoint_path.endswith('''safetensors'''):
								from safetensors import safe_open

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

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

				lowerCAmelCase__					:    Union[str, Any]												=					AutoencoderKL(**lowerCamelCase_)
				vae.load_state_dict(lowerCamelCase_)
				vae.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case    :		Optional[int]  =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.')

							__snake_case    :		List[str]  =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


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

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

